Intelligent Grid Operations: Integrating Artificial Intelligence Into Advanced Distribution Management Systems For Enhanced Reliability And Predictive Control

Abstract

Advanced Distribution Management Systems (ADMS) are systems used by modern utility operations to monitor‚ control‚ and optimize the electric power distribution systems in real-time. With utilities facing an increase in disturbances including distributed renewables penetration‚ network complexity‚ and extreme weather events‚ customary rule-based and reactive ADMS applications are no longer sufficient for an efficient and proactive approach to grid management. This paper proposes a formal approach to integrating AI into ADMS. The proposed framework consists of four applications in different operational areas of ADMS: outage prediction‚ load forecasting‚ anomaly detection‚ and optimal smart control of distribution systems. We propose a multi-layered AI architecture for data ingestion‚ feature engineering‚ model execution‚ and a decision-supporting module for predictive and adaptive operations in distribution networks via SCADA‚ GIS‚ smart meters‚ and OMS. The proposed architecture is applicable to heterogeneous ADMS deployments. The research shows through experimentation‚ that the proposed system reduces outage restoration times‚ improves load forecasts and operational efficiencies. The findings conclude that AI can ease the transition of an ADMS from a reactive operational platform to a proactive clever grid management system. It also proposes a vendor-neutral AI integration framework for utilities to adopt AI-enabled grid modernization.