Work

RBF Neural Network-Enhanced Adaptive Sliding Mode Control for VSG Systems with Multi-Parameter Optimization

Year: 2025

Type: article

Abstract: Virtual synchronous generator (VSG) simulates the dynamic characteristics of synchronous generator, offering significant advantages in flexibly adjusting virtual inertia and damping parameters. However, their dynamic stability is susceptible to constraints such as control parameter design, grid disturbances, and the intermittent nature of distributed power sources. This study addresses the degradation of transient performance in traditional sliding mode control for VSG, caused by insufficient multi-parameter cooperative adaptation. It proposes an adaptive sliding mode control strategy based on radial basis function (RBF) neural networks. Through theoretical analysis of the influence mechanism of virtual inertia and damping coefficient perturbations on system stability, the RBF neural network achieves dynamic parameter decoupling and nonlinear mapping. Combined with an integral-type sliding surface to design a weight-adaptive convergence law, it effectively avoids local optima and ensures global stability. This strategy not only enables multi-parameter cooperative adaptive regulation of frequency fluctuations but also significantly enhances the system’s robustness under parameter perturbations. Simulation results demonstrate that compared to traditional control methods, the proposed strategy exhibits significant advantages in dynamic response speed and overshoot suppression.

Source: Electronics

DOI: 10.3390/electronics14214309

Authors: Jian Sun, Chuangxin Chen, Huakun Wei

Institution: China Three Gorges University

Language: English

Topic: Microgrid Control and Optimization

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $2,896