DMPPT Control of Photovoltaic Microgrid Based on Improved Sparrow Search Algorithm

• Published in: IEEE access Vol. 9; pp. 16623 - 16629
• Main Authors: Yuan, Jianhua, Zhao, Ziwei, Liu, Yaping, He, Baolin, Wang, Lin, Xie, Binbin, Gao, Yanling
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Center of gravity; Distributed generation; Distributed maximum power point tracking; Electric power distribution; Extreme values; Irradiance; Machine learning; Maximum power tracking; Microgrids; Multichip modules; Mutation; Particle swarm optimization; Perturbation; photovoltaic microgrid; Photovoltaic systems; Power supplies; Search algorithms; Shading; Sociology; Solar power generation; sparrow search algorithm; spatial solution distribution; Statistics; steady-state
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2021.3052960

There are some problems in the photovoltaic microgrid system due to the solar irradiance-change environment, such as power fluctuation, which leads to larger power imbalance and affects the stable operation of the microgrid. Aiming at the problems of power mismatch loss under partial shading in photovoltaic microgrid systems, this paper proposed a distributed maximum power point tracking (DMPPT) approach based on an improved sparrow search algorithm (ISSA). First, used the center of gravity reverse learning mechanism to initialize the population, so that the population has a better spatial solution distribution; Secondly, the learning coefficient was introduced in the location update part of the discoverer to improve the global search ability of the algorithm; Simultaneously used the mutation operator to improve the position update of the joiner and avoid the algorithm falling into the local extreme value. The results of the model in Matlab showed that the ISSA can track the maximum power point(MPP) more accurately and quickly than the perturbation observation method (P&O) and the particle swarm optimization (PSO) algorithm, and had good steady-state performance.