Dynamic Event Detection Using a Distributed Feature Selection Based Machine Learning Approach in a Self-Healing Microgrid

A self-healing function is an attractive feature in any modern microgrid. Once a fault occurs, it is imperative for a grid to monitor its status, take action based on the level of severity, and after the contingency has been cleared, restore the system. With an increasing number of microgrids and distributed generation stations, deploying a centralized control is no longer a cost-effective option, and therefore distributed control is a likely solution. In an interconnected network, it is important to detect the underlying events taking place in each of the distributed stations, otherwise operational decisions become noncoherent. This paper proposes a novel, feature selection-based distributed machine learning approach to detect the dynamic signatures of different power system events. The purpose is to facilitate a postfault decision-making process in order to restore a stand-alone microgrid without the intervention of a central station. The proposed method detects meaningful features from the generator data and then applies a multiclass classification algorithm to the feature data. Each class represents one dynamic event taking place. The methodology is demonstrated in an interconnected two-area-based microgrid with multiple types of energy generation schemes.