In response to the issue that traditional energy storage VSGs(virtual synchronous generators)cannot si-multaneously possess good disturbance resistance and rapid dynamic response capabilities,a control strategy for en-ergy storage VSGs is proposed,optimizing the dynamic synchronizer using RBF(radial basis function)neural net-works.First,a mathematical model for VSG is established,analyzing the impact of rotor inertia and damping coeffi-cient configuration on VSG performance.This analysis reveals the conflicting relationship between parameter con-figuration and dynamic response versus system dynamic stability.Subsequently,the transient unbalanced power of the rotor is taken as input for a three-layer forward structure RBF neural network algorithm.Through online learning with the RBF neural network algorithm,the optimal transient compensation power is obtained to dynamically adjust the input power of VSG,thereby reducing unbalanced rotor torque and enhancing the transient stability of VSG.Fi-nally,simulation and comparative experiments are conducted to validate the effectiveness of the proposed control strategy.
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