.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Rajah 3: Banner komitmen Kumpulan Sawit Kinabalu dengan Program ADi6DZLW.LQDEDOX*URXS6GQ%KGGDQ3URJUDP$'LSawit Kinabalu Group Sdn. Bhd melalui penglibatannya dalam Program Akademi Dalam Industri (ADI), bukan sahaja komited dalam pembangunan kompetensi tenaga kerja sedia ada, malah turut mengambil langkah menyeluruh dalam memperkukuh aspek sekuriti organisasi melalui kerjasama strategik dengan institusi latihan dan agensi berkaitan. Program ini telah GLODQFDUNDQVHFDUDUDVPLSDGD0HL, bertempat di 3XVDW/DWLKDQ6DZLW.LQDEDOX:LOD\\DK7DZDX. Majlis pelaksanaan turut disertai oleh wakil pengurusan syarikat, institusi latihan bertauliah, serta calon peserta program.Pelaksanaan rasmi Program Akademi Dalam Industri (ADI) Siri Pertama ñ Batch 1/2025 oleh Kumpulan Sawit Kinabalu telah bermula pada 5 Mei 2025, bertempat di Pusat Latihan Sawit Kinabalu, Wilayah Tawau. Inisiatif ini merupakan sebahagian daripada strategi syarikat untuk memperkukuh kompetensi dan profesionalisme dalam bidang sekuriti melalui latihan berstruktur dan pensijilan formal. Program latihan Sijil Kemahiran Malaysia (SKM) Tahap 2 dalam bidang Kawalan Sekuriti (Security Services) adalah satu inisiatif pembangunan modal insan bagi melahirkan tenaga kerja mahir yang memenuhi standard industri keselamatan negara. Program ini dirangka khusus untuk memberi peluang peningkatan kemahiran kepada kakitangan muda yang baru memulakan kerjaya dalam bidang sekuriti.Siri Pertama (Batch 1/2025) telah dimulakan dengan penglibatan seramai lapan (8) orang peserta yang terdiri daripada calon pelatih, pengajar utama dan pembimbing industri. Program ini menyasarkan penyertaan daripada NDNLWDQJDQEHUXPXUWDKXQNHEDZDK dengan pengalaman kerja NXUDQJGDULSDGDGXD WDKXQdalam sektor keselamatan. 92
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Rajah 4: Barisan peserta program ADi Security Batch 1/2025 13Rajah 5: Patch Bakat ADi security batch 1/2025 .20326,6,%$.$7Secara keseluruhan, program ini melibatkan seramai ODSDQ fl RUDQJSHVHUWD, dengan pecahanberikut: 93
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7&DORQ3HVHUWD/DWLKDQ· Seramai OLPD fl RUDQJEDNDWmengikuti latihan untuk 6LMLO.HPDKLUDQ0DOD\\VLD 6.0 7DKDSdalam bidang Kawalan Sekuriti.· Calon-calon ini adalah kakitangan operasi yang memenuhi syarat umur dan pengalamankerja yang ditetapkan.3HFDKDQ/RNDVL%DNDW$'L%DWFKffl/RNDVL %LODQJDQ3HVHUWDTawau 3 orangLahad Datu 2 orangSandakan 2 orangIbu Pejabat 1 orang-XPODK flRUDQJJadual 5: Demografi Bakat ADi Batch 1/2025Nota: Penempatan calon mengikut lokasi ini membolehkan pendekatan latihan secara desentralisasi sambil mengekalkan standard yang seragam di seluruh cawangan. 3HQJDMDU8WDPD· 6HRUDQJ SHQJDMDUXWDPDyang bertauliah dan berkelayakan dalam bidang latihansekuriti industri.· Bertanggungjawab terhadap penyampaian kandungan modul latihan, pemantauan prestasipeserta, serta pematuhan kepada garis panduan SKM.3HPELPELQJ,QGXVWUL· 7LJD RUDQJSHPELPELQJLQGXVWUL, masing-masing berpengalaman dalampelaksanaan operasi sekuriti di peringkat lapangan.· Membimbing peserta dari aspek praktikal dan amalan terbaik di lokasi penempatansebenar.Komposisi peserta bagi Siri Pertama (Batch 1/2025) mencerminkan komitmen untuk menyediakan peluang pembangunan kerjaya kepada golongan muda dalam sektor keselamatan. Melalui gabungan latihan teknikal dan bimbingan lapangan, program ini diharapkan dapat melahirkan pengawal keselamatan yang cekap, berintegriti dan memenuhi kehendak industri. 94
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Program ini turut menggalakkan pembangunan berterusan (continuous professional development) dalam kalangan kakitangan muda yang baru menceburi bidang ini, selari dengan usaha memperkukuhkan keselamatan komuniti dan negara. &LUL&LUL3URJUDP6.07DKDS± %LGDQJ6HNXULWL· Program ini dilaksanakan selama satu (1) tahun, menggunakan pendekatan:· Latihan berasaskan tempat kerja (On-the-Job Training ñ OJT)· Modul yang berteraskan National Occupational Skills Standard (NOSS)· Penilaian kompetensi secara berkala, dijalankan oleh pegawai penilai dari institusilatihan yang diiktiraf oleh Jabatan Pembangunan Kemahiran (JPK)Rajah 6: Contoh Portfolio Bakat ADi Batch 1/202595
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7.DQGXQJDQ1266PHUDQJNXPL&RPSHWHQF\\8QLW\\DQJXWDPDfflRajah 7: Competency Profile Chart Security Service6HQDUDL.RPSHWHQVL7HUDV &RUH&RPSHWHQF\\ .RG.RPSHWHQVL 7DMXN.RPSHWHQVLDS-010-2:2013-C01 Premise Access/Exit ControlDS-010-2:2013-C02 Security PatrollingDS-010-2:2013-C03 Workplace Security & Safety ControlDS-010-2:2013-C04 Security Risk Situation ControlDS-010-2:2013-C05 Unarmed Escort (Bodyguard)Jadual 6: Core Competency+XUDLDQ.RPSHWHQVL5LQJNDVffl· 3UHPLVH$FFHVV([LW&RQWUROfflKawalan keluar masuk premis termasuk pemeriksaanindividu, barangan dan kenderaan bagi memastikan keselamatan kawasan.· 6HFXULW\\3DWUROOLQJ: Rondaan berkala bagi mengenal pasti ancaman, aktivitimencurigakan dan memastikan keselamatan fizikal sesuatu kawasan.96
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7· :RUNSODFH6HFXULW\\ 6DIHW\\&RQWURO: Pengawasan keselamatan di tempat kerjatermasuk pematuhan terhadap prosedur keselamatan dan tindakan semasa kecemasan.· 6HFXULW\\5LVN6LWXDWLRQ&RQWURO: Tindakan kawalan terhadap situasi berisikokeselamatan termasuk penggunaan SOP semasa insiden berlaku.· 8QDUPHG(VFRUW %RG\\JXDUG : Tugas pengiring dan perlindungan VIP tanpamenggunakan senjata, dengan penekanan terhadap penilaian risiko dan keupayaan reaksipantas.0DWODPDW3URJUDP· Melahirkan pengawal sekuriti profesional dan bertauliah melalui laluan pensijilan SKM· Mempertingkat kecekapan operasi sekuriti di ladang, kilang dan aset syarikat· Menyokong pematuhan terhadap piawaian pensijilan industri seperti MSPO dan RSPO· Menyediakan asas kukuh untuk pengembangan kerjaya dalam bidang sekuriti industriDengan penyertaan tenaga pengajar dan pembimbing yang kompeten, Program ADI Siri Pertama ini dijalankan secara sistematik, menyeluruh dan berkualiti tinggi. Ia menandakan satu pencapaian penting dalam usaha Kumpulan Sawit Kinabalu membangunkan ekosistem sekuriti yang mampan, berintegriti dan selaras dengan keperluan semasa industri.Sawit Kinabalu juga mempunyai:· Pusat Latihan Dalaman Wilayah yang berfungsi sebagai lokasi rasmi pelaksanaan latihanADI yang berdaftar Jabatan Pembangunan Kemahiran Malaysia khususnya dalam bidangsekuriti dan sekuriti operasi.· Pelan pembangunan tenaga kerja berasaskan kemahiran (skills-based workforcedevelopment plan) yang merangkumi perancangan jangka panjang untuk melahirkanpengawal sekuriti bertauliah melalui laluan Sijil Kemahiran Malaysia (SKM).· Tenaga pengajar dan pembimbing industri bertauliah yang dilantik bagi membimbingpeserta program secara teori dan praktikal mengikut standard JPK (Jabatan PembangunanKemahiran).· Hubungan berterusan dengan agensi latihan luar, termasuk JPK dan MPC, dan penyedialatihan swasta yang diiktiraf untuk memperkukuh lagi pelaksanaan latihan teknikal danvokasional.Langkah ini membuktikan komitmen Sawit Kinabalu untuk terus memainkan peranan penting dalam membentuk tenaga kerja sekuriti yang bukan sahaja cekap, malah diiktiraf secara profesional di peringkat nasional. 97
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Rajah 8: Pendaftaran Bakat ADi dalam sistem ADi%DKDQGDQ.DHGDKKajian ini menggunakan pendekatan JDEXQJDQNXDOLWDWLIGDQNXDQWLWDWLI(kaedah FDPSXUDQ)bagi mendapatkan data yang menyeluruh dan mendalam mengenai pelaksanaan serta keberkesanan 3URJUDP$NDGHPL 'DODP ,QGXVWUL $', dalam bidang sekuriti di KumpulanSawit Kinabalu. Pendekatan ini dipilih bagi PHPDGDQNDQ DQDOLVLV GDWD EHUDQJND NXDQWLWDWLI berkaitanpencapaian peserta dan kecekapan latihan, bersama SHPDKDPDQEHUEHQWXNQDUDWLI NXDOLWDWLI yang menggambarkan pengalaman, persepsi dan pemerhatian pihak terlibat secara langsung. .DHGDK3HQJXPSXODQ'DWD3HPHUKDWLDQ%HUVWUXNWXUPemerhatian dibuat secara langsung terhadap aktiviti latihan, penilaian kompetensi, dan pelaksanaan on-the-job training (OJT) di lokasi program. Elemen yang diperhatikan termasuk kehadiran peserta, penyampaian modul, pematuhan SOP, dan penglibatan pembimbing.· 7HPX%XDO6HSDUXK%HUVWUXNWXU- Temu bual dijalankan dengan:o Peserta program (calon SKM Tahap 2)o Pengajar dan pembimbing industrio Pegawai sumber manusia/pengurusan latihanTujuan temu bual adalah untuk mendapatkan maklum balas mengenai keberkesanan latihan, cabaran pelaksanaan, dan tahap penerimaan peserta terhadap kaedah pembelajaran. 98
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7· 6RDO6HOLGLN ,QVWUXPHQ.XDQWLWDWLI Borang soal selidik digunakan untuk menilai:o Tahap pengetahuan dan kemahiran sebelum dan selepas latihano Kepuasan peserta terhadap programo Persepsi terhadap struktur dan pengisian modul· 'RNXPHQ6RNRQJDQGDQ$QDOLVLV5HNRGo Data kehadiran, log latihan, laporan penilaian kompetensi, dan dokumen rasmiberkaitan program ADI digunakan sebagai rujukan sokongan untuk triangulasi data.Kaedah Analisis Data· 'DWDNXDOLWDWLIdianalisis menggunakan pendekatan WHPDWLN, bagi mengenal pasti pola,isu utama dan cadangan penambahbaikan daripada temu bual dan pemerhatian.· 'DWDNXDQWLWDWLIdianalisis secara deskriptif menggunakan statistik ringkas (min,peratusan) untuk melihat trend prestasi dan maklum balas peserta.Populasi dan Sampel · 3RSXODVLNDMLDQfflPeserta dan tenaga pelaksana Program ADI Siri Pertama ñ Batch1/2025· 6DPSHOXWDPDffl8 x Bakat ADi Tahap 2,.HSXWXVDQGDQ3HUELQFDQJDQLaporan analisis sampel soal selidik program adi sekuriti batch 1/2025 untuk pembentangan konvensyen TVET Madani PENGENALAN Program Akademi Dalam Industri (ADI) Sekuriti yang dilaksanakan oleh Kumpulan Sawit Kinabalu merupakan inisiatif pemerkasaan kompetensi anggota keselamatan melalui kerangka Sijil Kemahiran Malaysia (SKM) Tahap 2. Bagi menilai keberkesanan pelaksanaan program, satu soal selidik telah dijalankan dengan pengambilan sampel rawak daripada populasi seramai 477 anggota keselamatan di Kumpulan Sawit Kinabalu. 99
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Rajah 9: Skor likert Soal Selidik Bakat ADi METODOLOGI Sebanyak 8 responden telah dipilih secara rawak. Setiap responden menjawab 10 item berdasarkan skala Likert 1 hingga 5 bagi menilai aspek keberkesanan latihan, pengajar, fasiliti, dan aplikasi pengetahuan. ANALISIS PURATA & SISIHAN PIAWAI Berikut adalah purata dan sisihan piawai bagi setiap item soal selidik: %LO ,WHP3HQLODLDQ 3XUDWD6NRU6LVLKDQ3LDZDL1 Kandungan modul jelas 3.00 1.202 Latihan praktikal (OJT) membantu 3.88 1.363 Pengajar mempunyai pengetahuan tinggi 4.25 0.714 Pembimbing industri menyokong 2.50 1.605 Fasiliti latihan mencukupi 3.13 1.466 Penyampaian pengajar menarik 3.38 1.197 Latihan tingkatkan keyakinan 3.63 0.928 Penilaian kompetensi adil 4.00 1.209 Ilmu diaplikasi dalam tugas 3.38 1.3010 Program wajar diteruskan 4.13 1.13100
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7KATEGORI KEPUASAN RESPONDEN Responden dikategorikan berdasarkan skor purata individu seperti berikut: · Kategori Tinggi (Skor ! 4.0): 2 orang· Kategori Sederhana (Skor 3.0 - 3.9): 4 orang· Kategori Rendah (Skor < 3.0): 2 orangDAPATAN UTAMA · Item \"Pengajar mempunyai pengetahuan yang tinggi\" memperoleh skor tertinggi (Purata:4.25), mencerminkan kekuatan modul tenaga pengajar.· Item \"Pembimbing industri menyokong sepanjang latihan\" memperoleh skor terendah(Purata: 2.50), menandakan keperluan untuk penambahbaikan peranan mentor lapangan.· Item \"Program ini wajar diteruskan\" mencatatkan skor purata tinggi (Purata: 4.13), yangmenunjukkan sokongan kuat peserta terhadap kesinambungan program.· Kebanyakan responden menunjukkan tahap kepuasan sederhana ke tinggi, yangmenggambarkan bahawa program ini telah memberikan kesan positif secara menyeluruh.ANALISA KESELURUHAN Berdasarkan analisis data soal selidik, didapati bahawa Program ADI Sekuriti Batch 1/2025 sangatsesuai untuk diteruskan kepada siri seterusnya di Kumpulan Sawit Kinabalu. Skor purata yang konsisten tinggi terhadap aspek pengajaran, penilaian dan latihan praktikal menunjukkan keberkesanan struktur latihan yang ditawarkan. Tambahan pula, sokongan terhadap penerusan program mencerminkan nilai dan impak program ini kepada peserta dalam kerjaya keselamatan mereka. Namun begitu, terdapat beberapa aspek yang perlu diberi perhatian untuk penambahbaikanseperti:· Peranan pembimbing industri perlu diperkukuh melalui latihan dan pemantauan berkala.· Fasiliti latihan di lokasi-lokasi terpencil wajar dipertingkatkan dari segi keselesaan danperalatan.· Elemen bimbingan berterusan selepas latihan perlu diwujudkan bagi memastikanpemindahan ilmu yang berkesan ke dalam tugasan harian.CADANGAN PENAMBAHBAIKAN · Menyediakan latihan khas kepada pembimbing industri untuk meningkatkan kemahirankomunikasi dan penyeliaan.· Naik taraf fasiliti latihan di lokasi tertentu yang menunjukkan maklum balas kurangmemuaskan.· Menyediakan sistem bimbingan reflektif untuk menyokong aplikasi ilmu selepas latihan(post-training support).101
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7PENUTUP Dapatan ini mencerminkan pandangan wakil kecil peserta yang realistik dan berguna dalam memperkukuh struktur dan pelaksanaan Program ADI Sekuriti. Laporan ini diangkat sebagai input rasmi bagi Konvensyen TVET Madani yang berlangsung pada 11ñ13 Jun 2025. Program ini wajar dijadikan antara inisiatif tetap dalam pembangunan modal insan sektor keselamatan industri di Kumpulan Sawit Kinabalu. .HVLPSXODQPengurusan sekuriti dalam industri sawit Sabah memerlukan pendekatan baharu yang menyeluruh dan mampan. Pelaksanaan teknologi pemantauan, pemerkasaan sumber manusia melalui latihan formal, dan sistem pengurusan digital adalah antara langkah utama yang akan meningkatkan ketelusan, keberkesanan dan daya tindak pasukan sekuriti. Sekuriti bukan hanya tentang mencegah kejadian tetapi juga seperti yang dinyatakan oleh surat edaran PIKM bertarikh 22 November 20198, membina keyakinan terhadap profesionalisme organisasi. Pematuhan terhadap piawaian antarabangsa seperti 5632dan 0632turut memainkan perananpenting dalam memperkukuh keyakinan pemegang taruh serta pelanggan antarabangsa terhadap tahap profesionalisme dan keselamatan operasi sesebuah organisasi. Kepentingan pemerkasaan kompetensi keselamatan turut ditegaskan oleh Persatuan Industri Keselamatan Malaysia (2019) yang menyarankan agar pendekatan latihan dan pensijilan kemahiran melalui TVET dijadikan strategi pembangunan tenaga kerja berkemahiran tinggi dalam sektor keselamatan. Oleh itu, pendekatan keselamatan harus diarusperdanakan sebagai sebahagian daripada strategi korporat jangka panjang dan bukan sekadar dianggap sebagai fungsi sokongan. 3HQJKDUJDDQPenulis merakamkan setinggi-tinggi penghargaan kepada Jabatan Keselamatan Kumpulan Sawit Kinabalu atas bimbingan dan komitmen yang telah diberikan sepanjang pelaksanaan kajian dan program ini. Setulus penghargaan juga ditujukan kepada para pengurus ladang dan kilang, serta semua anggota sekuriti Kumpulan Sawit Kinabalu di semua lokasi operasi ó atas kerjasama, maklum balas dan penyertaan aktif yang amat bermakna. Ucapan terima kasih khas turut disampaikan kepada Unit Latihan dan Pembangunan Sumber Manusia, yang telah memberi sokongan penuh terhadap usaha memperkasa kompetensi dan profesionalisme anggota sekuriti organisasi. BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBflPersatuan Industri Keselamatan Malaysia. (2019, Oktober 11). Surat Edaran PIKM ke-20/2019: Pelaksanaan usahameningkatkan kompetensi di kalangan warga industri keselamatan melalui kaedah TVET.https://www.pikm.my/announcements/surat-edaran-pikm-ke-20-2019-pelaksanaan-usaha-meningkatkan-kompetensi-di-kalangan-warga-industri-keselamatan-melalui-kaedah-tvet/102
