Control for Grid-Connected VSC With Improved Damping Based on Physics-Informed Neural Network

Abstract

The rapid penetration of renewable energy sources into the power system makes the grid-connected voltage source converter (VSC) highly dynamic and uncertain. This necessitates designing new adaptive control for VSCs to ensure satisfactory system performance, reliability, and stability. This paper introduces a physics-informed artificial neural network (ANN) controller for the grid-connected VSC to improve the system performance and dampen the voltage oscillation due to the sudden change in power demand. The employed ANN structure is a feed-forward multilayer Neural Network trained offline by the Levenberg-Marquardt-based backpropagation algorithm. Results are presented for different dynamic scenarios to show the satisfactory operation of the proposed controller. The small-signal stability analysis is presented to validate the system's stability. Further, the performance of the proposed ANN controller is compared with the widely-used PI-controller and model predictive controller. The results prove that the proposed controller has a better dynamic performance in damping the voltage oscillation.

Control for Grid-Connected VSC With Improved Damping Based on Physics-Informed Neural Network