Machine learning for power system stability and control

• ML applications in the renewable energy sector enhance power system stability and control. • Data-driven techniques utilize both past and current data to enhance fault detection, predictive maintenance. • Artificial Neural Networks (ANN) and Support Vector Machines (SVM) are utilized for load forecasting, and system failure. • Reinforcement Learning (RL) technique optimizes resource allocation and demand-response strategies, ensuring grid resilience and variable renewable generation output. • Future power systems will require autonomous decision-making processes, federated learning frameworks, and physics-informed neural networks. Applying machine Learning (ML) techniques to power system control and stability has become a game-changing strategy for dealing with the increasing complexity of contemporary electrical grids. This review paper demonstrates how machine learning approaches can stabilize and manage three different power system types —voltage, small signal, and transient —for the integration of renewable energy sources. Data-driven methods and artificial neural networks can utilize sensors and actuator activities in conjunction with machine learning technologies that enable vector machines.ML ensures consistent power input and output in power systems, maximizing system restoration and safeguarding the entire system. Additionally, when the voltage source is autonomously controlled, ML technology simultaneously diagnoses and detects defects. Although obstacles are identified due to the lack of sophisticated monitoring, operation, and control, researchers are developing additional usage features, such as federated learning and physics-informed neural networks. Not all the data is available for testing, but researchers are currently working to obtain 99%.