Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm

Qiang Guo; Lei Gao; Xiaojie Chu; Huadong Sun

doi:10.17775/CSEEJPES.2021.03540

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Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm

Regular Papers | 更新时间：2026-10-15

- Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm
- Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm
- 中国电机工程学会电力与能源系统学报(英文) 2022年8卷第2期页码：535-547
- 作者机构：
  
  China Electric Power Research Institute,Beijing,China
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2021.03540
  中图分类号：
- 纸质出版：2022-03-01
- 稿件说明：
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Qiang Guo, Lei Gao, Xiaojie Chu, 等. Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(2):535-547.

Qiang Guo, Lei Gao, Xiaojie Chu, et al. Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm[J]. CSEE Journal of Power and Energy Systems, 2022, 8(2): 535-547.
Qiang Guo, Lei Gao, Xiaojie Chu, 等. Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm[J]. 中国电机工程学会电力与能源系统学报(英文), 2022,8(2):535-547. DOI： 10.17775/CSEEJPES.2021.03540.

Qiang Guo, Lei Gao, Xiaojie Chu, et al. Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm[J]. CSEE Journal of Power and Energy Systems, 2022, 8(2): 535-547. DOI： 10.17775/CSEEJPES.2021.03540.

摘要

Abstract

A parameter estimation method based on an im-proved Whale Optimization Algorithm is proposed in this paper to identify the parameters of a static var compensator (SVC) model. First

a mathematical model of SVC is established. Then

the reverse learning strategy and Levy flight disturbance strategy are introduced to improve the whale optimization algorithm

and the improved whale optimization algorithm is applied to the parameter identification of the static var compensator model. Finally

a stepwise identification method

by analyzing the local sensitivities of parameters

is proposed which solves the prob-lem of low accuracy caused by multi-parameter identification. This method provides a new estimation strategy for accurately identifying the parameters of the static var compensator model. Estimation results show that the parameter estimation method can be an effective tool to solve the problem of parameter identification for the SVC model.

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