VSG damping adaptive adjustment based on BP neural network

Abstract

The moment of inertia and damping of virtual synchronous generator (VSG) can be adjusted flexibly, which also has a significant impact on the transient performance of VSG. Constant damping or moment of inertia can not reduce frequency overshoot and fast response performance, so it is necessary to introduce adaptive damping control. Based on universal approximation theorem, BP neural network can fit continuous nonlinear function well. At the same time, it has the advantages of simple algorithm, powerful learning ability and fast learning speed. Based on the characteristics of the control object, the BP neural network is improved and a new adaptive control strategy is designed. The strategy uses improved BP neural network to adjust VSG virtual damping D online. Python-MATLAB-Simulink was used for co-simulation, BP neural network algorithm was integrated into the control object to establish an adaptive simulation model, and the proposed control strategy was simulated and verified. Simulation results show that the adaptive control strategy can eliminate overshoot and respond quickly when the frequency and active power of virtual synchronous generator change.