Adaptive control strategy for VSG virtual inertia based on RBF neural network
TL;DR
A hierarchical control scheme for virtual synchronous generators (VSGs) to mitigate power oscillations and steady-state errors induced by power command variations and grid frequency fluctuations is presented, offering an effective solution for high-penetration renewable energy systems.
Abstract
This paper presents a hierarchical control scheme for virtual synchronous generators (VSGs) to mitigate power oscillations and steady-state errors induced by power command variations and grid frequency fluctuations. Unlike conventional adaptive methods that operate at the parameter level, the proposed architecture addresses the inherent structural coupling between damping and droop characteristics from a control-structure perspective. It integrates two complementary modules: a lower-level differential feedforward compensation (DFC) loop that restructures the active-power control channel to achieve structural decoupling between damping and droop, thereby eliminating steady-state deviations under frequency disturbances; and an upper-level RBF neural network that uses frequency deviation and its rate of change to dynamically regulate the virtual inertia J-the sole optimized parameter-reducing the control dimension while enhancing transient response and frequency stability. The uniform ultimate boundedness of the closed-loop system is proved via Lyapunov theory. The strategy is validated through four typical disturbance scenarios via Starsim hardware-in-the-loop experiments, along with comprehensive sensitivity analyses covering system parameters (short-circuit ratio from 1 to 5) and controller parameters (DFC coefficient and filter time constant deviations), as well as RBF initialization uncertainties. Results demonstrate that the DFC-RBF-VSG achieves zero overshoot, settling time within 0.1 s, zero steady-state error under frequency disturbances, total harmonic distortion (THD) reduced to 2.44%–2.95%, and peak inrush current limited within 300 A, consistently outperforming conventional Fixed-VSG, linear-adaptive VSG (Linear-VSG), fuzzy-based adaptive VSG (Fuzzy-VSG), and feedback-compensated VSG (FBC-VSG). The method strikes a favorable balance among dynamic responsiveness, steady-state precision, and robustness, offering an effective solution for high-penetration renewable energy systems.