New Trends for Machine Learning Applications in Future Power Systems

Electrical engineering has the power to transform the world as we know it through the efficient use of energy. Electrical engineering forms the foundation of modern technology, powering industries, and innovation. It enables efficient energy use, energy conservation, integration of renewable resources, and optimization through power electronics. The future of electrical engineering lies in integrating the Internet of Things, artificial intelligence and automation, and energy storage innovations. The increasingly intricate and interconnected nature of modern power systems predicates a substantial reliance on advanced data processing and analytical capabilities to ensure efficient operation, reliability, and integration of renewable energy resources. Machine learning (ML) has surfaced as a formidable tool to confront these challenges by providing sophisticated means to analyze the massive volumes of data generated by the grid and facilitate real-time decision-making and predictive insights. Integrating ML techniques in power systems has brought about significant advancements in monitoring, controlling, and optimizing electrical networks. This chapter delves into the progressive trends of ML applications in power systems and explores the prospective landscapes these computational techniques are poised to transform. It explores the potential impact of new technologies such as digital twins, federated learning (FL) for data privacy and security, edge computing, explainable AI, natural language processing (NLP), and quantum computing (QC) on the future of power systems.