An Exploratory Analysis of Current and Future Trends of Artificial Neural Network (ANN) Paradigms in Wind Energy Systems

This chapter delves into the transformative role of artificial neural networks (ANNs) in wind energy systems, focusing on their application in forecasting, design optimization, fault detection, and control optimization. It begins by highlighting the significance of wind energy as a sustainable resource and the need for enhancing the efficiency and reliability of wind energy systems (WES). The study explores how ANNs, with their ability to process large amounts of data and adapt to complex situations, are being used to address various challenges in the wind energy sector. It provides a detailed classification of ANN techniques according to their specific goals, with a particular emphasis on forecasting and prediction, fault detection and diagnosis (FDD), and design and control optimization. The chapter also discusses the use of ANNs in predicting wind speed and power, designing wind turbines and farms, and detecting faults in various components of wind turbines. It concludes by providing a statistical analysis of the literature, showing how ANN applications in wind turbines have advanced over the previous ten years and where they are now. This comprehensive overview offers valuable insights into the current and future trends of ANN paradigms in wind energy systems, making it an essential resource for professionals seeking to understand and leverage these advanced technologies.