LEVERAGING BIG DATA AND MACHINE LEARNING FOR ADVANCED SUBSTATION MANAGEMENT

Abstract

The increasing complexity of modern power systems and the integration of renewable energy resources demand more intelligent and adaptive solutions for substation management. Conventional supervisory control and data acquisition (SCADA)-based approaches are often limited in handling the massive and heterogeneous data streams generated by smart grids. This paper explores the role of Big Data analytics and Machine Learning (ML) techniques in advancing substation management and control. By leveraging real-time data from sensors, intelligent electronic devices (IEDs), and communication networks, predictive models can be developed for fault detection, load forecasting, equipment health monitoring, and cyberattack prevention. The integration of Big Data frameworks enables scalable data storage and processing, while ML algorithms enhance decision-making accuracy and automation. The proposed framework demonstrates improved system reliability, reduced downtime, and enhanced operational efficiency. The study highlights the potential of data-driven strategies in building resilient, secure, and self-healing substations that align with the vision of future smart grids.