Machine Learning for Power Systems - Is it Time to Trust it?

Explore the critical question of whether machine learning applications in power systems have reached sufficient maturity for widespread deployment in this comprehensive lecture by Professor Spyros Chatzivasileiadis from the Technical University of Denmark. Examine the current state of ML technologies in electrical power system operations, including their capabilities, limitations, and reliability concerns that affect grid stability and safety. Analyze real-world case studies demonstrating both successful implementations and potential pitfalls of machine learning algorithms in power system monitoring, control, and optimization. Investigate the technical challenges surrounding data quality, model interpretability, and system integration that must be addressed before full-scale adoption. Learn about validation methodologies, performance metrics, and safety protocols essential for evaluating ML solutions in critical infrastructure applications. Discover emerging trends in power system digitalization and the role of artificial intelligence in supporting renewable energy integration, demand response, and smart grid operations. Gain insights into regulatory considerations, industry standards, and best practices for implementing trustworthy machine learning systems in power engineering contexts.