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Open Access

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

Qiang Guo, Lei Gao( ), Xiaojie Chu, Huadong Sun
China Electric Power Research Institute, Beijing 100192, China
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Abstract

A parameter estimation method based on an improved 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 problem 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.

Keywords

Local sensitivity analysisparameter estimationstatic var compensator modelstepwise identification methodwhale Optimization Algorithm

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CSEE Journal of Power and Energy Systems
Volume 8 Issue 2,
March 2022
Pages 535-547
DOI: 10.17775/CSEEJPES.2021.03540
Cite this article:
Guo Q, Gao L, Chu X, et al. Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm. CSEE Journal of Power and Energy Systems, 2022, 8(2): 535-547. https://doi.org/10.17775/CSEEJPES.2021.03540

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Received: 08 May 2021
Revised: 13 September 2021
Accepted: 29 November 2021
Published: 05 January 2022
© 2021 CSEE
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