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Enhanced MPPT for Solar PV Systems under Non-Uniform Irradiance Using Improved PSO Algorithm.

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Název: Enhanced MPPT for Solar PV Systems under Non-Uniform Irradiance Using Improved PSO Algorithm.
Autoři: Ansari, Akhtar Saleem, Kidwai, Mohd. Suhaib, Husain, Mohammed Aslam, Sharma, Yogendra Kumar, Kumar, Navneet, Khan, Ahmad Neyaz
Zdroj: Journal of Engineering Science & Technology Review; 2025, Vol. 18 Issue 5, p177-187, 11p
Témata: PHOTOVOLTAIC power generation, PARTICLE swarm optimization, MAXIMUM power point trackers, ENERGY harvesting, SMART power grids, SUNSHINE, OPTIMIZATION algorithms
Abstrakt: This paper introduces an Improved Particle Swarm Optimization (I-PSO)-based Maximum Power Point Tracking (MPPT) algorithm to maximize photovoltaic (PV) power harvesting under Partial Shading Conditions (PSC). Conventional MPPT methods are unable to locate the global peak (GP) because of the presence of multiple peaks in the power-voltage (P-V) curve under shading. Artificial Intelligence-based methods such as PSO present a powerful solution for maximizing power extraction. The I-PSO method proposed here overcomes this difficulty, with faster convergence and better tracking accuracy. Simulations were performed in Proteus Design Suite and MATLAB/Simulink, supported by hardware verification on a low-cost microcontroller-based circuit. The I-PSO converged to 0.19 seconds in Proteus, 0.17 seconds in MATLAB/Simulink, and arrived at the GP within about 1.1 seconds in hardware experiments. Comparative studies showed that I-PSO performed better than Conventional PSO and Perturb & Observe (P&O) methods, having the maximum efficiency with minimum steady-state error and quicker tracking time. Under dynamic shading conditions, I-PSO showed greater robustness and responsiveness. With slightly higher computational complexity, the improved accuracy and efficiency of the method make it a potential solution for real-world PV systems. Algorithm optimization for large-scale deployment and integration into grid-connected PV systems will be the focus of future work. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:This paper introduces an Improved Particle Swarm Optimization (I-PSO)-based Maximum Power Point Tracking (MPPT) algorithm to maximize photovoltaic (PV) power harvesting under Partial Shading Conditions (PSC). Conventional MPPT methods are unable to locate the global peak (GP) because of the presence of multiple peaks in the power-voltage (P-V) curve under shading. Artificial Intelligence-based methods such as PSO present a powerful solution for maximizing power extraction. The I-PSO method proposed here overcomes this difficulty, with faster convergence and better tracking accuracy. Simulations were performed in Proteus Design Suite and MATLAB/Simulink, supported by hardware verification on a low-cost microcontroller-based circuit. The I-PSO converged to 0.19 seconds in Proteus, 0.17 seconds in MATLAB/Simulink, and arrived at the GP within about 1.1 seconds in hardware experiments. Comparative studies showed that I-PSO performed better than Conventional PSO and Perturb & Observe (P&O) methods, having the maximum efficiency with minimum steady-state error and quicker tracking time. Under dynamic shading conditions, I-PSO showed greater robustness and responsiveness. With slightly higher computational complexity, the improved accuracy and efficiency of the method make it a potential solution for real-world PV systems. Algorithm optimization for large-scale deployment and integration into grid-connected PV systems will be the focus of future work. [ABSTRACT FROM AUTHOR]
ISSN:17912377
DOI:10.25103/jestr.185.17