文章基本信息

标题：Bio-inspired algorithms based on maximum power point tracking control for PMSG wind power generation systems
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作者：Tuan Ngoc Anh Nguyen ; Duy Cong Pham ; Luu Hoang Minh 等
期刊名称：Journal of Electrical Systems
印刷版ISSN：1112-5209
出版年度：2022
卷号：18
期号：2
页码：260-271
语种：English
出版社：ESRGroups
摘要：In this paper, a studying the performance of three optimization techniques include genetic algorithm, particle swarm optimization and differential evolution algorithm. They are designed to extract the maximum power point tracking and suggested in permanent magnet synchronous generator wind energy systems under randomly variable wind speed cases. The performance of the three algorithms is studied, assessed, and compared using key characteristics such as turbine power coefficient, convergence time, standard deviation, reliability, and turbine power under the same operating conditions. The tracking performances based on the three algorithms are assessed using MATLAB software. The results show that the differential evolution algorithm has a convergence to the global maximum power point that better solution quality while particle swarm optimization has a faster execution time in comparison with the genetic algorithm for solving the maximum power point tracking.
关键词：Permanent magnet synchronous generator;maximum power point tracking;genetic algorithm;particle swarm optimization;differential evolution