A Review of Neural Network Based Machine Learning Approaches for Rotor Angle Stability Control

TL;DR

This review paper discusses the application of neural network-based machine learning methods, especially reinforcement and supervised learning, for rotor angle stability control in power systems, highlighting their design, implementation, and effectiveness.

Contribution

It provides a comprehensive overview of neural network approaches for power system stability, categorizing recent research and evaluating their efficiency and practical deployment.

Findings

Neural networks effectively model nonlinearities in power systems.

Reinforcement and supervised learning are prominent in wide-area control.

Various techniques are compared for efficiency and implementation challenges.

Abstract

This paper reviews the current status and challenges of Neural Networks (NNs) based machine learning approaches for modern power grid stability control including their design and implementation methodologies. NNs are widely accepted as Artificial Intelligence (AI) approaches offering an alternative way to control complex and ill-defined problems. In this paper various application of NNs for power system rotor angle stabilization and control problem is discussed. The main focus of this paper is on the use of Reinforcement Learning (RL) and Supervised Learning (SL) algorithms in power system wide-area control (WAC). Generally, these algorithms due to their capability in modeling nonlinearities and uncertainties are used for transient classification, neuro-control, wide-area monitoring and control, renewable energy management and control, and so on. The works of researchers in the field of conventional and renewable energy systems are reported and categorized. Paper concludes by presenting, comparing and evaluating various learning techniques and infrastructure configurations based on efficiency.