| 引用本文: | 黄耀宣,程 杉,黄永章,等.基于改进粒子群算法的MMC-STATCOM参数仿射辨识方法[J].电力系统保护与控制,2025,53(9):176-187.[点击复制] |
| HUANG Yaoxuan,CHENG Shan,HUANG Yongzhang,et al.MMC-STATCOM affine parameter identification method based on improved particle swarm optimization[J].Power System Protection and Control,2025,53(9):176-187[点击复制] |
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| 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。 |
| 关键词: 静止同步补偿器 模块化多电平变换器 改进粒子群算法 仿射算法 熵权法 参数辨识 |
| DOI：10.19783/j.cnki.pspc.240869 |
| 投稿时间：2024-07-07修订日期：2024-08-31 |
| 基金项目:宁夏自然科学基金项目资助(2023AAC03857)；新能源电力系统全国重点实验室2024年开放课题项目资助(LAPS24006) |
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| MMC-STATCOM affine parameter identification method based on improved particle swarm optimization |
| HUANG Yaoxuan1,CHENG Shan1,HUANG Yongzhang2,XU Hengshan3,DU Pengfei4 |
| (1. College of Electrical Engineering & New Energy, China Three Gorges University, Yichang 443002, China; 2. State Key
Laboratory for Alternate Electrical Power System with Renewable Energy Sources (North China Electric Power University),
Beijing 102206, China; 3. Electric Power Research Institute of State Grid Ningxia Electric Power Co., Ltd.,
Yinchuan 750001, China; 4. State Grid Xuchang Electric Power Supply Company, Xuchang 461000, China) |
| 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. |
| Key words: static synchronous compensator modular multilevel converter improved particle swarm optimization affine algorithm entropy weight method parameter identification |
