Photovoltaic MPPT Control Based on Improved Cuckoo Search Algorithm
In the case of complex shadows,the P-V characteristic curve of the photovoltaic array will have multi-ple peaks.The traditional MPPT algorithm cannot accurately identify the local peak and the global peak,so it is una-ble to track the maximum power point in the case of complex shadows.This paper proposes a multi-strategy improved cuckoo search(EGICS)algorithm to solve the problem that the traditional cuckoo search(CS)algorithm may fall in-to a local optimum due to the lack of communication between nests.The selection of the discovery probability value in the traditional CS algorithm is adaptively changed to improve the search ability of the algorithm.The step factor is a-daptive to improve the convergence speed of the algorithm.Gaussian perturbation and elite reverse learning strategy are introduced to increase the diversity of the population and avoid the algorithm falling into the local optimum.The performance of the EGICS algorithm is tested in single-peak and multi-peak functions,and it is applied to the MPPT control of PV systems for simulation verification.The simulation results show that the EGICS algorithm has better effects in three aspects:convergence speed,tracking accuracy and dynamic stability.
Partial shadowMaximum power point trackingCuckoo search algorithmGaussian perturbationElite reverse learning
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