Journal of Power Electronics

The Korean Institute of Power Electronics (전력전자학회)
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Dual-Algorithm Maximum Power Point Tracking Control Method for Photovoltaic Systems based on Grey Wolf Optimization and Golden-Section Optimization

  • Shi, Ji-Ying (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Zhang, Deng-Yu (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Ling, Le-Tao (Shenzhen Power Supply Bureau, China Southern Power Grid) ;
  • Xue, Fei (Electric Power Research Institute, State Grid Ningxia Electric Power Company) ;
  • Li, Ya-Jing (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University) ;
  • Qin, Zi-Jian (Laiwu Power Supply Bureau, State Grid Shandong Electric Company) ;
  • Yang, Ting (Key Laboratory of Smart Grid of Ministry of Education, Tianjin University)
  • Received : 2017.09.28
  • Accepted : 2018.01.16
  • Published : 2018.05.20
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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.

Keywords

References

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