ML Self-Sufficient Sustainable Energy Resiliency Management System: Outage Forecasting, Classification and Restoration with Maintenance Indicators for All Types of Power Outages

Power systems resiliency studies focus largely on operational planning, optimization, and control strategies to restore critical loads, after blackouts from extreme incidents, and natural disasters, which characterize high-impact, low-probability events. There is a lacuna of resiliency studies of other events, including blackouts with high-impact, high-probability, which classify technical faults. However, the highest percentage of blackouts are from equipment failure technical related faults. Few ML studies cover both outage forecasting and restoration, including resiliency methods for all types of power outages. This study presents a resiliency management system framework, incorporating maintenance indicators, for all types of outages from different events, particularly in developing countries, where up to 60% of blackouts are technical related. A novel framework, with machine learning classification and regression is applied. The model is validated with real historic load flows and outage interruptions of four Nigeria states. Results reveal complex multiple power outages due to different causes at different locations. A relay target indication of 91.8%, an outage type classification accuracy of 85%, and a start time regression (R) value of one, signify that the onset of all types of power outages can be predicted accurately, including indication of maintenance targets where self-sufficient, sustainable energy resources can be applied to enhance power system resilience.