Davide Astolfi - Böcker

Wind power is unanimously recognized as one of the major drivers of the energy transition. Increasing renewable power generation introduces significant challenges for both the operation and planning of power systems, driven by the intrinsic uncertainty of stochastic renewable resources and by the growing spatial distribution of generation assets. Wind power presents a distinctive set of challenges in this context.Wind turbines are complex machines operating under highly non-stationary conditions and are composed of tightly coupled mechanical, electrical, and electronic subsystems. In order to ensure reliable power system operation and to minimize the levelized cost of energy, it is essential to continuously monitor the health status of wind turbines, and to improve the efficiency of wind energy conversion as much as possible. Artificial intelligence has the potential to help address these challenges.The objective of this book is to address the gap between domain expertise in wind energy and the rapid proliferation of machine learning techniques. While advanced data-driven models offer unprecedented flexibility and predictive capabilities, their increasing complexity can come at the cost of transparency, physical interpretability, and engineering insight. Bridging this gap demands a critical understanding of the problem at hand, a clear definition of the operational objective, and a conscious selection of the most appropriate techniques compatible with the available data sources.Offering concise but thorough coverage of the topic, AI for Wind Turbine Performance and Condition Monitoring explores data sources from turbines and fleets, reviews the fundamentals of ML, then covers AI-based wind turbine performance analysis, AI-based detection of static misalignment and sensor errors, and condition monitoring for wind turbine maintenance.Wind power researchers in academia and industry, grid operators, and maintenance managers will find this book offers a valuable overview and analysis of AI-based methodologies for wind generators.