.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7Komitmen dan semangat yang ditunjukkan oleh semua pihak telah menyumbang kepada kejayaan program ini, selaras dengan aspirasi syarikat untuk membina pasukan keselamatan yang berkemahiran, berintegriti dan bersedia menghadapi cabaran industri masa kini. 5XMXNDQDepartment of Environment Malaysia. (2022). Garis panduan kilang minyak kelapa sawit mentah.https://www.doe.gov.my/wp-content/uploads/2022/12/8.-GARIS-PANDUAN-KELAPA-SAWIT.pdfDepartment of Occupational Safety and Health (DOSH) Malaysia. (2021). Keselamatan dan kesihatan pekerjaan di sektor pertanian dan penternakan.https://intranet.dosh.gov.my/index.php/ms/penerbitan/brochure-pamphlet/agriculture-forestry fishing-1/7-keselamatan-dan-kesihatan-pekerjaan-di-sektor-pertanian-dan-penternakan-malay version-onlyInternational Labour Organization. (2001). Guidelines on occupational safety and health management systems (ILO-OSH 2001). Geneva: International Labour Office. https://www.ilo.orgJabatan Keselamatan dan Kesihatan Pekerjaan Malaysia. (2020). Garis panduan pengurusan risiko keselamatan dan kesihatan pekerjaan. Putrajaya: Kementerian Sumber Manusia Malaysia. Krueger, G. E. (2003). Security vs. safety: The difference in risk management. In J. F. Donahue & D. E. Crowley (Eds.), The handbook of security management (2nd ed., pp. 45ñ58). CRC Press. Mohd Azman, H. (2020). Risk assessment on palm oil industry jobs using HIRARC method [Bachelorís thesis, Universiti Tun Hussein Onn Malaysia]. Scribd. https://www.scribd.com/document/248271227/Risk-Assessment-On-Palm-Oil-Industry-JobsUsing-HIRARC-Method-pdfOccupational Safety and Health Administration. (n.d.). Safety and security: What's the difference? U.S. Department of Labor. Retrieved June 8, 2025, from https://www.osha.govPersatuan Industri Keselamatan Malaysia. (2019, October 11). Surat Edaran PIKM ke-20/2019:Pelaksanaan usaha meningkatkan kompetensi di kalangan warga industri keselamatan melalui kaedah TVET.https://www.pikm.my/announcements/surat-edaran-pikm-ke-20-2019-pelaksanaan-usahamen ingkatkan-kompetensi-di-kalangan-warga-industri-keselamatan-melalui-kaedah-tvet/103
Industri Elektrik dan Elektronik
This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License, permitting copy and redistribution of the material and adaptation for commercial and uncommercial use. 7KH'HYHORSPHQWRI('83/&7UDLQLQJ.LWIRU,QGXVWULDO$XWRPDWLRQZ. Zakaria1*, H. Jamal21Advanced Technology Training Centre (ADTEC) JTM, Tangkak Campus, 84900 Tangkak, Johor, Malaysia*corresponding authorís email: [email protected]$EVWUDFW ñ The high cost and complexity of industrial training equipment often hinder effectivetechnical education, especially in resource-constrained institutions. This paper introduces the Development of EDU PLC Training Kit for Industrial Automation, a cost-effective and portable learning kit designed to provide hands-on training in industrial automation. The system integrates a conveyor mechanism with multiple automation components, including sensors, switches, Raspberry Pi, and a Programmable Logic Controller (PLC). Unlike traditional PLC trainer kits, this solution supports four user and can be accessed through the internet, enhancing flexibility and collaboration. The kit is compatible with various curriculum modules such as PLC programming, mechanical systems, electrical systems, and computer programming (C++, Python, Flow Programming). Its modular design allows easy maintenance and efficient storage, making it ideal for vocational institutes and training centers. The effectiveness of the system was validated through use in mechatronics training sessions and customized industrial courses. The results and positive feedback from students and industry personnel highlight the potential of the EDU Multipurpose Conveyor as a scalable educational tool that supports the goals of Industry 4.0 and Sustainable Development.Keywords: Automation; educational kit; industry 4.0; multipurpose conveyor; PLC training; Raspberry Pi.Article HistoryReceived December 2017Received in revised form January 2018Accepted March 2018, ,QWURGXFWLRQIn the realm of technical and vocational education, industrial automation plays a central role in preparing students to meet the demands of modern manufacturing systems. However, the high cost, complexity, and maintenance challenges of conventional industrial training equipment, such as commercial PLC trainers and pneumatic systems, often limit access for many institutionsóespecially those operating under budget constraints. Existing training tools not only require significant financial investment but also rely on proprietary components, which are expensive and difficult to replace due to vendor lock-in or import requirements. This situation poses risks of equipment downtime, disrupted training sessions, and limited student hands-on experience.To address these barriers, the Development of EDU PLC Training Kit for Industrial Automation was developed at Institut Latihan Perindustrian (ILP) Tangkak as a modular, cost-effective, and portable training kit tailored for industrial automation education. The system integrates real-world components including Programmable Logic Controllers (PLCs), a Raspberry Pi, input/output sensors, relays, control switches, and a conveyor mechanism mounted on an aluminum profile frame. It supports multi-user functionality (up to four users) and allows remote access through Wi-Fi networking and smartphone control via the Node-RED platform, aligning the system with Industry 4.0 principles.This paper outlines the development, implementation, and evaluation of the EDU kit, which enables students to conduct hands-on experiments across a wide range of technical domains, including PLC programming, mechanical and electrical systems, flow programming (e.g., Node-RED), and languages such as C++ and Python. More than just a learning tool, the EDU Multipurpose Conveyor represents a scalable, future-ready solution to modernize industrial training and support sustainable educational development..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7105
,, 6\\VWHP'HVLJQDQG)HDWXUHVThe Development of EDU PLC Training Kit for Industrial Automation is composed of two main components: a PLC-based control kit and a Raspberry Pie.The control panel integrates programmable logic controllers (PLC), a Raspberry Pi 4, relay modules, indicator lights, and a Wi-Fi router. The setup is mounted on an aluminum profile frame, making it compact, portable, and easy to assemble.The system allows the connection of up to four different PLC. It features smart preventive maintenance capabilities through the use of easily available and cost-efficient components. The system supports wireless communication via Node-RED and is controllable through smartphones, aligning with IR 4.0 standards. The hardware design of the training kit, as presented in Section A, is depicted in Fig. 1. The layout includes essential components required for the operational demonstration of the EDU Multipurpose Conveyor system.Fig. 1. Physical layout of the EDU Multipurpose Conveyor training kit hardware design,,, 0HWKRGRORJ\\The methodology adopted for the development and implementation of the Development of EDU PLC Training Kit for Industrial Automation integrates both hardware and software components to simulate, monitor, and control an automation system in an educational setting. The approach ensures that students gain hands-on experience with industry-relevant technologies in a controlled and interactive learning environment.A. Control System Design and IntegrationThe process begins with the configuration and programming of the programmable logic controller (PLC). A Siemens S7-1200 PLC is used in conjunction with the Totally Integrated Automation (TIA) Portal software. The control logic is developed using industrial-standard programming languages such as Ladder Diagram (LD), Function Block Diagram (FBD), and Sequential Function Chart (SFC), ensuring that the implementation aligns with current industrial practices.Following this, the conveyor system is physically assembled and connected to the PLC. All input and output terminals are wired using jumper cables, allowing sensors and actuators to be properly integrated with the PLC. This step ensures the correct mapping of all hardware components for effective signal transmission and logic execution.To enhance system functionality, the PLC is interfaced with a Raspberry Pi 4 microcontroller using widely adopted industrial communication protocols such as Modbus TCP/IP, Profinet, and OPC UA. This connection enables reliable, real-time communication between devices and supports seamless data transfer required for advanced automation.In order to facilitate remote monitoring and control, Node-RED is deployed on the Raspberry Pi to create a browser-based IoT dashboard. This dashboard allows users to interact with the system wirelessly using smartphones or other mobile devices. The user interface provides real-time feedback on system status and supports direct manipulation of key variables.The development of the control system began with the design and simulation of the automation logic using ladder diagram programming in the TIA Portal environment. The simulation phase allowed for the identification and rectification of logical errors prior to hardware deployment. Following successful simulation, the validated control logic is deployed to the Programmable Logic Controller (PLC). Functional testing is then conducted to verify the correctness of control sequences, evaluate system responsiveness, and ensure robust hardware-software integration. The overall control architecture is illustrated in Fig. 2. Block diagram of the control system..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7106
Fig. 2. Block diagram of the control systemB. Cost AnalysisTABLE IPROJECT COMPONENT COSTSComponent Cost (RM)Banana Jack 376.00PLC Siemens S7 1200 7,200.00Raspberry Pi 4 355.00TP-Link ADSL Router 110.00Other components & Assembly 5,743.00Total 13,784.00Table I summarizes the components utilized in the project, with the total development cost amounting to RM13,784. The components include 170 units of Banana Jack costing RM376.00, 4 units of Siemens S7-1200 PLCs priced at RM7,200.00, one Raspberry Pi 4 (8GB) valued at RM355.00, and one TP-Link ADSL Router costing RM110.00. Additionally, other components and assembly expenses account for RM5,743.00. These costs collectively represent the total expenditure for the development of the project.C. Cost Comparison with Existing EquipmentTo evaluate the cost-efficiency of the proposed solution, a comparison was made between the market price of existing conventional training equipment and the cost of deploying the Development of EDU PLC Training Kit for Industrial Automation in a typical technical training setup. Table 2 summarizes the market cost of selected standard training tools.TABLE IICOST OF CONVENTIONAL TRAINING EQUIPMENT1R (TXLSPHQW'HVFULSWLRQ (VWLPDWHG&RVW 0<5 1 PLC Training Set (Brand X) RM26,988.002 Laboratory Cabinet and Bench RM64,468.00The PLC Training Set (Brand X) represents a dedicated solution for programmable logic controller instruction, often limited to single-system simulation without integration with other automation elements. Meanwhile, the Laboratory Cabinet and Bench serve as generalinfrastructure for conducting practical sessions but do not contribute directly to automation training outcomes.In contrast, the Development of EDU PLC Training Kit for Industrial Automation despite not being included in this table offers a consolidated platform that integrates elements from multiple modules such as electrical assembly, pneumatics, and PLC control within a single unit. This not only reduces the need for multiple dedicated training systems but also enhances modularity and flexibility in instructional delivery.When compared cumulatively, the acquisition of both a branded PLC training set and a laboratory bench setup could cost over RM91,000, whereas the proposed solution provides similar or enhanced functionality at a significantly lower cost. Additionally, the EDU Conveyor system supports a broader range of training modules as shown in Table II, thus improving return on investment and maximizing equipment utilization.The economic advantage becomes particularly relevant in institutional contexts where budget constraints limit equipment acquisition. By integrating multiple instructional elements into one multipurpose platform, the EDU Conveyor system ensures more efficient resource use, while still fulfilling the technical requirements specified in the MC-091-2/3:2016 standard.,9 5HVXOWVDQG'LVFXVVLRQThe Development of EDU PLC Training Kit for Industrial Automation was deployed across multiple technical training programs to evaluate its overall effectiveness as an instructional tool. Its implementation was assessed based on four key dimensions: technical performance, cost-effectiveness, student learning experience, and institutional benefits.A. Cost Comparison ResultsThe cost analysis of the training equipment revealed significant differences among the evaluated systems. The PLC Training Set (Brand X) is priced at RM26,988.00, representing the typical cost for a standalone programmable logic controller training package widely used in industrial automation education. The more comprehensive Laboratory Cabinet and Bench, which provides physical workspace and additional .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7107
