  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.
