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Parameter Identification for Static Var Compensator Model Using Sensitivity Analysis and Improved Whale Optimization Algorithm
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DOI：10.17775/CSEEJPES.2021.03540.png)
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摘要
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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.
Keyword:
Static VAr compensators
Mathematical models
Power system stability
Whales
Parameter estimation
Optimization
Stability analysis
Local sensitivity analysis
parameter estimation
static var compensator model
stepwise identification method
whale Optimization Algorithm
