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This paper reviews the current status and challenges of Neural Networks (NNs)based machine learning approaches for modern power grid stability controlincluding their design and implementation methodologies. NNs are widelyaccepted as Artificial Intelligence (AI) approaches offering an alternative wayto control complex and ill-defined problems. In this paper various applicationof NNs for power system rotor angle stabilization and control problem isdiscussed. 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 modelingnonlinearities and uncertainties are used for transient classification,neuro-control, wide-area monitoring and control, renewable energy managementand control, and so on. The works of researchers in the field of conventionaland renewable energy systems are reported and categorized. Paper concludes bypresenting, comparing and evaluating various learning techniques andinfrastructure configurations based on efficiency.

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