instrumentation for practical sessions, carries a substantially higher cost of RM64,468.00. In contrast, the EDU PLC Training Kit for Industrial Automation was developed at a considerably lower cost of RM13,784.00. This price point is less than half the cost of the Brand X PLC Training Set and approximately onefifth the cost of the combined Laboratory Cabinet and Bench as shown in Table III. These results indicate that the EDU PLC Training Kit provides a more affordable alternative for institutions seeking to equip their training laboratories with modern automation technology, without compromising on functionality or relevance to industry standards. TABLE IIICOST COMPARISON OF PLC TRAINING EQUIPMENT(TXLSPHQW'HVFULSWLRQ &RVW 0<5 PLC Training Set (Brand X) RM26,988.00 Laboratory Cabinet and Bench RM64,468.00 EDU PLC Training Kit for Industrial Automation RM13,784.00 B. Savings AnalysisA comparative cost analysis was conducted to evaluate the financial implications of implementing the EDU PLCTraining Kit for Industrial Automation against conventional training equipment. The PLC Training Set(Brand X) is priced at RM26,988.00, while the Laboratory Cabinet and Bench, typically used to support industrial automation training environments, costs RM64,468.00.In contrast, the EDU PLC Training Kit was developed at a cost of only RM13,784.00. This results in a cost saving of approximately 49% compared to the PLC Training Set (Brand X), and an even greater saving of 79% compared to the Laboratory Cabinet and Bench as shown in Fig3.These substantial reductions in cost highlight the potential of the EDU kit as a highly affordable solution for technical and vocational education institutions.The savings not only reduce capital expenditure but also make it possible to scale the implementation across multiple training labs or institutions, especially in budgetconstrained environments.Fig. 3. Comparative cost analysisC. Implementation in Learning Modules (MC-091-2/3:2016)The EDU PLC Training Kit for Industrial Automation used in the learning module MC-091-2/3:2016 has demonstrated consistent effectiveness from July 2018 until now. Based on the usage data across various modules such as Electrical and Electronic Assembly, Basic PLC Programming, Electrical and Electronic System Assembly, Automation System Calibration, Automation System Maintenance, Advance PLC, and Computer Programming, a stable usage pattern can be observed despite fluctuations from year to year.The number of students using this training kit was high at the beginning of 2018, around 45 students per module. Although there was a decrease in usage in subsequent years, the usage gradually increased again in recent years, notably in 2023 and 2024. This indicates a sustained interest and need for the training kit to support practical learning for students enrolled in related courses.The continuous usage proves that the training kit is not only relevant but also adds significant value to the learning process, especially in the fields of automation, electronics, and programming. This success also shows that the training kit can support the implementation of a dynamic curriculum that meets the evolving demands of the industry.In conclusion, the usage data indicates that the training kit has successfully helped students understand theoretical concepts in a practical manner and improved the necessary technical skills, thereby contributing to the achievement of the learning objectives of the offered modules.Table IV shows the module-wise implementation of the training kit, highlighting the total number of students who were exposed to the system each semester..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7108
TABLE IVMODULE-WISE IMPLEMENTATION D. Educational ImpactThe traditional pneumatic trainers at ILP Tangkak were limited, with only four units available for 30 students, which caused delays and reduced the amount of practice time for each student. The introduction of the EDU kit significantly expanded the training capacity by allowing more students to work simultaneously and practice more frequently. Additionally, the EDU kit supports nine modules from the Sijil Kemahiran Malaysia (SKM) syllabus, making it a versatile tool that can be used for multi-disciplinary training across various fields.E. User Experience and FeedbackStudents reported an improved understanding of the subject matter thanks to the extended access to hands-on activities provided by the training kit. Instructors also found the kit easier to schedule and manage compared to the legacy systems previously used. Furthermore, industry partners such as ST Microelectronics and Micron Malaysia gave positive feedback during customized training courses, highlighting the kitís effectiveness in meeting industry needs.F. Institutional BenefitsThe use of the training kit has led to reduced downtime and decreased dependence on external maintenance providers. It also results in lower procurement costs for future expansion, making it a cost-effective solution. Additionally, the training kit aligns well with the national focus on Industry Revolution 4.0 (IR 4.0) and supports sustainable training frameworks.9 &RQFOXVLRQThe development of EDU PLC Training Kit for Industrial Automation represents a significant advancement in the delivery of practical industrial automation training within technical and vocational education. By combining cost-effective design, modular construction, and integration of key Industry 4.0 technologies, the EDU kit successfully addresses several limitations associated with conventional training systemsónamely high procurement costs, limited scalability, and complex maintenance requirements.The kit not only reduces training equipment costs by more than 50% but also enhances training flexibility through features such as multi-user support, Wi-Ficonnectivity, and smartphone-based control via the NodeRED platform. Its application spans multiple disciplines,supporting learning modules in PLC programming, electrical and mechanical systems, and modern computing languages such as Python and C++. The portability and ease of use also ensure that students receive more frequent, individualized hands-on training an essential factor in skill mastery and industry readiness.Successful deployment in real classroom environments, as well as its positive reception in industrial short coursesand training collaborations, underscores the kitís relevance and adaptability. Furthermore, feedback from instructors, students, and industry stakeholders confirms the EDU kitís potential as a scalable, future-oriented solution for enhancing mechatronics and automation education.As institutions increasingly aim to align with the demands of the Fourth Industrial Revolution (IR 4.0), innovations such as the EDU Multipurpose Conveyor offer a sustainable pathway forward ensuring that both teaching and learning remain responsive, accessible, and technologically current.9, $FNQRZOHGJHPHQWVThe authors acknowledge the support of Institut Latihan Perindustrian Tangkak and all staff involved in the Mechatronics Workshop.9,, &RQIOLFWRI,QWHUHVWThe authors declare no conflict of interest in the publication of this research.9,,, $XWKRU&RQWULEXWLRQVZ. Zakaria: Supervision, manuscript preparation,testing, data analysis and evaluation; H. Jamal: System design and hardware integration..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7109
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This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3. 6DIHW\\'RPHVWLF:LULQJ%RDUG 6':% Nabilah binti Mohamed1*, Rozmi bin Rofle1, Jasnihisam bin Ishak1,Nurul Amin bin Badrul1Kolej Teknologi Termaju (ADTEC) JTM Kampus Ipoh, 30020 Perak, Malaysia *emel: [email protected]$EVWUDN ñ Peningkatan penggunaan peranti elektrik di rumah telah menyebabkan keperluanuntuk memastikan keselamatan elektrik dan meningkatkan pemahaman tentang konsep berkaitan elektrik semakin penting. Justeru, latihan kemahiran di dalam bidang elektrik harus memastikan latihan adalah selamat, mudah, berkesan dan menjimatkan. Projek ini bertujuan untuk membangunkan alat latihan iaitu Papan Pendawaian Domestik Keselamatan (SDWB) yang meningkatkan keselamatan dan kemahiran pendawaian pengguna. Projek ini menggunakan pendekatan sistematik proses rekabentuk generik. Pembangunan projek ini menumpukan kepada lima aspek utama iaitu analisa keperluan, reka bentuk, pembangunan prototaip, pengujian, penambahbaikan. Hasil pelaksanaan SDWB di dalam metodologi pengajaran mendapati SDWB berupaya mengurangkan bahaya dan menggalakkan amalan selamat dalam persekitaran domestik. Ia juga mampu mensimulasikan litar dan senario elektrik kehidupan sebenar, menyediakan pengguna dengan platform yang selamat untuk belajar, mencuba dan menguji konfigurasi pendawaian domestik yang berbeza. SDWB didapati boleh digunakan oleh pengguna untuk mempelajari kemahiran asas pendawaian elektrik, melakukan pengujian atau pemasangan yang selamat bagi pelbagai peralatan elektrik biasa. Selain itu, dengan projek ini juga pengguna akan menjadi mahir sebelum berada di kawasan pendawaian sebenar dan menjimatkan kos bahan latihan. SDWB merupakan langkah penting ke hadapan dalam pendidikan keselamatan elektrik, menawarkan penyelesaian berskala dan menarik untuk meningkatkan kecekapan keselamatan elektrik di kalangan orang awam dan profesional. Penyelidikan dan pembangunan lanjut ditujukan ke arah memperluaskan keupayaan SDWB, termasuk penyepaduan teknologi rumah pintar dan sistem tenaga boleh diperbaharui, untuk kekal sejajar dengan piawaian elektrik dan keselamatan yang berkembang..DWD.XQFLfflAccepted March 2018 , 3HQJHQDODQSelaras dengan kempen kesedaran mengenai keselamatan elektrik kepada masyarakat, pemerkasaan latihan kemahiran di dalam bidang elektrik harus ditingkatkan bagi mencapai hasrat tersebut. Inisiatif ini seiring dengan matlamat Pendidikan dan LatihanTeknikal dan Vokasional (TVET) melalui pemanfaatankemahiran dan teknologi moden untuk meningkatkan keberkesanan pembelajaran, kualiti latihan dan kesediaan graduan dalam menghadapi cabaran industri yang sentiasa berubah.Peralatan latihan yang usang dan tidak lagi relevan menjadi cabaran besar yang membelenggu proses pembelajaran di institusi TVET. Dalam dunia yang bergerak pantas ke arah teknologi moden, pelatih dan tenaga pengajar memerlukan peralatan latihan yang lebih berdaya saing dan selari dengan keperluan industri. Menyedari keperluan mendesak ini, pasukan inovasi dari ADTEC JTM Kampus Ipoh tampil dengan satu penyelesaian praktikal dan menjimatkan, iaitu membangunkan alat bantu mengajar yang dikenali sebagai Safety Domestic Wiring Board (SDWB). Inovasi ini bukan sekadar papan latihan biasa tetapi merupakan satu pendekatan baharu yang lebih tersusun, selamat, dan mengintergrasikan teknologi dalam pembelajaran. SDWB direka khas untuk membantu pelatih meningkatkan kemahiran dalam bidang pendawaian elektrik dengan selamat, berkesan dan pantas sekali gus menyokong matlamat TVET Madani dalam melahirkan pekerja berkemahiran dengan berpendapatan tinggi. ,, 2EMHNWLIProjek ini bertujuan untuk meningkatkan kefahaman pelatih terhadap sistem pendawaian elektrik satu fasa melalui pendekatan latihan yang lebih praktikal, selamat dan interaktif. Dalam masa yang sama, penggunaan bahan guna habis seperti seperti kabel, trunking dan conduit perlu dijimatkan bagi mengurangkan pembaziran dan menjimatkan kos latihan. Selain itu, projek ini .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7111
mengaplikasikan prinsip problem-based learning (PBL) dan elemen Internet of Things (IoT) dalam latihan TVET yang bukan sahaja menjadikan pembelajaran lebih menarik dan relevan dengan keperluan industri semasa tetapi juga membentuk pelatih yang lebih kreatif, berfikiran kritis dan bersedia menghadapi cabaran dunia pekerjaan sebenar. ,,, .DMLDQ/LWHUDWXUPelaksanaan program TVET yang berkesan memerlukan sokongan persekitaran pembelajaran yang lengkap dan komprehensif. Kajian oleh [1] menegaskan bahawa bengkel latihan yang dilengkapi peralatan, mesin dan kemudahan mencukupi memainkan peranan penting dalam memastikan keberkesanan latihan kemahiran teknikal. Sokongan infrastruktur ini bukan sahaja memudahkan pelaksanaan kurikulum amali malah meningkatkan tahap kesediaan pelatih untuk memenuhi keperluan sebenar industri. Seiring dengan itu, [2] menyatakan bahawa program TVET dirangka khusus untuk meningkatkan kemahiran amali dalam bidang seperti pembinaan, reka bentuk dan penyelenggaraan sekali gus memperkukuh kebolehpasaran graduan. Namun begitu, beberapa cabaran besar turut dikenalpasti dalam ekosistem TVET. Kajian oleh [3], [4] melaporkan bahawa kekurangan kemudahan pembelajaran, peralatan latihan serta kegagalan menyesuaikan kurikulum mengikut kehendak industri telah menyumbang kepada penurunan kadar kemasukan ke institusi TVET termasuk kolej komuniti.Ini menunjukkan bahawa pelaksanaan program TVET yang tidak relevan atau tidak disokong dengan fasiliti yang sesuai boleh menjejaskan minat dan kepercayaan masyarakat terhadap keberkesanan latihan kemahiran. Selain itu menurut [5], faktor seperti ketidakstabilan dasar, penggunaan modul latihan yang lapuk dan penyemakan kurikulum yang tidak menyeluruh turut menjadi penyumbang kepada tahap kompetensi pelatih yang rendah dari segi pengetahuan, kemahiran teknikal dan motivasi. Perkara ini menunjukkan keperluan mendesak kepada satu bentuk pendekatan pengajaran yang lebih dinamik dan menyeluruh bagi meningkatkan tahap keberkesanan latihan di institusi TVET. Dalam konteks pedagogi, pendekatan Problem-Based Learning (PBL) dilihat sebagai salah satu kaedah yang mampu meningkatkan keberkesanan pembelajaran dalam bidang teknikal. Kajian oleh [6] dalam kursus kejuruteraan di Malaysia mampu meningkatkan penglibatan aktif pelajar serta pemahaman terhadap konsep teknikal. Ini disokong oleh dapatan [7] yang mendapati bahawa PBL memberi kesan positif terhadap pencapaian pelajar dalam aspek penyelesaian masalah dan pembangunan pemikiran kritikal, dua kemahiran teras dalam latihan TVET. Dari aspek pematuhan keselamatan dan peraturan, Suruhanjaya Tenaga (2024) melalui Garis Panduan Pendawaian Elektrik Pepasangan Domestik menyatakan bahawa semua kerja pendawaian perlu dilaksanakan oleh Kontraktor Elektrik yang berdaftar di bawah AktaBekalan Elektrik dan Peraturan-Peraturan Elektrik 1994. Justeru itu latihan berkaitan pendawaian domestik perlu dilaksanakan dalam suasana yang terkawal, selamat dan mematuhi piawaian yang ditetapkan termasuk dari segi penggunaan peralatan, pengujian litar dan latihan teknikal. Berdasarkan dapatan kajian dan rujukan yang dikemukakan, pembangunan SDWB sebagai alat bantu mengajar dalam latihan amali pendawaian elektrik adalah selari dengan keperluan semasa dalam bidang TVET. Inovasi ini bukan sahaja membantu pelatih memahami konsep pendawaian secara teori dan praktikal malah mampu mengurangkan risiko bahaya, pembaziran bahan guna habis (BGH) dan memperkukuh amalan selamat di persekitaran pembelajaran. SDWB juga menyokong pelaksanaan pendekatan PBL melalui simulasi senario pendawaian sebenar dalam keadaan yang lebih sistematik dan selamat. ,9 0HWRGRORJLProjek SDWB dibangunkan menggunakan pendekatan sistematik berasaskan proses rekabentuk generik, yang merupakan satu kaedah struktur dalam pembangunan produk atau sistem teknikal. Pendekatan ini melibatkan lima fasa utama iaitu analisa keperluan, reka bentuk, pembangunan prototaip, pengujian dan penambahbaikan. 1. Analisa keperluanFasa ini dimulakan dengan pemerhatian dan pengumpulan maklumat daripada tenaga pengajar, pelatih dan pelaksanaan latihan amali sedia ada. Antara isu yang dikenalpasti termasuklah kesukaran pelatih memahami teori pendawaian elektrik, peningkatan kadar kesilapan yang berulang semasa amali dan pembaziran bahan guna habis (BGH). Analisa ini memberi asas yang kukuh kepada keperluan membangunkan alat bantu mengajar yang lebih interaktif, selamat dan menjimatkan serta meningkatkan kefahaman pelatih bagi pendawaian fasa tunggal. Tinjauan terhadap pelatih dilakukan di bengkel teknologi elektrik ADTEC JTM Kampus Ipoh dan melibatan 109 pelatih semester 1 dan 2 tahun bagi sesi 2020/2021 melalui edaran borang soal selidik. Terdapat empat bahagian di dalam set borang soal selidik iaitu Bahagian A, B, C dan D. Bahagian A mengandungi soalan-soalan berkaitan demografi. Seterusnya Bahagian B mengandungi soalan-soalan berkaitan dengan kefahaman teori dan amali, Bahagian C mengandungi .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7112
