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.