4.8 Article

Improved-Team-Game-Optimization-Algorithm-Based Solar MPPT With Fast Convergence Speed and Fast Response to Load Variations

期刊

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
卷 68, 期 8, 页码 7093-7103

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2020.3001798

关键词

Maximum power point trackers; Games; Optimization; Load management; Tuning; Convergence; Standards; Global maximum power point; maximum power point tracking (MPPT); metaheuristic algorithm; partial-shading condition; team game optimization algorithm (TGA)

资金

  1. University of Malaya Impact Oriented Interdisciplinary Research Grant (IIRG) [IIRG011C-2019]
  2. Fundamental Research Grant Scheme under UM Grant [FP095-2018A, FRGS/1/2018/TK07/UM/02/4]
  3. NICOP research [N62909-18-1-2030, IF011-2018]

向作者/读者索取更多资源

A new metaheuristic approach has been proposed to improve the performance of the maximum power point tracking (MPPT) system, with faster response time and higher efficiency under load variation conditions.
Maximum power point tracking (MPPT) is one of the crucial components to ensure that the photovoltaic (PV) system operates optimally. The bypass diodes are added across series-connected PV modules to avoid the hotspot phenomenon, which resulted in multiple peaks on the power-voltage curve during partial-shading conditions. In this article, a new metaheuristic approach, namely improved team game optimization algorithm, has been proposed. Only one tuning parameter is required for the proposed algorithm, and a new approach has been introduced to increase the convergence speed. Apart from that, a constant current method has been proposed during load variation conditions, which improved the response of the system toward load changes by 78.26%. The experimental results showed that the convergence speed of the proposed method is 72.5% faster than the standard team game optimization algorithm. The proposed method is also validated under different shading conditions and proven to have an average MPPT efficiency of 99.78% and an average tracking time of 0.9 s. The comparison between the proposed method and different metaheuristic approaches was also carried out based on grade point average, and it showed the effectiveness of the proposed algorithm with a higher number of features.

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