soalan-soalan berkaitan dengan cabaran pendawaian amali manakala Bahagian D mengandungi soalan-soalan berkaitan cadangan penambahbaikan dan inovasi.Hasil kajian mendapati pelatih menghadapi masalah semasa latihan amali disebabkan oleh kesukaran dalam membaca pelan pendawaian, keliru semasa menyambung wayar, tempoh pembelajaran teori sedia ada tidak mencukupi, perasaan tidak selamat semasa menjalankan proses pengujian di wiring bay. Lima masalah utama di wiring bay mengikut keutamaan adalah (i) kesilapan semasa sambungan wayar, (ii) BGH yang terhad, (iii) tempoh latihan yang terhad, (iv) peralatan yang terhad dan (v) kurang memahami fungsi komponen.Berdasarkan hasil kajian, pelatih bersetuju dengan cadangan pembangunan projek inovasi yang mempunyai ciri-ciri interaktif, selamat untuk digunakan, menepati pendawaian sebenar pepasangan domestik, binaan yang mobiliti dan disertakan panduan penggunaan. Pelatih berpendapat penggunaan SDWB dijangka boleh membantu pelatih memahami sambungan litar dengan lebih baik, mengurangkan kesilapan dalam pendawaian dan mengoptimumkan pengunaan BGH.2. Reka bentukRekabentuk 1 Rekabentuk 2Rajah 1. Reka bentuk alat bantu mengajar sedia ada Proses reka bentuk awal dimulakan dengan lakaran manual. Reka bentuk teknikal kemudiannya dihasilkan menggunakan perisian CAD bagi mendapatkan visualisasi yang lebih tepat, termasuk menentukan kedudukan komponen dan dimensi yang sesuai. Penggunaan perisian ini juga membantu dalam merancang reka bentuk yang lebih efisien dan kemas.Rekabentuk 1 adalah alat bantu mengajar sedia ada yang sering digunakan di dalam latihan dan mewakili satu jenis litar pendawaian. Rekabentuk 2 pula ialah litar pendawaian yang merangkumi pendawaian satu rumah. Manakala rekabentuk 3 adalah litar pendawaian yang merangkumi pendawaian fasa tunggal lengkap untuk satu rumah dan dilengkapi sistem IoT. 3. Pembangunan PrototaipRekabentuk 3Rajah 2. Reka bentuk alat bantu mengajar SDWB Pemilihan bahan dan komponen penting dikenal pasti bagi memastikan fungsi dan keselamatan papan pendawaian. Komponen utama yang digunakan termasuk suis utama, RCD, MCB penunjuk LED, terminal block,dan casing tahan api yang diperbuat daripada bahan PVC atau ABS yang mempunyai sifat kalis api. Pemilihan komponen ini dibuat berdasarkan kriteria seperti pematuhan kepada piawaian keselamatan, tersedia di pasaran, dan mempunyai pensijilan keselamatan seperti SIRIM.Akhir sekali, proses pemasangan dan pembinaan prototaip dilaksanakan berdasarkan reka bentuk yang telah dirancang. Pemasangan dilakukan mengikut kaedah kerja yang selamat dan menepati standard pendawaian yang ditetapkan oleh Suruhanjaya Tenaga. Komponen disusun secara sistematik untuk memastikan kemudahan penyelenggaraan dan keselamatan pengguna. Ujian awal turut dijalankan bagi memastikan setiap fungsi dan ciri keselamatan beroperasi dengan baik sebelum sistem ini digunakan secara sebenar. 4. PengujianSetelah sesuatu kerja pendawaian siap dipasang, beberapa pengujian perlu dilakukan bagi memastikan sistem pendawaian berfungsi dengan baik serta selamat untuk digunakan. Pengujian ini penting bagi mengesahkan kendalian setiap litar dan memastikan semua peralatan serta komponen yang dipasang berada dalam keadaan yang mematuhi piawaian keselamatan. Namun sebelum sebarang ujian dijalankan, satu proses pemeriksaan visual dan teknikal hendaklah dilakukan terlebih dahulu bagi mengenal pasti sebarang kecacatan pemasangan atau sambungan yang tidak mengikut spesifikasi. Ujian-ujian tersebut merangkumi: Ujian Keterusan bagi memastikan sambungan wayar lengkap tanpa putus, Ujian Rintangan Penebatan untuk mengukur tahap .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7113
penebatan antara konduktor aktif dan bumi, serta Ujian Kekutuban bagi memastikan fasa dan neutral tidak tertukar. Seterusnya, Ujian Rintangan Elektrod Bumi dilakukan untuk menilai kecekapan sistem pembumian, diikuti dengan Ujian PAB (Pemutus Arus Baki) untuk menguji keberkesanan RCCB/ELCB, dan akhir sekali, Ujian Galangan Gelung Bumi (Earth Loop Impedance Test) bagi mengesahkan bahawa sistem akan bertindak balas dengan cukup cepat sekiranya berlaku litar pintas kepada bumi. 5. PenambahbaikanSusun atur komponen di dalam papan perlu disusun semula agar lebih ergonomik khususnya dari sudut kemudahan capaian, kebolehbacaan dan keselamatan pengguna semasa operasi atau penyelenggaraan. Kedudukan seperti MCB, RCD dan penunjuk LED haruslah mudah dilihat dan diakses tanpa mengganggu komponen lain. Penambahan ciri keselamatan tambahan seperti buzzeramaran. Buzzer ini akan memberi isyarat bunyi sekiranya berlaku kebocoran arus atau litar pintas sekaligus memberi makluman awal kepada pengguna untuk mengambil tindakan segera. Ini bukan sahaja meningkatkan keselamatan, malah dapat mengurangkan risiko kerosakan lebih besar sekiranya berlaku kecemasan. Akhir sekali, penyediaan buku panduan penggunaan SDWB juga dicadangkan sebagai satu langkah penting. Buku ini akan mengandungi maklumat lengkap mengenai fungsi setiap komponen, langkah pemasangan, prosedur pengujian serta panduan penyelenggaraan berkala. Ini bertujuan untuk membantu pengguna memahami dan mengendalikan papan SDWB dengan lebih berkesan serta memastikan ia berfungsi dengan selamat dan optimum sepanjang tempoh penggunaannya. 9 +DVLOGDQ3HUELQFDQJDQPelatih menunjukkan peningkatan kefahaman terhadap sistem pendawaian satu fasa dan kecekapan dalam pengendalian komponen elektrik. Latihan amali pendawaian yang dijalankan menggunakan SDWB memperlihatkan pengurangan kesilapan teknikal dan meningkatkan kesedaran keselamatan. Data pengujian menunjukkan bahawa sistem pemutus litar (MCB dan RCB) berfungsi dengan baik dalam senario simulasi kecemasan seperti litar pintas dan kebocoran arus.Penggunaan lampu penunjuk memberikan maklum balas visual yang efektif kepada pelatih. Keberkesanan sistem SDWB juga dibuktikan melalui pengurangan ketara dalam penggunaan bahan guna habis dengan penjimatan kos sebanyak 75%. SDWB dibangunkan khusus untuk menangani pelbagai masalah utama yang sering dihadapi dalam latihan kemahiran di institusi TVET. Dari segi keselamatan, SDWB direka dengan perlindungan tambahan dan sistem penebat yang dapat mengelakkan risiko kejutan elektrik. SDWB juga berperanan sebagai pengganti yang lebih moden dan relevan kepada peralatan latihan usang yang sedia ada. SDWB memastikan keseragaman dalam silibus latihan menjadikan setiap pelatih mendapat pendedahan yang standard, selamat dan sistematik. SDWB merupakan projek inovasi asli yang dibangunkan berdasarkan keperluan sebenar institusi latihan. Berbeza dengan kit latihan sedia ada yang bersifat pasif atau terlalu kompleks, SDWB menggabungkan elemen keselamatan, kemudahan penggunaan, kecekapan kos dan kesesuaian dengan keperluan latihan tempatan. Rekabentuknya dihasilkan secara khusus oleh pasukan inovasi dan telah melalui fasa ujian di peringkat institut sebelum rancangan untuk dikomersialkan. 9, .HVLPSXODQGDQ&DGDQJDQPelaksanaan Safety Domestic Wiring Board (SDWB) dalam metodologi pengajaran telah menunjukkan potensi besar dalam mengurangkan risiko bahaya serta menggalakkan amalan kerja selamat dalam persekitaran domestik. SDWB bukan sahaja berupaya mensimulasikan litar dan senario elektrik kehidupan sebenar, malah menyediakan satu platform pembelajaran yang selamat dan praktikal untuk pelatih mempelajari, mencuba serta menguji pelbagai konfigurasi pendawaian domestik. Melalui penggunaan SDWB, pelatih dapat mempelajari kemahiran asas pendawaian elektrik dan menjalankan pengujian atau pemasangan secara selamat terhadapperalatan elektrik biasa sebelum berhadapan dengan situasi sebenar di lapangan. Hal ini bukan sahaja membantu meningkatkan keyakinan dan kecekapan pengguna, malah turut menjimatkan kos bahan latihan kerana penggunaan SDWB membolehkan latihan berulang dilakukan tanpa kerosakan pada komponen. Secara keseluruhan, SDWB merupakan satu langkah signifikan dalam meningkatkan tahap pendidikan keselamatan elektrik. Ia menawarkan penyelesaian berskala yang menarik dan efektif dalam usaha meningkatkan kesedaran serta kecekapan keselamatan elektrik di kalangan pelatih, orang awam, mahupun profesional. Bagi memperkukuh impak projek ini, penyelidikan dan pembangunan selanjutnya wajar ditumpukan ke arah memperluaskan keupayaan SDWB, termasuk integrasi teknologi rumah pintar (smart home)dan sistem tenaga boleh diperbaharui. Usaha ini bertujuan memastikan SDWB kekal relevan dan sejajar dengan perkembangan semasa dalam bidang kejuruteraan elektrik serta pematuhan terhadap piawaian keselamatan yang sentiasa berubah. .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7114
5XMXNDQ[1] Bakri, A., & Zakaria, I. H. (2018). Uplifting theFunction of Maintenance Management towardsSustainable Performance of Laboratory andWorkshop in TVET Institutions. The Journal ofSocial Sciences Research, No. 6, 153-160.[2] Omar, M. K., Ismail, N., Rauf, M. A., & Puad, M.H. M. (2020). Factors on Deciding TVET for FirstChoice Educational Journey among Pre-Secondary School Student. European Journal of Molecular &Clinical Medicine, 7, 609-622[3] Hong, C. M., Chíng, C. K., & Roslan, T. R. N.(2021). Studentsí Tendencies in ChoosingTechnical and Vocational Education and Training(TVET): Analysis of the Influential Factors UsingAnalytic Hierarchy Process. Turkish Journal ofComputer and Mathematics Education, 12, 2608-2615.https://doi.org/10.17762/turcomat.v12i3.1262[4] Sitepu, E. S., Nursiah, & Azhar, A. (2020). TheRole of Intellectual, Emotional and SpiritualIntelligence towards Entrepreneurial Intentionamong TVET Students Indonesia and Malaysia.International Journal of Technical Vocational andEngineering Technology, 2, 117-123[5] Nasir, M., Alvi, A. S., & Tarar, M. G. (2021).Role of Technical Education and VocationalTraining in Promoting Youth Employment: ACase study of TVET Institutes in DistrictGujranwala. Pakistan Social Sciences Review, 5,402-413.http://doi.org/10.35484/pssr.2021(5-I)31https://pssr.org.pk/issues/v5/1/role-of-technicaleducation-and-vocational-training-in-promotingyouth-employment-a-case-study-of-tevt-institutesin-district-gujranwala.pdf[6] Mohd Hafizuddin, M. H., Mohd Zaid, M. R., &Norazah, M. A. (2023). Penggunaan PBL dalamkursus kejuruteraan. Online Journal for TVETPractitioners, 8(1), 45ñ52.[7] Mohd Azizi, M. A., & Noraini, N. (2024).Keberkesanan PBL dalam latihan TVET. JurnalPendidikan Teknikal Malaysia, 14(2), 22ñ30.[8] (2024). Garis Panduan Pendawaian ElektrikPepasangan Domesttik (2nd ed., Vol. 2) [Reviewof Garis Panduan Pendawaian Elektrik Pepasangan Domesttik]. Suruhanjaya Tenaga. (Original workpublished 2022).219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7115
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This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License, permitting copy and redistribution of the material and adaptation for commercial and uncommercial use. (QKDQFLQJ/RDG)UHTXHQF\\&RQWUROLQ3RZHU6\\VWHPV8VLQJ-D\\D2SWLPL]DWLRQDQG+\\EULG$UWLILFLDO1HXUDO1HWZRUN7HFKQLTXHVYogeswaran Seleappan1*, Suhairi Rizuan Che Ahmad2, Kanendra Naidu3 , Mohd Faqrul Radzi4Tahiruddin41Centre for Instructor and Advanced Skill Training, Jalan Petani 19/1, Seksyen 19, 40300 Shah Alam Selangor Darul Ehsan, Malaysia 2Universiti Kuala Lumpur (UniKL) - British Malaysia Institute (BMI), 53100 Kuala Lumpur, MALAYSIA 3Universiti Teknologi MARA (UiTM), 40450 Shah Alam, Selangor, MALAYSIA 4Centre for Instructor and Advanced Skill Training, Jalan Petani 19/1, Seksyen 19, 40300 Shah Alam Selangor Darul Ehsan, Malaysia *Corresponding authorís email: [email protected]$EVWUDFW ñ This research examines the implementation of Artificial Neural Network (ANN)-basedcontrol strategies for enhancing Load Frequency Control (LFC) in a single-area power system. Conventional Proportional-Integral-Derivative (PID) controllers, although widely used due to their simplicity, often fall short in managing the non-linear and time-varying characteristics of modern power systems. To address these limitations, this study evaluates and compares the performance of several control configurations: Tuned PID, ANN-tuned PID (ANN-PID), Jaya Algorithm optimized PID (JAYA-PID), ANN combined with Jaya-PID (ANN-JAYA-PID), Particle Swarm Optimizationoptimized PID (PSO-PID), and ANN integrated with PSO-PID (ANN-PSO-PID). MATLAB Simulink is utilized to simulate and implement these control strategies under various load disturbance scenarios. The ANN model is trained using data generated from simulations to provide predictive capabilities that improve controller adaptability. Optimization algorithms, namely the Jaya and PSO techniques, are applied to determine the optimal PID parameters, aiming to enhance system stability and dynamic response. The simulation results indicate that the ANN-Jaya-PID configuration offers the best overall performance among the models tested. It achieves faster settling times and effectively minimizes both overshoot and undershoot, demonstrating superior ability in handling system disturbances. Additionally, the Jaya algorithm significantly improves the robustness of the LFC system in managing non-linear behavior. This study highlights the effectiveness of integrating intelligent control methods and metaheuristic optimization techniques in enhancing the performance of LFC. The proposed ANN-Jaya-PID controller provides a promising solution for improving frequency stability in evolving power systems with increasing demand for reliability and precisionKeywords: Load Frequency Control, Artificial Neural Network, PID Controller, Jaya Algorithm, Particle Swarm Optimization Article History Received December 2017 Received in revised form January 2018 Accepted March 2018 , ,QWURGXFWLRQLoad Frequency Control (LFC) is a critical component in modern power systems, ensuring their stability and reliable operation. LFC aims to maintain the system frequency at a nominal value, regulate the power exchange between interconnected systems, and quickly respond to load variations. With the increasing complexity of power systems, especially with the integration of renewable energy sources and the deregulation of electricity markets, the need for efficient LFC systems has never been more crucial. The evolving dynamics of these systems, characterized by non-linearity, volatility, and uncertainty, present significant challenges for traditional control techniques.The primary objective of LFC is to prevent frequency .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7125
