Maximum power point tracking strategy for photovoltaic system based on fuzzy information diffusion under partial shading conditions

Abstract

Under stochastic partial shading conditions, the photovoltaic power system often produces a non-convex characteristic curve. It makes the maximum power point tracking a difficult task. In this paper, based on fuzzy information diffusion, a novel probability algorithm is proposed to track the global maximum power point (GMPP) under partial shading conditions. By uniform incomplete sampling and information diffusion, the proposed method can avoid the random searching process of intelligence algorithms, which will effectively improve the tracking speed and global search capability. From the perspective of probability estimation, the information diffusion function is used to fill the information gap caused by incomplete sampling data, thus transforming the discrete sampling points of PV system into a continuous distribution function. According to the fuzzy probability, one or more discrete intervals are selected to estimate the real value of GMPP. At last, the variable step-size P&O method is adopted to get a more accurate result in these selected intervals. In fact, the information diffusion is a global fuzzy probability estimation algorithm. It has a good global search characteristic. The comprehensive simulations and experiments have fully demonstrated the efficacy and applicability of the proposed method in this paper.