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Combining Model-based and Heuristic Techniques for Fast Tracking the Global Maximum Power Point of a Photovoltaic String

  • Shi, Ji-Ying (School of Electrical and Automation Engineering, Tianjin University) ;
  • Xue, Fei (Electric Power Research Institute, State Grid Ningxia Electric Power Company (NEPC)) ;
  • Ling, Le-Tao (School of Electrical and Automation Engineering, Tianjin University) ;
  • Li, Xiao-Fei (State Grid Electric Power Research Institute) ;
  • Qin, Zi-Jian (School of Electrical and Automation Engineering, Tianjin University) ;
  • Li, Ya-Jing (School of Electrical and Automation Engineering, Tianjin University) ;
  • Yang, Ting (School of Electrical and Automation Engineering, Tianjin University)
  • Received : 2016.03.17
  • Accepted : 2016.12.03
  • Published : 2017.03.20

Abstract

Under partial shading conditions (PSCs), multiple maximums may be exhibited on the P-U curve of string inverter photovoltaic (PV) systems. Under such conditions, heuristic methods are invalid for extracting a global maximum power point (GMPP); intelligent algorithms are time-consuming; and model-based methods are complex and costly. To overcome these shortcomings, a novel hybrid MPPT (MPF-IP&O) based on a model-based peak forecasting (MPF) method and an improved perturbation and observation (IP&O) method is proposed. The MPF considers the influence of temperature and does not require solar radiation measurements. In addition, it can forecast all of the peak values of the PV string without complex computation under PSCs, and it can determine the candidate GMPP after a comparison. Hence, the MPF narrows the searching range tremendously and accelerates the convergence to the GMPP. Additionally, the IP&O with a successive approximation strategy searches for the real GMPP in the neighborhood of the candidate one, which can significantly enhance the tracking efficiency. Finally, simulation and experiment results show that the proposed method has a higher tracking speed and accuracy than the perturbation and observation (P&O) and particle swarm optimization (PSO) methods under PSCs.

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