deviations from the nominal value, which could lead to system instability. Conventional control methods, such as Proportional-Integral-Derivative (PID) controllers, have been widely used to achieve this objective. However, they are often inadequate for addressing the complexities introduced by modern power systems. Traditional controllers primarily rely on linear models, which fail to capture the nonlinear behavior of power systems, particularly with the integration of renewable energy sources. This limitation can result in instability when faced with large disturbances or rapid fluctuations in renewable generation [1]. Furthermore, conventional PID controllers exhibit slow response times to load disturbances, exacerbating frequency instability in systems with high renewable energy penetration [2]. To overcome these limitations, advanced control strategies that account for the dynamic and nonlinear nature of modern power systems are necessary.Another critical aspect of LFC is managing the power exchange between interconnected systems, also known as tie-line power. Maintaining the balance of power between areas is especially challenging in multi-area systems, where adherence to power-sharing agreements is essential. Inadequate management of tie-line power can lead to power imbalances, which compromise system stability [3]. Moreover, LFC systems must be capable of responding rapidly to load disturbances and restoring the frequency to its nominal value. The integration of soft computing techniques has proven beneficial in improving the responsiveness of LFC systems, enabling faster and more efficient frequency regulation [3]. Despite the advancements in control strategies, issues such as communication failures and cyber threats in smart grids continue to complicate the implementation of effective LFC, highlighting the need for further research and the development of adaptive control techniques [4].Conventional LFC strategies, particularly those relying on PID controllers, often fail to adequately address the challenges posed by modern power systems. These controllers are designed based on linearized models, which oversimplify the complex dynamic interactions in power grids. They fail to account for the presence of renewable energy sources, which are inherently more volatile and less predictable than traditional energy generation methods [5]. As renewable energy sources, such as wind and solar, become a larger share of the energy mix, traditional controllers struggle to maintain system stability. Furthermore, conventional controllers have slow response times when faced with sudden load changes, a critical issue for modern power systems that require rapid adjustments to maintain frequency stability [6].The increasing penetration of renewable energy, coupled with the complexity of modern power systems, underscores the limitations of traditional LFC strategies. As power systems evolve to include microgrids, electric vehicles, and other distributed energy resources, the robustness and adaptability of conventional controllers are further challenged [7]. The dynamic nature of these systems necessitates the development of advanced, intelligent control strategies that can handle non-linearity, uncertainties, and rapid fluctuations in load and generation.Optimization techniques, such as Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), have also been applied to LFC systems to improve the performance of PID controllers. These techniques are capable of tuning controller parameters to enhance system stability, reduce overshoot, and improve settling times [8]. Moreover, the use of Fractional-Order PID (FOPID) controllers, which offer greater flexibility and adaptability in dynamic environments, has also gained attention [9]. These advanced optimization techniques help mitigate the limitations of traditional controllers by providing more robust and efficient solutions for frequency regulation.Among the various optimization algorithms, the JAYA algorithm has recently gained attention for its simplicity and effectiveness in solving constrained and unconstrained optimization problems. This gradient-free algorithm iteratively improves candidate solutions by steering them toward optimal solutions while avoiding the worst solutions in the population. The JAYA algorithm has shown significant promise in optimizing the performance of PID controllers in LFC applications, demonstrating superior performance in terms of settling time and overshoot compared to other optimization techniques, such as PSO and Ant Colony Optimization [10]. Additionally, JAYA's parameterless design reduces the computational burden associated with tuning parameters in other evolutionary algorithms, making it an attractive option for complex power system optimization tasks [11].Fuzzy Logic Controllers (FLCs) have also been proposed for LFC in power systems, particularly in handling uncertainties and variable loads. However, FLCs face challenges such as the complexity of designing a comprehensive rule base and sensitivity to parameter variations, which can degrade performance under fluctuating conditions [12]. To improve the effectiveness of FLCs, optimization techniques like GA and PSO can be applied to tune the parameters, making the controllers more adaptive to dynamic environments [13].Intelligent control techniques, such as Artificial Neural Networks (ANNs) and optimization algorithms, have emerged as promising solutions to these challenges. ANNs offer adaptability and can model complex, nonlinear relationships in power systems. They have been shown to outperform traditional PID controllers in terms of dynamic response and stability, especially in interconnected systems [8]. However, ANN-based controllers also face .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7126
limitations, such as slower response times and computational inefficiency, particularly when dealing with highly variable load conditions [14]. To address these challenges, hybrid approaches that combine ANN-based controllers with traditional methods like PID have been proposed. These hybrid systems offer the benefits of both approaches, improving robustness and response times under varying system conditions.This paper proposes the use of an ANN-based controller optimized using the JAYA algorithm to enhance LFC in power systems. The primary aim of this paper is to:ï Conduct a comparative analysis of the performance ofconventional PID controllers and advanced ANNbased controllers, namely ANN-PSO-PID and ANNJAYA-PID, with a particular focus on their efficacy inmanaging load fluctuations.ï Investigate the incorporation of optimizationtechniques for refining the parameters of PIDcontrollers and enhancing the capability of ArtificialNeural Networks (ANNs) to predict optimalcontroller responses across a range of load conditions.The LFC is developed using an Artificial Neural Network (ANN)-based approach, in which the parameters of the PID controller are optimized through the PSO algorithm and Jaya Algorithm for a single-area power system. The training data for the ANN model are generated and evaluated based on the proposed process, as depicted in Fig. 1.Fig. 1: Process Diagram,, 6\\VWHP%DFNJURXQGA. Single Area LFC ModelThe modeling of LFC plays a critical role in analyzing the operational behavior of the system and its dynamic response to fluctuations in load demand and disturbances. The primary objective of LFC is to maintain a balance between the power supply and the load demand, thereby ensuring that the system frequency stays within acceptable limits. TABLE I POWER SYSTEM PARAMETERS VALUE FOR LFC /)&V\\VWHPSDUDPHWHUV 9DOXHVGenerator power output, !\"# 250MW System frequency, +] 60 Hz Turbine time constant, $# 0.5 sec Turbine reheat constant, %& 0.5 Sec Turbine reheat time constant, $& 12 Sec Governor time constant, $' 0.3 Sec Speed regulation, R 15 pu Inertia constant, + 5 Sec The model incorporates the dynamic behavior of governors, turbines, and load dynamics, while simplifying the system by disregarding nonlinearities to facilitate implementation [15]. The single-area LFC model of a thermal system is shown in Fig. 2.Fig. 2. Single area LFC model of thermal system Power System B. System ControllersPID controller is utilized to tune LFC. The controller adjusts the Area Control Error (ACE) by applying the proportional, integral, and derivative components of the error signal. ACE quantifies the discrepancy between the desired and actual system outputs. The proportional component addresses the current error, the integral component accumulates past errors to mitigate steadystate deviations, and the derivative component forecasts future errors based on the rate of change, thereby improving system stability and response time. Fig. 3. PID Controller Developed For LFC .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7127
The PID controllerís parameters include proportional gain (Kp), integral time constant (Ti), and derivative time constant (Td). Figure 3 depicts the block diagram of the PID controller for LFC. The PID controllerís output in the time domain is given by Equation 1: ()*+ = , -./)*+ 0,-1 2 /)*+3* 0 -445)6+4667 (1) where -., -1 and -4 represent the proportional, integraland derivative gains of the controller. The term /)*+ denotes the error signal (ACE) used in the loadfrequency control (LFC) system. ,,, 6\\VWHP0HWKRGRORJ\\To enhance both the transient response and steady-state performance of the Load Frequency Control (LFC) system, this study employs two advanced metaheuristic optimization techniques, which are Particle Swarm Optimization (PSO) and JAYA algorithm to optimally tune the PID controller parameters. Both algorithms seek to minimize the frequency deviation error by adjusting the proportional (Kp), integral (Ki), and derivative (Kd) gains, thereby improving the overall stability and dynamic response of the LFC. A. Particle Swarm Optimization (PSO)Particle Swarm Optimization is a population-based stochastic search technique inspired by the collective social behavior observed in bird flocking and fish schooling. In this study, PSO is utilized to optimize the PID controller gains by iteratively updating a swarm of candidate solutions in the search space according to their individual and collective experiences. The PSO implementation initializes a swarm of 10 particles, where each particle represents a candidate solution vector X=Kp, Ki, Kd within predefined bounds [0.2,5] for each gain parameter. Particle velocities are initially set to zero. The objective function evaluates the LFC system's frequency control error corresponding to each candidate set of PID parameters. At each iteration (up to 100), the particles update their velocities and positions guided by three components, which are inertia, personal best (pBest), and global best (gBest). The inertia weight 8 decreases linearly from 1 to 0.1 to balance exploration and exploitation. Velocity updates incorporate cognitive and social coefficients 9:and 9; alongside random factors to encourage diversesearch dynamics. Updated positions and velocities are constrained within the allowable parameter ranges to maintain feasible solutions. The iterative process continues until convergence, or the maximum iteration count is reached, yielding optimal PID gains that minimize frequency deviations. The convergence trend is tracked by monitoring the best fitness value per iteration. B. Jaya Optimization AlgorithmThe Jaya algorithm, a parameter-less optimization technique, is employed as an alternative approach to PID tuning in the LFC system. Distinguished by its simplicity and lack of algorithm-specific parameters, Jaya iteratively improves candidate solutions by moving them closer to the best solution and away from the worst in the population. In this study, a population size of 20 candidate solutions is initialized randomly within the same bounded search space as PSO. Each candidate solution x = [Kp, Ki, Kd] is evaluated via the objective function that measures the LFC frequency error. At every iteration (up to 100), the algorithm identifies the best and worst performing solutions based on objective function values. Each candidate is then updated to generate a new solution X_new, guided by its relative distance to the best and worst solutions, thus promoting convergence toward an optimal set of PID gains. If the new solution yields an improved objective function value, it replaces the original candidate. This iterative process continues until the algorithm converges to an optimal or near-optimal solution, effectively tuning the PID parameters for improved LFC performance. The stepwise procedure of the Jaya algorithm for this optimization task is depicted in Figure 4. Start Setting population size, number of design variables, and the number of iterations Identified the best and worst candidate solutions from initial population as: <(>)?/@* and <(>)ABC@*Is the solution corresponding to >D,E,FGbetter than solution >D,E,F? Safe and keep >D,E,FSafe and replace >D,E,F with >D,E,FGNumber of iterations completed? Optimal solution is obtained ExitConsider the new set of solutions as the initial population Iteration = Iteration + 1 <HV 1R1R<HVBased on <(>)?/@* and <(>)ABC@* solutions, modify the decision variables using the equation: >D,E,FG = >D,E,F + C1,E,FH>D,I/@*,F J K>D,E,FKL J C2E,F(>D,8BC@*,F J K>D,E,FK)Fig 4: Flow Chart of JAYA Algorithm Applied To LFC .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7128
C. Artificial Neural NetworkThe integration of Artificial Neural Networks (ANNs) into Load Frequency Control (LFC) systems significantly enhances their performance by leveraging adaptive learning capabilities. This integration enables the ANN controller to learn the LFC modelís behavior by training on its input and output data. The primary objective is to accurately predict the controllerís output signal based on the Area Control Error (ACE). Fig. 5 illustrates the typical architecture of an ANN model. A feedforward ANN structure consists of three primary layers: input, hidden, and output. Depending onthe number of layers, ANN models can be categorized into three types: single-layer, multi-layer, and radial-layer networks. Among these, multi-layer feedforward networks are widely applied in machine learning due to their biologically inspired architecture, which facilitates data reception, processing, and transmission, mimicking neural functions in the brain. The layers are interconnected through neurons, with weights and biases determining the strength of these connections. The weighted summation of inputs is calculated using mathematical operations, as described in Eq. (2): ,MN = ,O 81NPN 0 IDQ1R:, ,)S+PN represent the input signal value, 81N denotes the weightsbetween connected layers, ID is the weight value attributedto the nodes, and n is the total number of input signal. Thebackpropagation (BP) algorithm is typically employed to adjust these initial weights. The sigmoid activation function, often used to detect the output signal, is mathematically expressed in Eq. (3): <)@+ = ,TT 0 /UVN ,)W+The core principle of this methodology lies in optimizing ANN performance by iteratively refining the weights of interconnections. This process generates output signals through predictive computations based on gradient descent, incorporating weight adjustments )X8+, asdetailed in Eq. (4). )*+ = ,Y8N1Z)* J T+ 0 ,[,X8N1Z)*+ ,)\\+In this equation, 8N1Z)*+ represents the current trainingweight, 8N1Z)* J T+ is the previous training weight, whereas! is the learning rate, and \" is the momentum coefficient. The BP algorithm operates in two stages: forward propagation and backward propagation. During each iteration, the weights of the ANN are continually updated, and the mean squared error (MSE) between the predicted and actual values is calculated using Eq. (5): ,]M^ = ,T_OO`aN)F+ J ,bN)F+c;dNR:Q1R:,)e+where variables n and m represent the number of input data points and output signals, respectively, while aN)F+ andbN)F+ denote the actual and predicted outputs.Fig 5: Structure of an artificial neural network (ANN) D. Optimal Neural NetworkOptimal neural network for Load Frequency Control (LFC) in a single-area power system is developed by integrating parameter optimization and advanced neural network training techniques. PID controller parameters, Kp, Ki and Kd gains are fine-tuned using metaheuristic optimization algorithms which are Particle Swarm Optimization (PSO) and JAYA algorithm. These optimization techniques iteratively minimize frequency deviation errors, thereby improving the dynamic response and stability of the LFC system. Optimized PID parameters serve as training data for a multilayer feedforward neural network implemented in MATLAB. The network architecture comprises three hidden layers with 40, 20, and 10 neurons, designed to enhance the modelís learning capacity and ability to capture complex input-output relationships inherent in the LFC system. Bayesian regularization is employed during training to improve generalization and avoid overfitting, particularly valuable given the nonlinear dynamics of power systems. Training proceeds for up to 100 epochs or until the mean squared error (MSE) reaches a predefined threshold, ensuring both convergence and accuracy. The integration of the PID controller optimization with neural network based learning provides a hybrid model that combines the strengths of both techniques. The optimized PID gains improve the initial control performance, while the neural network further enhances the systemís ability to adapt to dynamic changes in the system. .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7129
