Husam AlShannaq, Aly Mousaad Aly*
Structural Durability & Health Monitoring, Vol.18, No.6, pp. 707-737, 2024, DOI:10.32604/sdhm.2024.054731
Abstract Wind turbines have emerged as a prominent renewable energy source globally. Efficient monitoring and detection methods are crucial to enhance their operational effectiveness, particularly in identifying fatigue-related issues. This review focuses on leveraging artificial neural networks (ANNs) for wind turbine monitoring and fatigue detection, aiming to provide a valuable reference for researchers in this domain and related areas. Employing various ANN techniques, including General Regression Neural Network (GRNN), Support Vector Machine (SVM), Cuckoo Search Neural Network (CSNN), Backpropagation Neural Network (BPNN), Particle Swarm Optimization Artificial Neural Network (PSO-ANN), Convolutional Neural Network (CNN), and nonlinear autoregressive… More >
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