A Deterministic Particle Swarm Optimization Maximum Power Point Tracker for Photovoltaic System Under Partial Shading Condition

摘要

This paper proposes a deterministic particle swarm optimization to improve the maximum power point tracking (MPPT) capability for photovoltaic system under partial shading condition. The main idea is to remove the random number in the accelerations factor of the conventional PSO velocity equation. Additionally, the maximum change in velocity is restricted to a particular value, which is determined based on the critical study of $P$–$V$ characteristics during partial shading. Advantages of the method include: 1) consistent solution is achieved despite a small number of particles, 2) only one parameter, i.e., the inertia weight, needs to be tuned, and 3) the MPPT structure is much simpler compared to the conventional PSO. To evaluate the idea, the algorithm is implemented on a buck-boost converter and compared to the conventional hill climbing (HC) MPPT method. Simulation results indicate that the proposed method outperforms the HC method in terms of global peak tracking speed and accuracy under various partial shading conditions. Furthermore, it is tested using the measured data of a tropical cloudy day, which includes rapid movement of the passing clouds and partial shading. Despite the wide fluctuations in array power, the average efficiency for the 10-h test profile reaches 99.5%.