Machine Learning based Optimal Feedback Control for Microgrid Stabilization

TL;DR

This paper introduces a machine learning approach to optimize feedback control in microgrids, enhancing stability amid uncertainties and large disturbances by training neural networks on data from traditional control methods.

Contribution

It presents a novel ML-based control scheme trained on LQR and brute-force data to achieve optimal feedback control for microgrid stabilization.

Findings

Neural network controller effectively stabilizes microgrid under disturbances.

The approach improves real-time control performance.

Demonstrated success on a modified Kundur two-area system.

Abstract

Microgrids have more operational flexibilities as well as uncertainties than conventional power grids, especially when renewable energy resources are utilized. An energy storage based feedback controller can compensate undesired dynamics of a microgrid to improve its stability. However, the optimal feedback control of a microgrid subject to a large disturbance needs to solve a Hamilton-Jacobi-Bellman problem. This paper proposes a machine learning-based optimal feedback control scheme. Its training dataset is generated from a linear-quadratic regulator and a brute-force method respectively addressing small and large disturbances. Then, a three-layer neural network is constructed from the data for the purpose of optimal feedback control. A case study is carried out for a microgrid model based on a modified Kundur two-area system to test the real-time performance of the proposed control scheme.