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- Title
Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention.
- Authors
Li, G. J.; Wang, J.; Qin, Y. W.; Bai, X. F.; Jiang, Y. H.; Deng, Y.; Ma, Z. Y.; Cao, M. N.
- Abstract
Wind farms play a crucial role in renewable energy generation, but their reliability is often compromised by complex environmental and equipment conditions. This study proposes a field of view enhanced convolutional neural network (CNN) model for fault diagnosis and prevention in wind farms. The model is developed by collecting and processing wind farm fault data and compared with support vector machine (SVM) and k-nearest neighbor (KNN) models. The results showed that the proposed CNN model outperformed the other models in terms of convergence speed (17 iterations to reach the minimum loss), fault diagnosis accuracy (99.3% and 99.2% for inner and outer circle faults, respectively), and stable power output improvement. The model's application to maintenance scheduling and economic benefit analysis in a real wind farm case demonstrated its high consistency and accuracy in fault prediction and maintenance optimization. The proposed approach has the potential to enhance wind farm reliability, efficiency, and economy by enabling accurate fault diagnosis, early warning, and preventive maintenance.
- Subjects
WIND power plants; CONVOLUTIONAL neural networks; FAULT diagnosis; OFFSHORE wind power plants; SUPPORT vector machines; K-nearest neighbor classification
- Publication
International Journal of Computers, Communications & Control, 2024, Vol 19, Issue 3, p1
- ISSN
1841-9836
- Publication type
Article
- DOI
10.15837/ijccc.2024.3.6609