Neural network-based integral sliding mode backstepping control for virtual synchronous generators

Abstract

As an important component of microgrid system, the grid-connected inverter is composed of power electronic devices, which makes the system lack physical inertia and exhibit low output impedance. Especially in the transition process of grid connection, a large current impact will be generated, which may make the system unstable. Therefore, in response to these problems, a nonlinear control method is proposed that enables the grid-connected inverter to switch freely between grid-connected mode and island mode. The linear control part introduces the mechanical equations of the rotor of the synchronous motor, which has virtual inertia and damping, and at the same time derives the corresponding mathematical model. As regards the nonlinear control part, the nonlinear controller is designed by integrating the integral sliding mode control method and backstepping control method. Meanwhile, considering the uncertainties including external disturbances, measurement errors and modelling errors, an observer based on the radial basis function neural network (RBFNN) is proposed to estimate the uncertainties to offset their influence. Then, the stability of the control system is proved by Lyapunov stability criterion. Finally, the simulation results prove that the proposed control scheme can realize the stability of the microgrid system in several operation modes and ensure the sound dynamic performance with quick response comparing with other nonlinear control methods.