Deep Learning Method for Fault Detection of Wind Turbine Converter
The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper pr...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/11/3/1280 |
| _version_ | 1797417587202588672 |
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| author | Cheng Xiao Zuojun Liu Tieling Zhang Xu Zhang |
| author_facet | Cheng Xiao Zuojun Liu Tieling Zhang Xu Zhang |
| author_sort | Cheng Xiao |
| collection | DOAJ |
| description | The converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning. |
| first_indexed | 2024-03-09T06:20:50Z |
| format | Article |
| id | doaj.art-7e4a64401fe4449498c621b887559936 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T06:20:50Z |
| publishDate | 2021-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-7e4a64401fe4449498c621b8875599362023-12-03T11:46:38ZengMDPI AGApplied Sciences2076-34172021-01-01113128010.3390/app11031280Deep Learning Method for Fault Detection of Wind Turbine ConverterCheng Xiao0Zuojun Liu1Tieling Zhang2Xu Zhang3School of Artificial Intelligence, Hebei University of Technology, Tianjin 300131, ChinaSchool of Artificial Intelligence, Hebei University of Technology, Tianjin 300131, ChinaFaculty of Engineering and Information Sciences, University of Wollongong, Wollongong, NSW 2522, AustraliaDepartment of Technical Development, AT&M Environmental Engineering Technology Co., Ltd., Beijing 100801, ChinaThe converter is an important component in wind turbine power drive-train systems, and usually, it has a higher failure rate. Therefore, detecting the potential faults for prediction of its failure has become indispensable for condition-based maintenance and operation of wind turbines. This paper presents an approach to wind turbine converter fault detection using convolutional neural network models which are developed by using wind turbine Supervisory Control and Data Acquisition (SCADA) system data. The approach starts with the selection of fault indicator variables, and then the fault indicator variables data are extracted from a wind turbine SCADA system. Using the data, radar charts are generated, and the convolutional neural network models are applied to feature extraction from the radar charts and characteristic analysis of the feature for fault detection. Based on the analysis of the Octave Convolution (OctConv) network structure, an improved AOctConv (Attention Octave Convolution) structure is proposed in this paper, and it is applied to the ResNet50 backbone network (named as AOC–ResNet50). It is found that the algorithm based on AOC–ResNet50 overcomes the issues of information asymmetry caused by the asymmetry of the sampling method and the damage to the original features in the high and low frequency domains by the OctConv structure. Finally, the AOC–ResNet50 network is employed for fault detection of the wind turbine converter using 10 min SCADA system data. It is verified that the fault detection accuracy using the AOC–ResNet50 network is up to 98.0%, which is higher than the fault detection accuracy using the ResNet50 and Oct–ResNet50 networks. Therefore, the effectiveness of the AOC–ResNet50 network model in wind turbine converter fault detection is identified. The novelty of this paper lies in a novel AOC–ResNet50 network proposed and its effectiveness in wind turbine fault detection. This was verified through a comparative study on wind turbine power converter fault detection with other competitive convolutional neural network models for deep learning.https://www.mdpi.com/2076-3417/11/3/1280AOC–ResNet50 networkconverterdeep learningfault detectionradar chart |
| spellingShingle | Cheng Xiao Zuojun Liu Tieling Zhang Xu Zhang Deep Learning Method for Fault Detection of Wind Turbine Converter Applied Sciences AOC–ResNet50 network converter deep learning fault detection radar chart |
| title | Deep Learning Method for Fault Detection of Wind Turbine Converter |
| title_full | Deep Learning Method for Fault Detection of Wind Turbine Converter |
| title_fullStr | Deep Learning Method for Fault Detection of Wind Turbine Converter |
| title_full_unstemmed | Deep Learning Method for Fault Detection of Wind Turbine Converter |
| title_short | Deep Learning Method for Fault Detection of Wind Turbine Converter |
| title_sort | deep learning method for fault detection of wind turbine converter |
| topic | AOC–ResNet50 network converter deep learning fault detection radar chart |
| url | https://www.mdpi.com/2076-3417/11/3/1280 |
| work_keys_str_mv | AT chengxiao deeplearningmethodforfaultdetectionofwindturbineconverter AT zuojunliu deeplearningmethodforfaultdetectionofwindturbineconverter AT tielingzhang deeplearningmethodforfaultdetectionofwindturbineconverter AT xuzhang deeplearningmethodforfaultdetectionofwindturbineconverter |