关键词: 粒子群算法, 人工鱼群算法, 行为模式, 光伏, 最大功率点跟踪

Abstract: Introducing the velocity inertia, memory capacity of each individual and learning or communicating capacity of Particle Swarm Optimization (PSO) into the Artificial Fish-Swarm Algorithm (AFSA), a new algorithm called the “Fish-Swarm Algorithm optimized by PSO(PSO-FSA)” was put forward. In this new algorithm, the swimming of each fish has velocity inertia, and the PSO-FSA has totally five kinds of behavior pattern as follows: swarming, following, remembering, communicating and searching. The simulation analysis shows that PSO-FSA has more stable and higher performance in convergence speed and searching precision than PSO and AFSA. Finally, the PSO-FSA was applied to the maximum power point tracking of photovoltaic power generation system under partially shaded condition, and the experimental results show that PSO-FSA can find the maximum power point under partially shaded insolation conditions quickly and precisely.

Key words: Particle Swarm Optimization (PSO), Artificial Fish Swarm Algorithm (AFSA), human activities, photovoltaic, maximum power point tracking