Artificial Intelligence and Digital Twin Applications in Wind Turbine Monitoring, Control, and Maintenance

Abstract

This chapter explores how digital twins (DT), machine learning (ML), and artificial intelligence (AI) enhance monitoring, control, and maintenance of modern wind turbines. These technologies enable predictive, data-driven solutions using real-time SCADA and high-resolution sensor data, reducing reliance on reactive, schedule-based maintenance. AI and ML improve resource allocation, fault detection, and performance optimization through continuous learning, while DTs synchronize virtual and physical turbines for advanced diagnostics, planning, and simulation. The chapter also outlines implementation challenges, including model drift, data delays, security risks, and integration within existing infrastructure. It aims to guide researchers and engineers in deploying AI-based tools to improve turbine reliability, efficiency, and lifecycle management.