دورية أكاديمية
Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention
| العنوان: | Enhancing Wind Farm Reliability: A Field of View Enhanced Convolutional Neural Network-Based Model for Fault Diagnosis and Prevention |
|---|---|
| المؤلفون: | Li, Guojian, Wang, Jian, Qin, Yingwu, Bai, Xuefeng, Jiang, Yuhan, Deng, Yi, Ma, Zhiyuan, Cao, Mengnan |
| المصدر: | INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL; Vol. 19 No. 3 (2024): International Journal of Computers Communications & Control (June) ; 1841-9844 ; 1841-9836 ; 10.15837/ijccc.2024.3 |
| بيانات النشر: | Agora University Press |
| سنة النشر: | 2024 |
| المجموعة: | Agora University Editing House: Journals |
| مصطلحات موضوعية: | Enhanced visual field, Convolutional neural network, Wind farms, Fault diagnosis, Prevention model |
| الوصف: | 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. |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | application/pdf |
| اللغة: | English |
| العلاقة: | https://univagora.ro/jour/index.php/ijccc/article/view/6609/2088Test; https://univagora.ro/jour/index.php/ijccc/article/view/6609Test |
| DOI: | 10.15837/ijccc.2024.3.6609 |
| الإتاحة: | https://doi.org/10.15837/ijccc.2024.3.6609Test https://doi.org/10.15837/ijccc.2024.3Test https://univagora.ro/jour/index.php/ijccc/article/view/6609Test |
| حقوق: | Copyright (c) 2024 Guojian Li, Jian Wang, Yingwu Qin, Xuefeng Bai, Yuhan Jiang, Yi Deng, Zhiyuan Ma, Mengnan Cao ; http://creativecommons.org/licenses/by-nc/4.0Test |
| رقم الانضمام: | edsbas.D9BB8EFA |
| قاعدة البيانات: | BASE |
| DOI: | 10.15837/ijccc.2024.3.6609 |
|---|