,9 5HVXOWDQG6LPXODWLRQThe performance of load frequency control for a singlearea power system network was evaluated using MATLAB Simulink. The study compared six control strategies which is Tuned PID, ANN-PID, PSO optimized PID, ANN-PSO-PID, JAYA optimized PID, and ANNJAYA-PID. Simulations were conducted for 30 seconds, with step-load perturbation of 10% and 20% introduced at t = 5 seconds. Tables II and III present the results for these step-load perturbations, including metrics such as maximum overshoot, maximum undershoot, transient time, and ITAE value. PID controller tuned in MATLAB, the gain parameters are provided as Kp=1.9809, Ki = 7.8513 and Kd=0.1200. PSO optimized PID are Kp=5.000, Ki=5.000 and Kd=1.0914, whereas the JAYA optimized PID controller achieves gains of Kp=5.000, Ki=5.000 and Kd=1.0055. TABLE II COMPARISON PERFORMANCE FOR SLP=0.1 P.U 1R &RQWUROOHU7\\SH7UDQVLHQW7LPH 6 2YHUVKRRW 3X 8QGHUVKRRW 3X ,7$(1 Tuned PID 6.4413 0.0168 -0.0500 0.8961 2 PSO Optimized PID 5.9711 0.0006 -0.0500 0.0145 3 JAYA Optimized PID 5.1429 0.0004 -0.0500 0.3377 TABLE III COMPARISON PERFORMANCE FOR SLP=0.2 P.U 1R &RQWUROOHU7\\SH7UDQVLHQW7LPH 6 2YHUVKRRW 3X 8QGHUVKRRW 3X ,7$(1 Tuned PID 6.4435 0.0336 -0.1000 1.7921 2 PSO Optimized PID 5.9629 0.0119 -0.1000 0.0890 3 JAYA Optimized PID 5.1427 0.0008 -0.1000 0.6757 The performance comparison shows that the JAYA Optimized PID controller outperforms the Tuned PID and PSO Optimized PID controllers for both SLP values of 0.1 P.U. and 0.2 P.U. It achieves the lowest transient time, minimal overshoot, and the best ITAE in both cases, demonstrating superior response and error minimization. While the PSO Optimized PID controller offers a lower ITAE at SLP 0.1 P.U., it shows slower response times and higher accumulated errors at SLP 0.2 P.U. The comparative profiles of frequency deviation for optimized PID following an SLP of 10% and 20% are shown in Fig. 6-7.Fig. 6. System response for optimized controllers at 10% SLP Fig. 7. System response for optimized controllers at 20% SLP Controller based JAYA optimized PID consistently demonstrates its effectiveness in terms of transient time and overshoot minimization, making it the optimal choice for both lower and higher load conditions in comparison to the Tuned PID and PSO Optimized PID controller. Fig. 8-9 shows the comparison between the ANN PID, ANNPSO-PID and ANN-JAYA-PID at SLP 0.1 and 0.2respectively.Fig. 8. Comparison of frequency respone for ANN controller for 10% SLP Fig. 9. Comparison of frequency respone for ANN controller for 20% SLP.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7130
The graph demonstrates the comparative performance of three controller ANN PID, ANN-PSO-PID, and ANNJAYA-PID. At SLP 0.1, the ANN-PSO-PID exhibits performance metrics closely aligned with the ANNJAYA-PID, particularly in transient time, overshoot, and undershoot, indicating competitive effectiveness. However, at SLP 0.2, the ANN-JAYA-PID outperforms the other controllers, achieving the lowest transient time and overshoot values. These findings indicate that JAYA optimization combined with ANN provides superior control robustness and stability under varying load conditions compared to other controllers. To further assess the robustness of ANN-JAYA-PID, the SLP for the controllers varied from -20% to +20%. The results, as detailed in Table 4-5, demonstrate the controller's response to fluctuations in SLP ranging from a 20% decrease to a 20% increase at SLP 0.1 and 0.2. The transient time, maximum overshoot, and maximum undershoot metrics provide a comprehensive view of the controllerís dynamic behavior under these perturbations.TABLE IV PERFORMANCE OF THE ANN JAYA PID CONTROLLER UNDER FLUCTUATIONS AT SLP 0.1 )OXFWXDWLRQLQ6/3 6/3 7UDQVLHQW7LPH V 0D[2YV SX 0D[8QGHUVKRRW SX Decrease 20% 0.08 6.2441 0.0029 -0.0390Decrease 10% 0.09 6.3315 0.0028 -0.0440ANN JAYA PID 0.1 6.4635 0.0027 -0.0490Increase 10% 0.11 6.7305 0.0027 -0.0540Increase 20% 0.12 7.5199 0.0027 -0.0590At an SLP base of 0.1, the controller exhibits a stable transient response, with transient time increasing modestly from 6.2441 seconds at a 20% decrease to 7.5199 seconds at a 20% increase. The maximum overshoot remains minimal, ranging narrowly between 0.0027 and 0.0029 per unit, indicating a tightly controlled response. Similarly, the maximum undershoot slightly intensifies from -0.0390 to -0.0590 per unit as the SLP rises, reflecting minor variations in the system's deviation before stabilization. These variations indicate the controller's ability to maintain performance close to nominal levels despite SLP fluctuations. TABLE V PERFORMANCE OF THE ANN JAYA PID CONTROLLER UNDER FLUCTUATIONS AT SLP 0.2 )OXFWXDWLRQLQ6/3 6/3 7UDQVLHQW7LPH V 0D[2YV SX 0D[8QGHUVKRRW SX Decrease 20% 0.18 9.8520 0.0019 -0.0893Decrease 10% 0.19 9.9555 0.0019 -0.0943ANN JAYA PID 0.2 10.0632 0.0019 -0.0993Increase 10% 0.21 10.1758 0.0019 -0.1043Increase 20% 0.22 10.2944 0.0019 -0.1093As seen in Table 5 when the base SLP is raised to 0.2, a consistent trend emerges with increased transient times, ranging from approximately 9.8520 to 10.2944 seconds as SLP varies by ±20%. The maximum overshoot is further reduced compared to the lower base SLP, stabilizing around 0.0019 per unit, which points to enhanced control precision at higher load conditions. The maximum undershoot values deepen from -0.0893 to -0.1093 per unit, which, although higher than those at the lower SLP, remain within acceptable operational limits. These consistent performance metrics under significant load variations validate that the ANN-JAYA-PID controller generalizes well beyond its training dataset. 9 &RQFOXVLRQ This study conclusively demonstrates that integrating Artificial Neural Networks with metaheuristic optimization algorithms significantly enhances Load Frequency Control in single-area power systems. Among the evaluated control strategies, the ANN-JAYA-PID controller consistently outperformed conventional PID and other hybrid configurations by achieving faster settling times, reduced overshoot, and superior disturbance rejection under varying load conditions. ANN-PSO-PID controller also exhibited strong performance, with results closely approaching those of ANN-JAYA-PID, particularly at SLP 0.1. The application of the Jaya algorithm proved particularly effective in optimizing PID parameters, thereby improving the systemís robustness in managing nonlinear and timevarying dynamics typical of modern power grids. Moreover, the predictive capabilities of the ANN model, trained on simulation data, allowed for adaptive and precise controller adjustments, addressing the limitations of traditional controllers. This research validates the potential of combining intelligent control methodologies with advanced optimization techniques to meet the increasing demand for reliability and precision in frequency stability. The findings underscore the practicality of ANN-JAYA-PID as a promising control framework that can adapt to the complexities of evolving power systems, thereby contributing valuable insights toward the development of more resilient and efficient energy management solutions. Future work may focus on extending this approach to multi-area systems and incorporating real-time data for further enhancement. $FNQRZOHGJHPHQWVThe authors wish to acknowledge the Centre for Instuctor and Advance Skill Training (CIAST), Department of Skill Development, Ministry of Human Resouces, Malaysia and Universiti Kuala Lumpur for their encouragement and support in completing this research work. .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7131
&RQIOLFWRI,QWHUHVWThe authors declare no conflict of interest in the publication process of the research article. $XWKRU&RQWULEXWLRQVY.S is the main author who conducted the systemdesign, data collection and simulations. S.R.C.A supervised the work and draft review. K.N contribute to conceptualization and review, M.F.R contribute to editing the manuscipt. 5HIHUHQFHV[1] S. A. Nugroho and A. F. Taha, ìHow Vintage Linear Systems Controllers Have Become Inadequate In Renewables-Heavy Power Systems: Limitations and New Solutions,î in 2022 American Control Conference (ACC), IEEE, Jun. 2022, pp. 4553ñ4558. doi: 10.23919/ACC53348.2022.9867388.[2] S. H. Yeoh, K. H. Yiauw, S. Y. Yip, and Z. H. Kwan, ìOptimizing Frequency Stability in Power Systems through Particle Swarm Optimization-Based Control Strategies,î in 2024 20th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), IEEE, Mar. 2024, pp. 79ñ84. doi: 10.1109/CSPA60979.2024.10525601.[3] D. Jain and M. K. Bhaskar, ìOptimization of controllers using soft computing technique for load frequency control of multiarea deregulated power system,î International Journal of Applied Power Engineering (IJAPE), vol. 13, no. 1, p. 52, Mar. 2024, doi: 10.11591/ijape.v13.i1.pp52-65. [4] M. Wadi, A. Shobole, W. Elmasry, and I. Kucuk, ìLoad frequency control in smart grids: A review of recent developments,î Renewable and Sustainable Energy Reviews, vol. 189, p. 114013, Jan. 2024, doi: 10.1016/j.rser.2023.114013.[5] A. R. , & S. V. Rajkumar, ìAn impact of TCPS with LFC inan multi area power system using conventional controllers,î 2011. [6] Sateesh Kumar Vavilala, ìLoad Frequency Control of Two Area Interconnected Power System Using Conventional and Intelligent Controllers,î Int. Journal of Engineering Research and Applications, vol. 4, no. 1, pp. 156ñ160, Jan. 2014. [7] S. Kumari and P. K. Pathak, ìA State-of-the-Art Review on Recent Load Frequency Control Architectures of Various Power System Configurations,î Electric Power Components and Systems, vol. 52, no. 5, pp. 722ñ765, Mar. 2024, doi: 10.1080/15325008.2023.2234373.[8] F. Bano, M. Ayaz, D.-Z. Baig, and S. M. H. Rizvi, ìIntelligent Control Algorithms for Enhanced Frequency Stability in Single and Interconnected Power Systems,î Electronics (Basel), vol. 13, no. 21, p. 4219, Oct. 2024, doi: 10.3390/electronics13214219.[9] M. K. Senapati, P. S. Das, K. P. Samal, J. Sahoo, and M. K. Debnath, ìEnhancing Load Frequency Control in DGIntegrated Power Systems Using Advanced PID Optimization,î in 2024 3rd Odisha International Conference on Electrical Power Engineering, Communication and Computing Technology (ODICON), IEEE, Nov. 2024, pp. 1ñ6. doi: 10.1109/ODICON62106.2024.10797579.[10] S. Pahadasingh, C. Jena, B. P. Ganthia, and C. K. Panigrahi, ìJAYA Algorithm-Optimized Load Frequency Control of a Four-Area Interconnected Power System Tuning Using PID Controller,î Engineering, Technology & Applied Science Research, vol. 12, no. 3, pp. 8646ñ8651, Jun. 2022, doi: 10.48084/ETASR.4891. [11] N. Palod, V. Prasad, and R. Khare, ìComparing a Parameterless Technique Jaya with Parameters-Based Evolutionary Algorithms,î 2023, pp. 53ñ63. doi: 10.1007/978-981-19-9285-8_6. [12] F. Zahid, M. Wali, and G. E. M. Abro, ìVoltage and Frequency Control of A Synchronous Generator Implementing FuzzyLogic Approach,î in 2024 1st International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR), IEEE, May 2024, pp. 1ñ6. doi: 10.1109/ICIESTR60916.2024.10798139.[13] S. Jangiri and K. O. Jones, ìOptimizing an intelligent fuzzyhybrid Proportional-Integral-Derivative (PID) controller for Load Frequency Control systems,î in 2024 International Conference on Information Technologies (InfoTech), IEEE, Sep. 2024, pp. 1ñ4. doi: 10.1109/InfoTech63258.2024.10701332.[14] Z. Li, Z. Chu, and F. Teng, ìOptimal design of neural network structure for power system frequency security constraints,î IET Conference Proceedings, vol. 2023, no. 15, pp. 230ñ236, Oct. 2023, doi: 10.1049/icp.2023.2147.[15] D. A. Leiva Roca, P. Mercado, and G. Suvire, ìSystemFrequency Response Model Considering the Influence ofPower System Stabilizers,î IEEE Latin America Transactions, vol. 20, no. 6, pp. 912ñ920, Jun. 2022, doi: 10.1109/TLA.2022.9757373..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7132
This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License, permittingcopy and redistribution of the material and adaptation for commercial and uncommercial use. 3,'&RQWURO7XQHGE\\3DUWLFOH6ZDUP2SWLPL]DWLRQ 362 IRU9HKLFOH6XVSHQVLRQ6\\VWHPNurul Shahira Mohd Kassim1, Ahmad Tarmizi Md Nor2*, Norzilahwati Md Noh3, Mohd Hanif Harun4123Unit Akademik dan Pendidikan Berterusan, Kolej Komuniti Kelana Jaya, Petaling Jaya Commercial City, 43700 Petaling Jaya, Selangor, Malaysia3Faculty of Mechanical Technology and Engineering, UTeM, Hang Tuah jaya, 76100 Durian Tunggal, Melaka, Malaysia *corresponding authorís email: [email protected]$EVWUDFW ñ Road-induced vibrations often lead to reduced ride comfort, especially when drivingon uneven surfaces, as these vibrations transfer to the vehicle body. A reliable suspension system is essential to ensure better ride comfort and stability. This study proposes an active suspension system utilizing a PID controller optimized with Particle Swarm Optimization (PSO) to enhance ride performance. A complete seven degree-of-freedom (7-DOF) full car ride model was developed and validated by comparing its response trends with experimental data. The PID controller's performance then was assessed through simulations under two different speeds (10, and 50 km/h) to seek improvements in term of body vertical acceleration, roll rate and pitch rate. Simulations were conducted using MATLAB/Simulink, and the results were compared to passive suspension system. The Root Mean Square (RMS) results indicated that the PID controller improved ride comfort by approximately 74.6% compared to the passive suspension. Furthermore,it achieved an additional improvement of about 69.4% when the PID controller was tuned using the PSO algorithm. These findings demonstrate that PSO enables effective and efficient PIDparameter tuning, resulting in significantly improved ride performance.Keywords: suspension system, PID controller, Particle Swarm Optimization (PSO)Article HistoryReceived December 2017Received in revised form January 2018Accepted March 2018, ,QWURGXFWLRQIn the automotive industry, vehicle performance is often associated with engine power and torque. However, achieving optimal safety and handling is equally vital, especially in ensuring ride comfort and stability. Anefficient suspension system plays a significant role in maintaining tire-to-road contact, minimizing vibrations transmitted to the vehicle body, and enhancing the overall driving experience for the passengers. Generally, suspension systems are classified as passive, semi-active, and active. A passive suspension system consists of a fixed mechanical spring and damper. The system itself is dependable, less complex and is exorbitant. The function of the spring is for energy storage and then dissipates it through a damper. Spring will start to compress if a load is put to it, and it will continue to do so until the force produced by the compression equals the force being supplied by the load. However, due to damper limitation in absorbing energy to adapt to various road profiles, thepassive suspension system demonstrates low performance where its vibration amplitude is higher, and the time required to dissipate the vibration took longer[1]. [2] modifying the passive suspension system by placing a hydraulic actuator between sprung and unsprung masses to overcome the passive suspension basic problem. And it has been pointed out that by replacing passive suspension with controlled actuatorsí active or semi-active suspensions, the potential to theoretically and practically improved vehicle handling and stability can be achieved.Active suspension systems have gained traction in recent years as they offer improved ride comfort and handling by dynamically adjusting suspension parameters using electronically controlled actuators compared to the conventional springs and dampers that are used in passive and semi-active suspensions. The actuator performs its function by possessing or distributing system power and depending on the specified .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7133
