研究目的
To propose an Improved Hybrid Simulated Annealing and Particle Swarm Optimization (ISA-PSO) for maximum power point tracking (MPPT) in photovoltaic generation systems, addressing the shortcomings of PSO slow convergence and tendency to fall into local optimal solutions.
研究成果
The ISA-PSO algorithm demonstrates superior efficiency and faster tracking times compared to PSO, SA, and SA-PSO under various simulated conditions, making it a promising approach for MPPT in photovoltaic systems.
研究不足
The study is based on simulations using MATLAB, and real-world application results may vary. The performance under more extreme or varied environmental conditions was not explored.
1：Experimental Design and Method Selection:
The study combines Simulated Annealing (SA) and Particle Swarm Optimization (PSO) to form ISA-PSO, utilizing SA's escaping mechanism to enhance PSO's performance. MATLAB is used for simulation.
2：Sample Selection and Data Sources:
Simulations are conducted under single peak, partial shading double peak, temperature changing, and illumination changing conditions.
3：List of Experimental Equipment and Materials:
MATLAB software for simulation.
4：Experimental Procedures and Operational Workflow:
The ISA-PSO algorithm is applied to MPPT in photovoltaic systems, with performance compared against PSO, SA, and SA-PSO under various conditions.
5：Data Analysis Methods:
Efficiency and tracking speed are measured and compared across different algorithms under simulated conditions.
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