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Modeling and global MPPT for PV system under partial shading conditions using modified artificial fish swarm algorithm

Due to the non-linear characteristics I-V of the photovoltaic (PV) curve, the tracking of the maximum power point (MPP) under partial shading conditions (PSCs) can sometimes be a challenging task. This paper presents a global MPPT (GMPPT) technique for PV system under PSCs using modified artificial fish swarm algorithm (MAFSA). In MAFSA, Firstly, this algorithm introduce the velocity inertia, memory capacity of each individual and learning or communicating capacity of PSO into the AFSA, as a result, the MAFSA has totally five kinds of behavior pattern as follows: swarming, following, remembering, communicating and searching. Furthermore, according to the average distance between each artificial fish and other five artificial fishes in the neighborhood, visual and step of each artificial fish are adaptively calculated before each iteration to improve the convergence of AFSA. Combining the searching capabilities of the PSO and the self-learning ability of adaptive visual and step for AFSA, the GMPPT technique based on MAFSA is developed. To validate the effectiveness of the novel GMPPT technique, the PV system under PSCs along with the proposed technique is simulated using Matlab/Simulink simscape tool box. Experimental results show that the proposed technique outperforms the other methods for GMPPT in PV system under PSCs.