Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies
The rising demand for clean energy has intensified research into solar photovoltaic (PV) systems, where efficient Maximum Power Point Tracking (MPPT) is critical to optimizing energy extraction. However, conventional MPPT techniques, such as Perturb and Observe (P&O), often suffer from power los...
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Elsevier
2025-09-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025024983 |
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| author | Denesh Sooriamoorthy Aaruththiran Manoharan Siva Kumar Sivanesan Soon Kian Lun Alexander Chee Hon Cheong Sathish Kumar Selva Perumal |
| author_facet | Denesh Sooriamoorthy Aaruththiran Manoharan Siva Kumar Sivanesan Soon Kian Lun Alexander Chee Hon Cheong Sathish Kumar Selva Perumal |
| author_sort | Denesh Sooriamoorthy |
| collection | DOAJ |
| description | The rising demand for clean energy has intensified research into solar photovoltaic (PV) systems, where efficient Maximum Power Point Tracking (MPPT) is critical to optimizing energy extraction. However, conventional MPPT techniques, such as Perturb and Observe (P&O), often suffer from power losses, slow convergence, and poor performance under partial shading conditions (PSC). Metaheuristic algorithms such as Particle Swarm Optimization (PSO) are explored extensively for MPPT applications. Various adaptive cognitive and social coefficients (c1 and c2), and inertia weight (w) strategies have been proposed in the literature for improved dynamic response and tracking precision. This paper presents a comparative analysis to determine suitable adaptive c1, c2 and w strategies for improved tracking speed, accuracy, and stability of PSO-based MPPT under uniform and partial shading conditions reflecting real world scenarios. Using a 4 × 5 PV array model simulated in MATLAB/Simulink, different adaptive PSO variants i.e. linear, exponential, and performance-based adaptive w along with linear and trigonometric-based adaptive c1 and c2 are evaluated across diverse irradiance scenarios. The individual and combined performance of adaptive w, c1 and c2 are evaluated, especially with small and narrow w operational range studied as it contributes to high convergence, especially under fast-changing shading patterns. The results demonstrate that linear adaptive w combined with trigonometric adaptive c1 and c2 consistently achieves high tracking accuracy (99.4%) with minimal steady-state oscillations and faster convergence times (average 0.0642 s), outperforming conventional PSO and P&O algorithms. These findings highlight the efficacy of finely-tuned adaptive PSO in enhancing the reliability and efficiency of MPPT in real-world solar applications. |
| format | Article |
| id | doaj-art-2d55cd71d511445c9a22c420ca12846e |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-2d55cd71d511445c9a22c420ca12846e2025-08-20T03:56:41ZengElsevierResults in Engineering2590-12302025-09-012710642910.1016/j.rineng.2025.106429Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategiesDenesh Sooriamoorthy0Aaruththiran Manoharan1Siva Kumar Sivanesan2Soon Kian Lun3Alexander Chee Hon Cheong4Sathish Kumar Selva Perumal5School of Engineering, Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000, Kuala Lumpur, Malaysia; Corresponding author.Newcastle University in Singapore, SingaporeSchool of Engineering, Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000, Kuala Lumpur, MalaysiaSchool of Engineering, Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000, Kuala Lumpur, MalaysiaSchool of Engineering, Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000, Kuala Lumpur, MalaysiaSchool of Engineering, Asia Pacific University, Jalan Teknologi 5, Taman Teknologi Malaysia, 57000, Kuala Lumpur, MalaysiaThe rising demand for clean energy has intensified research into solar photovoltaic (PV) systems, where efficient Maximum Power Point Tracking (MPPT) is critical to optimizing energy extraction. However, conventional MPPT techniques, such as Perturb and Observe (P&O), often suffer from power losses, slow convergence, and poor performance under partial shading conditions (PSC). Metaheuristic algorithms such as Particle Swarm Optimization (PSO) are explored extensively for MPPT applications. Various adaptive cognitive and social coefficients (c1 and c2), and inertia weight (w) strategies have been proposed in the literature for improved dynamic response and tracking precision. This paper presents a comparative analysis to determine suitable adaptive c1, c2 and w strategies for improved tracking speed, accuracy, and stability of PSO-based MPPT under uniform and partial shading conditions reflecting real world scenarios. Using a 4 × 5 PV array model simulated in MATLAB/Simulink, different adaptive PSO variants i.e. linear, exponential, and performance-based adaptive w along with linear and trigonometric-based adaptive c1 and c2 are evaluated across diverse irradiance scenarios. The individual and combined performance of adaptive w, c1 and c2 are evaluated, especially with small and narrow w operational range studied as it contributes to high convergence, especially under fast-changing shading patterns. The results demonstrate that linear adaptive w combined with trigonometric adaptive c1 and c2 consistently achieves high tracking accuracy (99.4%) with minimal steady-state oscillations and faster convergence times (average 0.0642 s), outperforming conventional PSO and P&O algorithms. These findings highlight the efficacy of finely-tuned adaptive PSO in enhancing the reliability and efficiency of MPPT in real-world solar applications.http://www.sciencedirect.com/science/article/pii/S2590123025024983Solar PV systemsMaximum power point trackingParticle swarm optimizationPartial shading |
| spellingShingle | Denesh Sooriamoorthy Aaruththiran Manoharan Siva Kumar Sivanesan Soon Kian Lun Alexander Chee Hon Cheong Sathish Kumar Selva Perumal Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies Results in Engineering Solar PV systems Maximum power point tracking Particle swarm optimization Partial shading |
| title | Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies |
| title_full | Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies |
| title_fullStr | Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies |
| title_full_unstemmed | Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies |
| title_short | Optimizing solar maximum power point tracking with adaptive PSO: A comparative analysis of inertia weight and acceleration coefficient strategies |
| title_sort | optimizing solar maximum power point tracking with adaptive pso a comparative analysis of inertia weight and acceleration coefficient strategies |
| topic | Solar PV systems Maximum power point tracking Particle swarm optimization Partial shading |
| url | http://www.sciencedirect.com/science/article/pii/S2590123025024983 |
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