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Artificial Intelligence and Digital Twin Applications in Wind Turbine Monitoring, Control, and Maintenance
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Author(s): Siphelele Joseph Mndiya (Cape Peninsula University of Technology, South Africa)and Senthil Krishnamurthy (Cape Peninsula University of Technology, South Africa)
Copyright: 2026
Pages: 78
Source title:
AI-Powered Analysis, Modeling, and Monitoring of Wind Energy Systems
Source Author(s)/Editor(s): Jackson J. Justo (University of Dar es Salaam, Dar es Salaam, Tanzania & ITMO University, St. Petersburg, Russia), Galina Demidova (Hangzhou Dianzi University, China & ITMO University, St. Petersburg, Russia), Francis A. Mwasilu (University of Dar es Salaam, Tanzania), Dmitry V. Lukichev (ITMO University, Russia)and Ramesh C. Bansal (University of Sharjah, Sharjah, UAE & University of Pretoria, Pretoria, South Africa)
DOI: 10.4018/979-8-3373-4159-0.ch003
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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.
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