design, the actuator can be controlled by a variety of controllers. The actuator delivers active force controlled by an algorithm that makes use of data obtained from attached vehicle sensors, its significantly higher responding capabilities against created vertical forces induced by unforeseen road input irregularities make it more efficient and better design trade-off compared to the passive and semi-active suspension system [3].Various control strategies, including PID, Fuzzy Logic, LQR, and Adaptive Neural Networks, have been explored for active suspension systems. Earlier research using Sliding Mode Controller (SMC) started as early as 2000s done by [4] that aimed a non-linear 7 DOF car model. The adaptability of the controller is demonstrated by altering the vehicle parameters. However, these studies have only been involved with the known system states that make it vulnerable for certain situations. [5]explore the performance using LQR controller in a fullcar model of an active suspension system. The controller arises among the earliest control strategies as it reliable in closed-loop systems, stable and simple computation.However, the major issue with using LQR control approach concerned that it is not able to work well in the presence of harsh road disturbances.Though, complex computational for some of the controller approaches shed light on the use of PID controllers. The PID controller stands out for its simplicity, robustness, and ease of implementation.Fig. 1: Conventional PID control system[6] and [7] proposed PID controller for activesuspension system aiming for reducing the acceleration of the sprung mass while exposed to a variety of different types of road profiles. And although conventional PID controller is uncomplicated and simple to construct, in practice, its performance is usually subpar when it is used in conjunction with complex and dynamic environments. As a result, evolutionary computation strategies have been effectively employed to regulate the PID controllers.The most prevalent swarm algorithm used to cater optimization problems including particle swarm optimization (PSO), ant colony (ACO), artificial bee colony (ABC), cuckoo search algorithm (CSA), glowworm swam optimization (GSO) and differential evolution (DE). Due to its merits, PSO algorithm has gained a lot of attention due to its simplicity in implementation and ability to converge to a good solution with a minimum number of iterations [8]. These results are also supported by [9] and [10] that proposedthe PSO algorithm performance for the optimal PID controller parameters for quarter-car model. The results indicate that the PID controller optimized by PSO algorithm yields superior results than using only PID controller.This study aims to investigate the improvement in dynamic performance of an active suspension system by integrating a PSO-optimized PID controller, with the goal of enhancing ride comfort and vehicle stability. The proposed control structure is considered to a 7 degree-offreedom (DOF) full vehicle ride model. MATLABSIMULINK software is used as the simulation platform to model vehicle dynamic and assess the performance of the control structure. To evaluate the effectiveness of the proposed controller, the passive suspension system and the active suspension system utilizing a PID controller are used as benchmarks.,, 0DWKHPDWLFDO0RGHOOLQJThere are some modelling assumptions that are takeninto consideration. The vehicle's body is a single sprung mass connected to four unsprung masses. The roll center coincides with the pitch center and is situated directly below the body's center of gravity. The suspensions between the sprung mass and the unsprung masses are modeled as passive dampers and spring components, which integrates force actuator to signify active suspension. The sprung mass is depicted as a plane and is permitted to move in the pitch, roll and yaw as well as vertical displacement of four unsprung masses. The tires were conceived to be linear springs devoid of any viscous dampening. It was also anticipated that the location of the center of gravity (CG) of the sprung mass would remain unchanged during the simulation.A. Modelling of 7-DOF Full Car Model inMATLAB/Simulink SoftwareFigure 3.3 shows the setup of the MATLAB/Simulink full-car model. The vehicle dynamics model was developed using MATLAB/Simulink version R2022a based on the mathematical equations that were derived from the full-car ride model equations. The input to the vehicle model is the road profile at the tires, while the dynamic output. responses are a variety of behaviourincluding the vehicle body vertical acceleration, pitch velocity and roll velocity..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7134
Fig. 2: 7-DOF full vehicle ride model in MATLAB/SimulinkB. Verification of a 7-DOF Full Car Model withExperimental Vehicle[11] presented experimental data that serve as areference benchmark for validating the simulation model developed in this study. The experimental investigation was conducted using a 1,300 cc Malaysian national vehicle, specifically the Proton Preve, by which the vehicle parameters are as in Table 1 below. Model verification was performed by comparing the trend of the simulated results with the corresponding trends observed in the experimental data.TABLE IFULL CAR MODEL PARAMETERSParameters ValueSprung mass (vehicle body) mass, !\" 1,160 kgUnsprung mass (tire) mass, !# 59 kgSuspension spring stiffness, $\" 15,000 N/mSuspension damping, %\" 1,000 Ns/mTire stiffness, $& 190,000 N/mWidth of sprung mass, ' 1.34 mDistance between front of vehicle and C.G of sprung mass, (1.28 mDistance between rear of vehicle to and C.G of sprung mass, )1.10 mC.Control Design Using PID ControllerIn this step, the PID controller was introduced to the full car ride model. This controller structure as in Fig. 3includes a closed loop feedback method that utilizes three control schemes to trigger the control action that is caused by road input disturbances and minimizes undesired motion to see its deliverable in improving the suspension systemís performance in terms of ride quality.Fig. 3: Basic structure for PID control systemThe vehicle's body vertical acceleration, pitch and roll rate was served as the loop controller's input and to achieve zero steady-state error, the desired reference values for these parameters were set to zero. Note that the least steady-state error reduces the vibrations effect to the vehicle body, thus in turn improves the ride quality.The controller's control action was prompted by the error signal, which was calculated from the discrepancies between actual behavior of input parameters and desired behavior to the suspension system. By multiplying the error by the controller gains, a controller signal is produced that corresponds to the actuator force acting on the vehicleís body contributed as the desired output.D.PID Controller Optimization with PSOThe PID controller was fine-tuned using PSO to achieve better performance. The first thing that will take place in PSO algorithm is the random initialization of the total number of particles. Each particle in PSO is related to velocity and position. All the particles that make up the population (swarm) move all throughout the search space in accordance with the velocity vector. The previous velocity and position information will be utilized to update the particles' new position. The position of each particle is characterized by *-th particle in the +-th dimensional vector as ,*=(,*1,,*2,Ö.,,*+)while the velocity vector is expressed as -*=(-*1,-*2,Ö.,-*+) where *=1,2,Ö,. (. is number of particles). In each cycle, the optimization is carried out by following the personal best solution of each particle and the global best of the whole swarm which then will be compared subsequently to determine the own best values. Literally, the best fitness value is expressed/*=(/*1,/*2,Ö.,/*+) and the fitness particle found at &time is expressed as /0=(/01,/02,Ö.,/0+). The velocities and positions of the particles are iteratively updated using the following equations [12][13]:-*+(&!+!1)!=!'-*+(&)!+!112(.+1(/*+ 34,*+(&))!+!122(.+2(/*+ 34,*+(&))!!!!!!!!!!!!(1)!!!!!!,*+(&!+!1)!=!,*+(&)!+!-*+(&!+!1)! !(2)!.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7135
Where,& = Number of iterations11, 12! = Acceleration coefficientrand1, rand2= Random number in range [0,1]' = Weight of inertia impact,,, 3,'&RQWUROOHU2SWLPL]DWLRQ$SSURDFKThe last iteration with the optimized value will be used and integrated into the active suspension control approach. The flow chart implementation of PSO to tune the PID gains depicted in the Figure 4.Fig. 4: The flowchart of PSO algorithm,9 5HVXOWVDQG'LVFXVVLRQThe simulation was done in three suspension system conditions: passive suspension, active suspension (PID controller) and active suspension (PID controller tuned by PSO). The results obtained are compared and evaluated in terms of vehicle body vertical acceleration, pitch velocity and roll velocity with three different cases, namely 10, 30 and 50 km/h named Case 1, 2 and 3 respectively.A. Performance Evaluation of Active Suspension withPID Controller Compared to Passive SuspensionThe evaluation of the ride performance of theactive suspension system implemented with a Proportional-Integral-Derivative (PID) control strategy including body vertical acceleration, roll rate, and pitch rate, were utilized to assess the efficacy of the control algorithm. The system response under the PID controller was quantitatively analysed using root-mean-square (RMS) metrics. Subsequently, these RMS values were compared against those obtained from the passive suspension system to assess performance differences. The percentage reduction in RMS values served as a quantitative indicator of the improvements in ride quality achieved by the active suspension system with the PID controller relative to the passive, uncontrolled suspension configuration.Fig. 5: Comparison of the performance for passive and PID controller at 10km/h for (a) Body vertical acceleration; (b) Roll rate; (c) Pitch rate.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7136
Fig. 5 shows that the active suspension system equipped with a PID controller improves body vertical acceleration, roll rate, and pitch rate as compared to passive suspension system. These improvements are summarized in Table 2 using RMS percentage reductions. Compared to passive suspension, the active system reduces body vertical acceleration by approximately 37.8%. This improvement results from actuator forces that actively counteract disturbances acting on the vehicle body due to road irregularities.Although the RMS improvements are moderate, they indicate better isolation of vibrations by the sprung mass, which enhances ride comfort over the passive suspension. Additionally, the active suspension more effectively reduces pitch and roll motions through realtime adjustments. Roll motion is a critical safety concern as it is a major cause of accidents during cornering or sudden maneuvers, while pitch motion negatively impacts ride comfort. By implementing the PID controller, the roll rate is reduced by 50.2% and the pitch rate amplitude by 62.7%, as shown in Fig. 5(b) and 5(c). These results confirm that the PID-controlled active suspension significantly improves both ride comfort and vehicle safety, especially at lower speeds.TABLE 2PERFORMANCE EVALUATION OF PASSIVE SUSPENSION AS COMPARED TO ACTIVE SUSPENSION WITH PID CONTROLLER AT 10KM/HObjective Function RMS VALUEPassive PID VarianceBody Acceleration (m/s2) 0.0057093 0.0035531 38.0%Roll Rate (rad/s) 0.0037440 0.0018657 50.1%Pitch Rate (rad/s) 0.0001636 0.0000609 62.7%Fig. 6: Comparison of the performance for passive and PID controller at 50km/h for (a) Body vertical acceleration; (b) Roll rate; (c) Pitch rateAs shown in Fig.6, the application of a PID controller compared to the passive suspension system at 50km/h results in an approximate 55.8% reduction in the amplitude of body vertical acceleration. This improvement arises because the PID controller is designed to regulate and minimize body displacement by generating additional actuator forces that counteract disturbances acting on the vehicle body from uneven road surfaces. Consequently, the active suspension systemís enhanced performance in reducing body acceleration reflects an improved ability of the sprung mass to isolate vibrations, thereby enhancing ride comfort.Compared to passive suspension, the active suspension system more effectively reduces pitch and roll rates through real-time adjustments. Roll motion is particularly critical as it is a leading cause of vehicle instability and accidents during cornering or maneuvering, while pitch motion adversely affects ride comfort. Therefore, minimizing these motions is essential for improved ride performance.With the PID controller integrated into the suspension system, roll rate is reduced by 62.0%, and pitch rate amplitude is reduced by 74.6%, as illustrated in Fig. 6(b) and 6(c), and summarized in Table 3. These results indicate that the PID-controlled active suspension can significantly enhance both ride comfort and vehicle safety across a range of speeds.TABLE 3PERFORMANCE EVALUATION OF PASSIVE SUSPENSION AS COMPARED TO ACTIVE SUSPENSION WITH PID CONTROLLER AT 50KM/HObjective Function RMS VALUEPassive PID VarianceBody Acceleration (m/s2) 0.0073392 0.0032439 55.8%Roll Rate (rad/s) 0.0010107 0.0003845 62.0%Pitch Rate (rad/s) 0.0008281 0.0002102 74.6%B. Performance Evaluation of Active Suspension withPID Controller Tuned by PSO AlgorithmThe performance of the active suspension system with PSO based PID controller is compared with conventional PID controller. Adopted the same road input disturbances, the simulation was carried out using the same vehicle parameters as in Table 1..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7137
