基于改进粒子群算法的MMC-STATCOM参数仿射辨识方法

HUANG Yaoxuan; CHENG Shan; HUANG Yongzhang; XU Hengshan; DU Pengfei

doi:10.19783/j.cnki.pspc.240869

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基于改进粒子群算法的MMC-STATCOM参数仿射辨识方法

更新时间：2025-10-29

- 基于改进粒子群算法的MMC-STATCOM参数仿射辨识方法
- Power System Protection and Control Vol. 45, Issue 9, (2025)
- 作者机构：
  
  1. 1.三峡大学电气与新能源学院,湖北,宜昌,443002
  2. 新能源电力系统国家重点实验室(华北电力大学),北京,102206
  3. 国网宁夏电力有限公司电力科学研究院,宁夏,银川,750001
  4. 国网许昌供电公司,河南,许昌,461000
- 作者简介：
- 基金信息：
- DOI：10.19783/j.cnki.pspc.240869
  CLC：
- Published：2025
- 稿件说明：
移动端阅览
HUANG Yaoxuan, CHENG Shan, HUANG Yongzhang, et al. 基于改进粒子群算法的MMC-STATCOM参数仿射辨识方法[J]. Power system protection and control, 2025, 45(9).
DOI：

HUANG Yaoxuan, CHENG Shan, HUANG Yongzhang, et al. 基于改进粒子群算法的MMC-STATCOM参数仿射辨识方法[J]. Power system protection and control, 2025, 45(9). DOI： 10.19783/j.cnki.pspc.240869.

摘要

参数辨识效果会强关联于耦合误差，同时参数的耦合误差又呈现出高复杂性特征，但传统粒子群算法难以高效利用耦合误差扩充算法搜索范围。提出一种基于改进粒子群算法的模块化多电平静止同步补偿器参数仿射辨识方法。首先，建立模块化多电平静止同步补偿器(modular multilevel converter static synchronous compensator

MMC-STATCOM)的数学模型，确定待辨识参数。其次，提出一种基于仿射算法的参数辨识方法，将参数辨识问题转化为参数与耦合误差联合辨识问题。在此基础上，采用熵权法综合考虑区间满足度和区间误差对辨识效果的影响，结合改进粒子群算法实现模块化多电平静止同步补偿器的参数与耦合误差的解耦辨识。仿真结果证明，参数和耦合误差的辨识误差分别在1.06%和2.95%以内。

Abstract

The effectiveness of parameter identification is strongly related to coupling errors

which exhibit highly complexity characteristics. However

traditional particle swarm optimization (PSO) algorithms are difficult to efficiently utilize the coupling errors to expand their search range. This paper proposes an affine parameter identification method for modular multilevel converter static synchronous compensator (MMC-STATCOM) based on an improved PSO. First

the mathematical model of the MMC-STATCOM is established to determine the parameters to be identified. Next

an affine-based parameter identification method is proposed to transform the parameter identification problem into a joint identification problem of parameters and coupling errors. Building on this

the entropy weight method is adopted to comprehensively consider the influence of interval satisfaction and interval errors on the identification results. The improved PSO is then used to realize decoupling identification of the parameters and coupling errors for the MMC-STATCOM. Simulation results prove that the identification errors of parameters and coupling errors are within 1.06% and 2.95%

respectively.

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XU Hengshan(1. College of Electrical Engineering & New Energy, China Three Gorges University

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