Real-Time Machine Learning for Power Grid SCADA Alarm Event Detection Decision Support

The operation of power grids is increasingly complex and there is a growing need for data-driven fault diagnosis in smart grid dispatching. This study presents an innovative real-time machine learning framework that significantly enhances the detection and management of alarm events in power grid SCADA systems. At the heart of this framework is a novel deep neural network (DNN) architecture designed to efficiently identify alarm events. Additionally, an algorithm prioritizing the identification of historically relevant alarm events is developed, providing robust decision support. The proposed framework is constructed and underwent thorough testing with a comprehensive power grid dataset. The results demonstrate that the framework not only meets but exceeds the operational demands for real-time alarm detection, particularly during periods of alarm floodpeak. Moreover, the alarm event searching and decision support algorithm is proven to be a critical tool for power grid control room experts and operators, facilitating rapid decision-making by providing actionable insights based on historical and current data analysis. The successful integration of this machine learning framework into SCADA systems marks a significant step forward in the technological evolution of energy systems, leading to smarter, more efficient, and reliable power grid management.