Research on Inertia Lifting of New Power System Based on VSG Double Sagging Optimization Control

Abstract With the development of new energy sources, the power grid inertia declines rapidly, which makes the frequency adjustment ability and the power system stability poor. The virtual synchronous generator (VSG) uses a control algorithm to simulate the synchronous generator, which improves the frequency stability of the grid, but the dynamic adjustment performance would likely be degraded. Moreover, the optimal values of the pivotal parameters such as virtual inertia and damping are difficult to determine. Therefore, a VSG optimization strategy of double sagging control and neural network prediction is proposed which can effectively enhance the voltage and frequency safety of the system. Simulating the characteristic equation of the generator, the small signal analysis model of VSG is built, and the initial value of J and D is completed. An artificial neural network is accomplished, and the weight is adjusted to change the J and D of VSG by analyzing and learning the power grid operation. The expected parameter value can be gained after many times of learning and the adaptive control of VSG is realized.