Optimization of Power Flow Outage Detection using Machine Learning Algorithm

This study investigates the application of Machine Learning (ML) methods to detect problems in distribution networks. The main goal is to swiftly and reliably identify and categorize disturbances, hence improving the network's reliability and accelerating restoration efforts. The proposed methodology leverages the functionalities of Supervised Machine Learning algorithms such as Random Forest, Logistic Regression, and K-Nearest Neighbours, which offer user-friendliness and adaptability in addressing both classification and regression tasks for pattern recognition and anomaly detection. Through the analysis of real-time data streams collected from various sensors distributed across the grid, encompassing current measurements, breaker conditions, voltage monitoring, meteorological data, and load information, the machine learning model can discern the typical operational patterns of the network during optimal functioning. Identifying and highlighting variations in these established patterns helps facilitate prompt responses and enhance service consistency. The sophisticated framework may be modified and applied to various network architectures, promoting a more efficient and automated approach to outage management. This paper utilizes machine learning algorithms and their diverse evaluation measures to accurately anticipate power interruptions.