研究目的
To present an Inertia Modified Particle Swarm Optimization (IM-PSO) algorithm for photovoltaic power extraction to track the maximum power point efficiently under varying environmental conditions and shading effects.
研究成果
The IM-PSO algorithm successfully enhances the optimization ability of basic PSO by improving population diversity and achieving a balance between exploration and exploitation. It demonstrates superior performance in solving multimodal and high dimensionality problems under partial shading conditions, as evidenced by simulation results.
研究不足
The study is based on simulations in MATLAB/SIMULINK environment; real-world application and performance under more diverse conditions are not explored.
1：Experimental Design and Method Selection:
The study employs an Inertia Modified Particle Swarm Optimization (IM-PSO) algorithm for MPPT in photovoltaic systems under partial shading conditions.
2：Sample Selection and Data Sources:
Four 250 Watts panels connected in series to a boost converter under varying irradiance conditions (500 W/m2, 800 W/m2, 1000 W/m2, and 1000 W/m2).
3：2). List of Experimental Equipment and Materials:
3. List of Experimental Equipment and Materials: MATLAB/SIMULINK environment for simulation.
4：Experimental Procedures and Operational Workflow:
The duty cycle of the boost converter is varied periodically using the IM-PSO Algorithm to track the maximum power point.
5：Data Analysis Methods:
Comparison of the proposed IM-PSO method with basic PSO in terms of exploration and exploitation properties, convergence speed, and stability.
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