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ISSN 0254-0096 CN 11-2082/K

太阳能学报 ›› 2022, Vol. 43 ›› Issue (7): 64-72.DOI: 10.19912/j.0254-0096.tynxb.2021-0571

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基于混沌海鸥优化算法的含光伏发电系统负荷模型参数辨识

盛四清1, 关皓闻1, 雷业涛2, 张文朝2, 潘晓杰3, 邵冲4, 王逾桐5   

  1. 1.华北电力大学电气与电子工程学院,保定 071003;
    2.北京科东电力控制系统有限责任公司,北京 100192;
    3.国家电网公司华中分部,武汉 430077;
    4.国家电网甘肃省电力公司,兰州 730000;
    5.沈阳工业大学电气工程学院,沈阳 110870
  • 收稿日期:2021-05-24 出版日期:2022-07-28 发布日期:2023-01-28
  • 通讯作者: 盛四清(1965—),男,博士、教授、博士生导师,主要从事电力系统运行、分析与控制方面的研究。1015980368@qq.com
  • 基金资助:
    国家重点研发计划(2017YFB0902100)

PARAMETER IDENTIFICATION OF LOAD MODEL OF PHOTOVOLTAIC POWER GENERATION SYSTEM BASED ON CHAOTIC SEAGULL OPTIMIZATION ALGORITHM

Sheng Siqing1, Guan Haowen1, Lei Yetao2, Zhang Wenchao2, Pan Xiaojie3, Shao Chong4, Wang Yutong5   

  1. 1. School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China;
    2. Beijing Kedong Electric Power Control System Co., Ltd., Beijing 100192, China;
    3. Central China Branch of State Grid Corporation, Wuhan 430077, China;
    4. State Grid Gansu Electric Power Company, Lanzhou 730000, China;
    5. School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
  • Received:2021-05-24 Online:2022-07-28 Published:2023-01-28

摘要: 针对分布式光伏发电系统广泛接入配电网,导致电力系统潮流计算速度和精度较低的问题,提出一种基于混沌海鸥优化算法的含光伏发电系统负荷模型参数辨识模型。首先,在综合负荷模型的虚拟母线上接入等效光伏发电系统的负荷模型,从而建立配电网广义负荷模型;之后,提出一种将混沌优化与海鸥优化相结合的优化算法,基于该算法完成配电网的等值,并在此基础上进行含光伏发电的综合负荷模型参数辨识。最后,通过仿真表明该文提出的算法,相比于传统的粒子群算法和单一海鸥优化算法,在计算精度和收敛速度等方面具有优越性,并可应用于负荷模型的参数优化。

关键词: 光伏发电, 负荷模型, 参数辨识, 海鸥优化算法

Abstract: Aiming at the problem that distributed photovoltaic power generation systems are widely connected to the distribution network, resulting in low power flow calculation speed and accuracy, this paper proposes a parameter identification model of photovoltaic power generation system load models based on chaotic seagull optimization algorithm. First, the load model of the equivalent photovoltaic power generation system is connected to the virtual bus of the comprehensive load model to establish a generalized load model of the distribution network; then, an optimization algorithm combining chaos optimization and seagull optimization is proposed, based on this algorithm Complete the equivalence of the distribution network, and on this basis, carry out the parameter identification of the comprehensive load model including photovoltaic power generation. Finally, simulations show that the proposed algorithm is superior to traditional particle swarm optimization and single seagull optimization in terms of calculation accuracy and convergence speed, and can be applied to load model parameter optimization.

Key words: photovoltaic power generation, load model, parameter identification, seagull optimization algorithm

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