引用本文: | 王福忠,陶新坤,田广强.基于改进果蝇算法优化的微电网逆变器恒功率控制算法[J].电力系统保护与控制,2021,49(21):71-79.[点击复制] |
WANG Fuzhong,TAO Xinkun,TIAN Guangqian.Constant power control algorithm for a microgrid inverter based on an improved fruit fly algorithm[J].Power System Protection and Control,2021,49(21):71-79[点击复制] |
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摘要: |
针对微电网逆变器恒功率控制器PI参数优化的问题,提出了基于种群分区的多策略自适应果蝇(ppmaFOA)优化的PI参数在线优化算法。根据适应度值进行排序将果蝇种群分为三个区,因每个区果蝇性能的差异提出多策略更新模式:I区局部精细搜索保证种群不退化,II区根据算法所处环境自适应调节算法的多样性和收敛性,III区引导果蝇加速收敛,提高了算法的开发和探索能力。采用5种对比算法,利用微电网恒功率控制系统进行实验,验证了所提算法的性能和经ppmaFOA算法优化后逆变器的响应速度更快、误差更小、输出更稳定。 |
关键词: PQ控制 PI参数 在线优化 改进果蝇算法 多策略 自适应 |
DOI:DOI: 10.19783/j.cnki.pspc.210154 |
投稿时间:2021-02-05修订日期:2021-02-05 |
基金项目:国家自然科学基金项目资助(U1804143);河南省科技攻关项目资助(212102210146) |
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Constant power control algorithm for a microgrid inverter based on an improved fruit fly algorithm |
WANG Fuzhong,TAO Xinkun,TIAN Guangqian |
(1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454000, China;
2. School of Intelligent Engineering, Yellow River Transportation Institute, Jiaozuo 454950, China) |
Abstract: |
To optimize PI parameters of the constant power controller of a microgrid inverter, an online optimization algorithm of PI parameters based on the multi-strategy adaptive fruit fly (ppmaFOA) optimization, which is itself based on population partition is proposed. According to the fitness value, the fruit fly population is divided into three areas, and a multi-strategy update mode is proposed because of the difference in the performance of the fruit flies in each area: local fine search in area I ensures that the population does not degenerate, area II adjusts the diversity and convergence of the algorithm adaptively according to the environment in which the algorithm is located, and area III guides fruit flies to accelerate convergence. All this improves the development and exploration capabilities of the algorithm. An experiment uses five comparison algorithms and uses the micro-grid constant power control system to perform experiments to verify the performance of the proposed algorithm. The inverter response speed after optimization by the ppmaFOA algorithm is faster, the error smaller, and the output more stable.
This work is supported by the National Natural Science Foundation of China (No. U1804143) and the Science and Technology Key Project of Henan Province (No. 212102210146). |
Key words: PQ control PI parameters online optimization improved Drosophila algorithm multi-strategy adaptive |