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Dual-Algorithm Maximum Power Point Tracking Control Method for Photovoltaic Systems based on Grey Wolf Optimization and Golden-Section Optimization
Ji-Ying Shi, Deng-Yu Zhang, Le-Tao Ling, Fei Xue, Ya-Jing Li, Zi-Jian Qin, and Ting Yang
Abstract This paper presents a dual-algorithm search method (GWO-GSO) combining grey wolf optimization (GWO) and golden-section optimization (GSO) to realize maximum power point tracking (MPPT) for photovoltaic (PV) systems. First, a modified grey wolf optimization (MGWO) is activated for the global search. In conventional GWO, wolf leaders possess the same impact on decision-making. In this paper, the decision weights of wolf leaders are automatically adjusted with hunting progression, which is conducive to accelerating hunting. At the later stage, the algorithm is switched to GSO for the local search, which play a critical role in avoiding unnecessary search and reducing the tracking time. Additionally, a novel restart judgment based on the quasi-slope of the power-voltage curve is introduced to enhance the reliability of MPPT systems. Simulation and experiment results demonstrate that the proposed algorithm can track the global maximum power point (MPP) swiftly and reliably with higher accuracy under various conditions.
Keyword Golden-section optimization, Maximum power point tracking, Modified grey wolf optimization, Partial shading conditions, Photovoltaic systems
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