Fig. 7: Comparison of the performance of PID controller with PSO algorithm at 10km/h for (a) Body vertical acceleration; (b) Roll rate; (c) Pitch rateThe results in Fig. 7 indicate that the amplitude of body vertical acceleration for the Particle Swarm Optimization (PSO)-optimized controller is the lowest compared to both the conventional PID tuning method and the passive suspension system. Specifically, the PSOoptimized controller achieves approximately a 55.4% reduction in body vertical acceleration, outperforming the 38.0% improvement obtained with the previous PID tuning approach. Additionally, the RMS value of the roll rate is further decreased to 0.0017164, representing an 8% reduction and demonstrating enhanced dynamic response with PSO optimization.A reduced roll rate is beneficial as it minimizes lateral movement and sway experienced by passengers during cornering maneuvers. Similarly, the body pitch rate amplitudes under PSO optimization are lower than those observed with the standard PID controller, with nearly a 49% reduction in RMS value. This decrease in pitch rate correlates with reduced bounce and jolting, thereby contributing to a more stable and comfortable ride. These improvements are quantitatively summarized as RMS percentage reductions in Table 4.TABLE 4PERFORMANCE EVALUATION OF ACTIVE SUSPENSION FOR PID AND PID TUNED BY PSO AT 10KM/HObjective Function RMS VALUEPID PID (PSO) VarianceBody Acceleration (m/s2) 0.0035531 0.0015847 55.4%Roll Rate (rad/s) 0.0018657 0.0017164 8.0%Pitch Rate (rad/s) 0.0000609 0.0000311 49.0%Fig. 8: Comparison of the performance of PID controller with PSO algorithm at 50km/h for (a) Body vertical acceleration; (b) Roll rate; (c) Pitch rateFig. 8(a) shows that the PID controller optimized with Particle Swarm Optimization (PSO) reduces body vertical acceleration amplitude by 58.0%, compared to a 55.8% reduction with the standard PID controller. This improvement over the passive suspension indicates better actuator force control, which more effectively counteracts road disturbances and reduces vehicle body acceleration. In Fig. 8(b), the RMS value of the roll rate is further improved by 37.6% with PSO optimization, showing enhanced dynamic response compared to the non-optimized PID controller. Although the nonoptimized PID achieves a 62% reduction in roll rate, the additional improvement with PSO helps decrease lateral motion and sway during cornering, improving vehicle stability and safety. Fig. 8(c) presents a 69.4% reduction in pitch rate RMS with PSO optimization, surpassing the 74.6% improvement from the PID controller alone. While PID tuning already improves ride comfort, PSO optimization further reduces roll and pitch, leading to a smoother and more stable ride.TABLE 5PERFORMANCE EVALUATION OF ACTIVE SUSPENSION FOR PID AND PID TUNED BY PSO AT 50KM/HObjective Function RMS VALUEPID PID (PSO) VarianceBody Acceleration (m/s2) 0.0032439 0.0013624 58.0%Roll Rate (rad/s) 0.0003845 0.0002399 62.0%Pitch Rate (rad/s) 0.0002102 0.0000643 69.4%.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7138
This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License, permittingcopy and redistribution of the material and adaptation for commercial and uncommercial use. 9 &RQFOXVLRQThis research has successfully demonstrated the effectiveness of applying a PID control strategy, enhanced by Particle Swarm Optimization (PSO), to improve the ride performance of an active suspension system modeled on a full-car 7-DOF dynamic model. Through rigorous simulation and validation against experimental data, the study confirmed the capability of the optimized controller to significantly reduce body vertical acceleration, roll rate, and pitch rate under various driving conditions. These improvements directly translate to enhanced ride comfort, vehicle stability, and safety, highlighting the potential of optimization techniques in advancing suspension control systems.$FNQRZOHGJHPHQWVThe authors wish to acknowledge the Kolej, Komuniti Kelana Jaya, Jabatan Pendidikan Politeknik and Kolej Komuniti, Ministry of Education, Malaysia and Universiti Teknikal Malaysia Melaka.&RQIOLFWRI,QWHUHVWThe authors declare no conflict of interest in the publication process of the research article.$XWKRU&RQWULEXWLRQVFor research articles with several authors, a short paragraph specifying their individual contributions must be provided. Authorship must be limited to those who have contributed substantially to the work reported. Example : Author 1: Data collection, analysis, writing ñoriginal draft preparation; Author 2: Supervision, draft review and editing, investigation; Author 3: Conceptualization, review; Author 4: Funding acquisition, project administration.5HIHUHQFHV[1]Vu, T. N. L., Dung, D. Van, Trang, N. Van, & Hai, P. T. (2017).Analytical Design of PID Controller for Enhancing Ride Comfortof Active Vehicle Suspension System. Proceedings - 2017International Conference on System Science and Engineering, ICSSE 2017, 305ñ308.https://doi.org/10.1109/ICSSE.2017.8030886[2]Patil, S. A., & Joshi, S. G. (2014). Experimental Analysis of 2 DOFQuarter-Car Passive and Hydraulic Active Suspension Systems forRide Comfort. Systems Science and Control Engineering, 2(1),621ñ631. https://doi.org/10.1080/21642583.2014.913212[3]Rajamani, R., & Maiarutselvan, V. (2021). Development of anActive Suspension System Model Based on Dynamic Performance.Journal of Contemporary Issues in Business and Government,27(6), 4005ñ4017. https://doi.org/10.47750/cibg.2021.27.06.066[4]Yagiz, N., Yuksek, I., & Sivrioglu, S. (2000). Robust Control ofActive Suspensions for a Full Vehicle Model Using Sliding Mode Control. JSME International Journal, 43(2), 101ñ104. https://doi.org/https://doi.org/10.1299/jsmec.43.253[5]Darus, R., & Md. Sam, Y. (2009). Modeling and Control Active Suspension System for A Quarter-Car Model. CSPA 2010 - 2010 International Colloquium on Signal Processing & Its Applications,4(7), 1203ñ1206. https://doi.org/10.1109/CSSR.2010.5773718[6]Das, D. D. D., Kumar, M. S., & Gampa, S. R. (2017). Optimum PIDController Design using PSO for Vehicle Active SuspensionSystem Considering MATLAB Simulink Modeling based RoadProfiles. Journal of Electrical Engineering, July.http://www.jee.ro/index.php/jee/article/view/WW1467987284W577fb5541cf9e%0Ahttp://www.jee.ro/index.php/jee/article/download/WW1467987284W577fb5541cf9e/1521[7]Mohammed, M. H. ., & Bash, A. M. (2018). PID Controller ofQuarter Car Model of Active Suspension System. Journal of Advanced Sciences and Engineering Technologies (JASET), 2(1),1ñ12. https://doi.org/10.32441/jaset.02.01.01[8]Aranza, M. F., Kustija, J., Trisno, B., & Hakim, D. L. (2016).Tunning PID Controller using Particle Swarm OptimizationAlgorithm on Automatic Voltage Regulator System. IOPConference Series: Materials Science and Engineering, 128(1).https://doi.org/10.1088/1757-899X/128/1/012038[9]Tousi, S. M. A., Mostafanasab, A., & Teshnehlab, M. (2020).Design of Self Tuning PID Controller Based on CompetitionalPSO. Conference on Swarm Intelligence and Evolutionary Computation, CSIEC, 2, 22ñ26. https://doi.org/10.1109/CSIEC49655.2020.9237318[10]Zhao, L., Zeng, Z., Wang, Z., & Ji, C. (2021). PID Control of Vehicle Active Suspension Based on Particle Swarm Optimization.Journal of Physics: Conference Series, 1748(3).https://doi.org/10.1088/1742-6596/1748/3/032028[11]Ahmad, F. Bin, Hudha, K., & Jamaluddin, H. (2009). GainScheduling PID Control with Pitch Moment Rejection forReducing Vehicle Dive and Squat. International Journal of VehicleSafety, 4(1), 45ñ83. https://doi.org/10.1504/IJVS.2009.026973[12]Pedro, J. O., Nhlapo, S. M. S., & Mpanza, L. J. (2020). ModelPredictive Control of Half-Car Active Suspension Systems usingParticle Swarm Optimisation. IFAC-PapersOnLine, 53(2), 14438ñ14443. https://doi.org/10.1016/j.ifacol.2020.12.1443[13]Qiao, J., Choi, Y., & Yang, F. (2021). PSO Optimum Control Strategy of 7 Degrees of Freedom Semi-Active Suspensions. Journal of Mechanical Engineering, Automation and Control Systems, 2(2), 98ñ108. https://doi.org/10.21595/jmeacs.2021.22164.219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7139
5HOLDELOLW\\RID&RROLQJ6\\VWHPNorzilahwati Binti Md Noh1, Ahmad Tarmizi Bin Md Nor 2, Nurul Shahira binti Mohd Kassim31, 2 Unit Sijil Teknologi Penyenggaraan Industri, Kolej Komuniti Kelana Jaya, 2, Jln PJS 5/28 B, Commercial City, 46150 Petaling Jaya, Selangor, Malaysia 3, Unit Sijil Teknologi Automatif, Kolej Komuniti Kelana Jaya, 2, Jln PJS 5/28 B, Commercial City, 46150 Petaling Jaya, Selangor, Malaysia $EVWUDFW ñ This study evaluates the Reliability, Availability, and Mean Time to Failure (MTTF)of a Package Air Conditioner with Water-Cooled Condenser by employing a Fault Tree Analysis (FTA) approach. These systems play a critical role in maintaining environmental control in commercial and industrial settings, where uninterrupted operation is essential. Reliability is defined as the probability that the air conditioning system performs its intended function without failure under specified operational conditions for a given period. In this analysis, the system reliability is determined to be 88.7%, indicating a high likelihood of uninterrupted performance over the evaluated time interval. Availability, the probability that the system is functioning at any given point in time, is estimated at 99.8%, which reflects the effectiveness of thesystemís fault recovery mechanisms and maintenance strategies. The failure rate of the system is calculated at 0.1552 failures per year, translating to a Mean Time to Failure (MTTF) of approximately 6.4 years, meaning that on average, the system can operate for 6.4 years before a fault occurs. Using Fault Tree Analysis, the study systematically identifies and models the potential failure modes of critical components, including the compressor, condenser, cooling water pump, control circuitry, and expansion valve. Logical gates are used to trace the propagation of component-level faults to the top-level system failure. Quantitative evaluation is carried out by assigning failure probabilities and using Boolean reduction to determine minimal cut sets and overall system metrics. The results highlight the importance of minimizing system complexity, as excessive complexity can increase Mean Time to Repair (MTTR) and reduce overall system dependability. It is recommended to simplify the system design, incorporate redundancy in essential components to enhance availability through faster recovery, and implement effective failure detection mechanisms. These measures ensure faults can be promptly identified and addressed, thereby improving both operational continuity and long-term system reliability Keywords: Reliability, Availability, Mean Time to Failure (MTTF) of a Package Air Conditioner with Water-Cooled Condenser ,1752'8&7,21)LJXUH shows a system of a package air conditionerwith water cooled type. The water cooled type usually has capacity above 5 tonnes. It has bigger capacitycompared to air cooled type. The water-cooled type can be completely factory assembled, tested and chargedwith refrigerant before being installed in the field. This is advantages because less man power is needed in the field to do the installation hence cost saving. This package air conditioner water cooled type consists of forced air evaporator, compressor, condenser water pump, watercooled condenser and cooling tower. In these packaged air conditioners, the forced air evaporator has shell and tube types. This type is commonly known as chillers. They are classified into two types: dry expansion type and flooded type of chillers. In dry expansion chillers the refrigeratornt flows along the tube side and the fluid to be chilled flows along the shell side. The flow of the refrigerant to these chillers is controlled by the expansion valve.In the packaged units with the water cooled condenser, the compressor is located at the bottom along with the condenser. The function of a compressor is to increase the pressure and corresponding saturation temperature (boiling point) of the refrigerant vapor to high enough level so the refrigerant can condense by rejecting its heat through the condenser. Above these components the evaporator or the cooling coil is located. The filter drier is located above the cooling coil.The condenser is cooled by the water. It is .219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7140
shell and tube type with refrigerant flowing along the tube side and the cooling water flowing along the shell side. Chilled water expel heat through condenser bywater cooled system. It rejects heat into water and recirculated through a cooling tower. The water has to be supplied continuously in these systems to maintain functioning of the air conditioning system )LJXUHffl3DFNDJH$LU&RQGLWLRQHUZLWK:DWHU&RROHG&RQGHQVHU0HWKRGRORJ\\The study was undertaken in the following phases: x System familiarizationx Input data collection and analysisx Construct Fault Tree Analysisx Construct Reliability Block Diagramx Evaluate availability of the system configurationx Report the results 7HUPLQRORJ\\x Reliability - the probability that the entityexperiences no failure under given conditions for agiven time intervalx Availability - The probability that an entity isoperating under given conditions at a given instant oftime1.2.1 Mean Time to Failure (MTTF) - The averageoperating time to failure 5(/,$%,/,7< %/2&. ',$*5$02YHUDOO5HOLDELOLW\\%ORFN'LDJUDP)LJXUH ffl 2YHUDOO 5HOLDELOLW\\ %ORFN 'LDJUDP IRU&RROLQJ 6\\VWHP)LJXUHffl6XE6\\VWHP5HOLDELOLW\\%ORFN'LDJUDPIRU&RROLQJ6\\VWHP )$8/775(($1$/<6,6 )7$ ?A technique by which conditions and factors that can contribute to a specified undesired event are identified and organized in a logical manner and represented pictorially Starting with the undesired (top) event thepossible causes of that event are identified at the next lower level. If each of those contributors could produce the top event alone an OR gate is used; if all the contributors must act to result in the top event an AND gate is used. Then continue to the next level.7KH7RS(YHQWffl/RVVRI&RROLQJThis top event loss of cooling occurs when either one of failure occur, evaporator fault, compressor fault, condenser fault, or cooling tower system fault. These must connect with OR function on the Fault Tree Analysis. An evaporator is used in an air-conditioning system to allow a compressed cooling chemical, such as R-22 (Freon) or R-410A, to evaporate from liquid to gaswhile absorbing heat in the process. The evaporator faultcan occur when forced air evaporator loss of circulation,low pressure control, suction pressure failure ORmalfunction of access valve 1 AND access valve 2..219(16<(179(70$'$1,ffl5(92/86,'$7$'$1,129$6,79(7141