2022 Volume 19 Issue 12 Pages 20220165
When the photovoltaic system is put into use, some natural objects will cause partial shadows on the photovoltaic modules. And in this case, the output characteristics of the photovoltaic array will change from the original “single peak” to “multi-peak”, and the power of the photovoltaic array will have multiple extreme values, which will increase the difficulty of tracking the maximum power of the photovoltaic array. The traditional maximum power point tracking (MPPT) algorithm is no longer applicable. Most of them fall into the local maximum power and cannot find the global maximum power. Although the particle swarm optimization algorithm (PSO) has a certain ability to solve the global optimization problem, the standard particle swarm optimization algorithm can’t be regarded as a complete global optimization algorithm. Since the power output curve of the photovoltaic array is severely non-linear in the shaded situation, the standard particle swarm optimization algorithm may also fall into a local optimum and fail to find the global optimum. Compared with the standard particle swarm optimization algorithm, the particle swarm optimization algorithm with time-varying compression factor proposed in this paper can better balance the relationship between global search and local search, and can effectively avoid falling into the local optimal value and not finding it. To the correct maximum power point, while also increasing the speed of convergence. Comparing the method proposed in this paper with the standard particle swarm algorithm through experiments, the results show that the method proposed in this paper is of great significance for improving the efficiency of photovoltaic systems under partial shadow conditions.