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Simulation and verification of improved particle swarm optimization for maximum power point tracking in photovoltaic systems under dynamic environmental conditions

International Journal of Power Electronics and Drive Systems · Vol. 16 No. 1 (2025) · hlm. 608–621

DOI
10.11591/ijpeds.v16.i1.pp608-621
ISSN
2722-256X, 2088-8694
Terbit
2025-03-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

This paper introduces an improved particle swarm optimization (iPSO) algorithm designed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The proposed algorithm incorporates a novel reinitialization mechanism that dynamically detects and adapts to environmental changes. Additionally, an exponentially decreasing inertia weight is utilized to balance exploration and exploitation, ensuring rapid convergence to the global maximum power point (GMPP). A deterministic initialization strategy is employed to uniformly distribute particles across the search space, thereby increasing the likelihood of identifying the GMPP. The iPSO algorithm is thoroughly evaluated using a MATLAB/Simulink simulation and validated with real-time hardware, including a boost DC-DC converter, dSPACE, and a Chroma PV simulator. Comparative analysis with conventional PSO and PSO-reinit algorithms under various irradiance patterns demonstrates that the iPSO consistently outperforms in terms of convergence speed and MPPT efficiency. The study highlights the robustness of the iPSO algorithm in bridging theoretical models with practical applications.

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  • Universiti Teknologi MARA
  • University of Malaya
  • Universiti Putra Malaysia
  • University of Technology Malaysia
  • Universiti Teknikal Malaysia Melaka
  • University of Technology Sydney
  • Tun Hussein Onn University of Malaysia
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Rekan penulisOrganisasiArtikel bersama
Wan Muhamad Hakimi Wan BunyaminUniversiti Teknologi MARA7
Ismail MusirinUniversiti Teknologi MARA7
Khairul Safuan MuhammadUniversiti Teknologi MARA6
Wan Noraishah Wan Abdul MunimUniversiti Teknologi MARA5
Dalina JohariUniversiti Teknologi MARA5
Nor Farahaida Abdul RahmanUniversiti Teknologi MARA5
Ahmad Farid AbidinUniversiti Teknologi MARA5
Nik Fasdi Nik IsmailUniversiti Teknologi MARA3
Naeem M. S. HannoonUniversiti Teknologi MARA3
Samshul Munir MuhamadUniversiti Teknologi MARA2
Zulkiffli Abdul HamidUniversiti Teknologi MARA2
Siti Zaliha Mohammad NoorUniversiti Teknologi MARA2
Mohd Shukri Mohd GhazaliUniversiti Teknologi MARA2
Mohammad Lutfi OthmanUniversiti Putra Malaysia2
Mohammad Nawawi SerojiUniversiti Teknologi MARA2
Shahril Irwan SulaimanUniversiti Teknologi MARA2
Zainal SalamUniversity of Technology Malaysia2
Muhammad Shawwal Mohamad RawiUniversiti Teknologi MARA2
Nabil M. HidayatUniversiti Teknologi MARA2
Muhamad Nabil Bin HidayatUniversiti Teknologi MARA2
Azhan Ab RahmanUniversiti Teknikal Malaysia Melaka1
Wan Salha SaidonUniversiti Teknologi MARA1
Ammar Yasier AzmanUniversiti Teknologi MARA1
Muhammad Alif MansorUniversiti Teknologi MARA1
Dylan Dah‐Chuan LuUniversity of Technology Sydney1
Muhamad Amirul Naim Mohd JamaluddinUniversiti Teknologi MARA1
Faisal FauziUniversiti Teknologi MARA1
Hang SengUniversity of Malaya1
Nooradzianie Muhammad ZinTun Hussein Onn University of Malaysia1
Mohamad Fathi Mohamad EliasUniversity of Malaya1
Muhammad Amirul Ashraf Zarrul HayatAfiliasi tidak diketahui1
Kamrul HasanUniversiti Teknologi MARA1
M. A. HannanUniversiti Tenaga Nasional1
Mohammad Fazrul Ashraf Mohd FazilUniversiti Teknologi MARA1
Mohd Hafez Hilmi HarunUniversiti Teknologi MARA1
Muhammad Hasbi AzmiUniversiti Teknologi MARA1

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Sumber metadata: OAI-PMH (metadata jurnal) http://ijpeds.iaescore.com/index.php/IJPEDS/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
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	<dc:title xml:lang="en-US">Simulation and verification of improved particle swarm optimization for maximum power point tracking in photovoltaic systems under dynamic environmental conditions</dc:title>
	<dc:creator>Mohd Jamhari, Muhammad Khairul Azman</dc:creator>
	<dc:creator>Hashim, Norazlan</dc:creator>
	<dc:creator>Baharom, Rahimi</dc:creator>
	<dc:creator>Othman, Muhammad Murtadha</dc:creator>
	<dc:subject xml:lang="en-US">artificial intelligence algorithm; MATLAB/Simulink; maximum power point tracking; particle swarm optimization; PV system</dc:subject>
	<dc:description xml:lang="en-US">This paper introduces an improved particle swarm optimization (iPSO) algorithm designed for maximum power point tracking (MPPT) in photovoltaic (PV) systems. The proposed algorithm incorporates a novel reinitialization mechanism that dynamically detects and adapts to environmental changes. Additionally, an exponentially decreasing inertia weight is utilized to balance exploration and exploitation, ensuring rapid convergence to the global maximum power point (GMPP). A deterministic initialization strategy is employed to uniformly distribute particles across the search space, thereby increasing the likelihood of identifying the GMPP. The iPSO algorithm is thoroughly evaluated using a MATLAB/Simulink simulation and validated with real-time hardware, including a boost DC-DC converter, dSPACE, and a Chroma PV simulator. Comparative analysis with conventional PSO and PSO-reinit algorithms under various irradiance patterns demonstrates that the iPSO consistently outperforms in terms of convergence speed and MPPT efficiency. The study highlights the robustness of the iPSO algorithm in bridging theoretical models with practical applications.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2025-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
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	<dc:identifier>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/23626</dc:identifier>
	<dc:identifier>10.11591/ijpeds.v16.i1.pp608-621</dc:identifier>
	<dc:source xml:lang="en-US">International Journal of Power Electronics and Drive Systems (IJPEDS); Vol 16, No 1: March 2025; 608-621</dc:source>
	<dc:source>2722-256X</dc:source>
	<dc:source>2088-8694</dc:source>
	<dc:source>10.11591/ijpeds.v16.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijpeds.iaescore.com/index.php/IJPEDS/article/view/23626/14884</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2025 Norazlan Hashim, Muhammad Khairul Azman Mohd Jamhari, Rahimi Baharom</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>