Deep Generative Convolutional Neural Network based Wind Turbines Condition Monitoring
Publish place: 6th Annual Clean Energy Conference
Publish Year: 1397
نوع سند: مقاله کنفرانسی
زبان: English
View: 540
This Paper With 6 Page And PDF Format Ready To Download
- Certificate
- من نویسنده این مقاله هستم
این Paper در بخشهای موضوعی زیر دسته بندی شده است:
استخراج به نرم افزارهای پژوهشی:
شناسه ملی سند علمی:
CLEANENERGY06_048
تاریخ نمایه سازی: 1 دی 1398
Abstract:
Condition monitoring of wind turbines (WTs) has attracted attention due to fast development of WTs. The inherent intermittence of wind energy and being located in remote area makes it difficult to design proper fault diagnosing method for WTs. To address this issue, we proposed a two block deep learning based method in this paper, which encapsulates two feature extraction and classification in an end-to-end architecture. In the designed method, we used generative adversarial network (GAN) as the feature extraction block and convolutional neural network (CNN) as the fault classifier block. The simulations are fulfilled based on real-data from a 3 MGW WT in Ireland, which is obtained from supervisory control and data acquisition system (SCADA). The results demonstrate that the proposed method is a proper alternative for fault classification of WTs. To show the superiority of the proposed method, the results are compared with the results of applying support vector machine (SVM) and feed-forward neural network (FFNN).
Keywords:
Wind turbine fault classification , Generative adversarial network (GAN) , Convolutional neural network (CNN) , Deep learning , Condition and monitoring of WT.
Authors
Benyamin Parang
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
Shahabodin Afrasiabi
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
Mousa Afrasiabi
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
Mohammad Mohammadi
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran
Mohammad Rastegar
School of Electrical and Computer Engineering Shiraz University Shiraz, Iran