AI for Wind Turbine Performance and Condition Monitoring

Davide Astolfi is an assistant professor in power and energy systems at the University of Brescia, Italy. He holds a PhD in physics and a PhD in industrial and information engineering. His research focuses on data-driven methods and artificial intelligence for wind turbine performance assessment, condition monitoring, and fleet-wide diagnostics based on SCADA data. His interests also include renewable generation forecasting, electrical load forecasting, data imputation techniques, and the integration of renewable energy with electric mobility and vehicle-to-grid systems. He has authored more than 160 scientific publications and serves in editorial roles for leading journals in wind energy and smart grids, collaborating with major utility companies on predictive maintenance and performance optimization of multi-MW wind farms.Silvia Iuliano is a doctoral researcher in information technologies for engineering at the University of Sannio, Benevento, Italy. She received her BSc (Magna Cum Laude with Special Mention) and MSc (Magna Cum Laude) degrees in energy engineering from the University of Sannio. Her research interests include the optimization of distributed energy resources, game-theoretic methods for power system operation, voltage regulation in large power systems with high penetration of renewable energy resources, and hybrid supervised and unsupervised Machine Learning approaches for predicting the dynamic security state of the transmission power systems.Alfredo Vaccaro is a full professor of power and energy systems at the University of Sannio, Benevento. Italy. He is a fellow of the IEEE. He received his MSc (Hons.) degree in electronic engineering from the University of Salerno, Italy, and his PhD degree in electrical and computer engineering from the University of Waterloo, Waterloo, ON, Canada. From 1999 to 2002, he was an assistant researcher with the Department of Electrical and Electronic Engineering at the University of Salerno, Fisciano, Italy. From 2002 to 2015, he was an assistant professor of electric power systems in the Department of Engineering at the University of Sannio. His research interests include soft computing and interval-based methods applied to power system analysis, as well as self-organizing paradigms for smart grid computing. He was the Chair of the IEEE Power System Operation, Planning, and Economics Committee-Technologies and Innovations (T&I) SC. He is an associate editor of IEEE Transactions on Power Systems and IEEE Transactions on Smart Grids. He is the editor-in-chief of Smart Grids and Sustainable Energy, published by Springer Nature. He has authored more than 230 scientific publications and collaborates with the Italian Transmission System Operator and major Italian utility companies in the electric power sector.

• Chapter 1: IntroductionChapter 2: Data sources for wind turbine monitoringChapter 3: Machine Learning: concepts and foundationsChapter 4: AI-based wind turbine performance analysisChapter 5: AI-based detection of static alignment and sensor errors in wind turbinesChapter 6: AI-based condition monitoring for wind turbine maintenanceChapter 7: Concluding remarks