| 引用本文: | 张 丽,刘雨航,贾晨豪,等.基于多分区自适应改进果蝇算法的风光燃储微电网协调控制策略[J].电力系统保护与控制,2023,51(22):13-23. |
| ZHANG Li,LIU Yuhang,JIA Chenhao,et al.A coordinated control strategy for a wind/ photovoltaic/ gas turbine/ storage microgrid based on a multi-partition adaptive improved fruit fly algorithm[J].Power System Protection and Control,2023,51(22):13-23 |
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| 摘要: |
| 为了使微电网控制系统中PI控制器的参数能够更好地适应可再生能源的随机性和波动性，提出了基于自适应步长的四分区多策略果蝇优化算法(fruit fly optimization algorithm, FOA)对PI参数进行实时优化。首先，以风光燃储微电网不同微源控制系统中的变换器为控制对象，建立微电网整体控制系统模型，基于此模型实时调整PI参数。然后，根据不同果蝇个体的适应度值将果蝇种群分为4个区，同时考虑4个区果蝇收敛性以及多样性的差异，设计不同的自适应更新策略。最后，采用所提算法对各微源控制过程中的PI参数进行寻优，与其他3种智能算法进行对比，验证了所提算法的可行性和优越性。仿真结果表明，所提算法可以使系统变换器响应速度更快，输出更加稳定。 |
| 关键词: 风光燃储 微电网 PI参数 改进果蝇算法 自适应更新策略 |
| DOI：10.19783/j.cnki.pspc.230441 |
| 分类号: |
| 基金项目:国家自然科学基金项目资助(U1804143)；河南省高校基本科研业务费专项资助(NSFRF210424)；河南省高等学校重点科研计划项目资助(21B470005) |
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| A coordinated control strategy for a wind/ photovoltaic/ gas turbine/ storage microgrid based on a multi-partition adaptive improved fruit fly algorithm |
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ZHANG Li1, 2, LIU Yuhang1, JIA Chenhao1, ZHANG Tao1, ZHANG Hongwei3
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1. School of Electrical Engineering and Automation, Henan Polytechnic University, Jiaozuo 454003, China;
2. Henan Key Laboratory of Intelligent Detection and Control of Coal Mine Equipment, Jiaozuo 454003, China;
3. Linfen Power Supply Company, State Grid Shanxi Electric Power Company, Linfen 041000, China
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| Abstract: |
| To better adapt PI controller parameters in a microgrid control system to the randomness and fluctuations of renewable energy sources, this paper proposes a four-partition multi-strategy fruit fly optimization algorithm based on adaptive step size for real-time optimization of PI parameters. First, the microgrid overall control system model is established by taking the converters in different micro-source control systems of the wind/ photovoltaic/ gas turbine/ storage energy microgrid as the control objects. The PI parameters are adjusted in real-time based on this model. Then, the fruit fly population is divided into four partitions according to the fitness values of different individuals, and different adaptive updating strategies are designed based on the differences in convergence and diversity of fruit flies in the four partitions. Finally, the algorithm proposed is used to optimize the PI parameters in the control process of each microsource, and compared with three other intelligent algorithms to verify the feasibility and superiority of the proposed algorithm. The simulation results show that the algorithm can make the response of the system converter faster and the output more stable. |
| Key words: wind/ photovoltaic/ gas turbine/ energy storage microgrid PI parameter improved fruit fly algorithm adaptive updating strategies |
