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

被引：5

作者：

Bana, Prabhat Ranjan ^{[1
]}

Amin, Mohammad ^{[2
]}

机构：

[1] Norwegian University of Science and Technology, Department of Electric Energy, Trondheim,7491, Norway

[2] Enchanted Rock Management Llc, Houston,TX,77002, United States

来源：

IEEE Journal of Emerging and Selected Topics in Industrial Electronics | 2023年 / 4卷 / 03期

关键词：

Adaptive control systems - Controllers - Damping - Dynamics - Electric power system control - Multilayer neural networks - Renewable energy resources;

D O I：

10.1109/JESTIE.2023.3258339

中图分类号：

学科分类号：

摘要：

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 article 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. © 2020 IEEE.

引用

页码：878 / 888