Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network
Rotor imbalance in wind turbines presents a serious problem. Particularly for offshore wind turbines, aerodynamic imbalance could have a severe impact because of the large size of the rotor. A diagnosis method based on a parallel convolutional neural network with multi-scale feature fusion is propos...
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| Format: | Article |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10752392/ |
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| author | Zhenling Li Yukun Gao |
| author_facet | Zhenling Li Yukun Gao |
| author_sort | Zhenling Li |
| collection | DOAJ |
| description | Rotor imbalance in wind turbines presents a serious problem. Particularly for offshore wind turbines, aerodynamic imbalance could have a severe impact because of the large size of the rotor. A diagnosis method based on a parallel convolutional neural network with multi-scale feature fusion is proposed to diagnose rotor imbalance. It consists of two feature extractors of different scales, which are combined in the fully connected layer. Firstly, a model of a 3MW wind turbine is built and the mass imbalance and aerodynamic imbalance are added to the simulation. The signal is collected and the effects of rotor imbalance on the nacelle vibration in wind turbines are investigated and described. Secondly, the nacelle vibration is selected as the target signal. Wavelet transform is performed on the collected signals, and the 2-dimensional time-frequency map is obtained as the object dataset for the classification. Thirdly, a convolutional neural network is used to classify rotor imbalances of different magnitudes, and different convolution kernels and activation functions are tested. Finally, a new data set is built in the highly fidelity simulation model, and the trained model is loaded for test and verification. The experiments show that the proposed diagnosis model based on the time-frequency map of nacelle vibrations and a convolutional neural network can identify rotor imbalance effectively, and the accuracy is greater than 98%. The results demonstrate the satisfactory performance of the proposed method. It can diagnose rotor imbalance effectively without additional sensors. |
| format | Article |
| id | doaj-art-45c73611201740f895b047a61430c635 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-45c73611201740f895b047a61430c6352025-01-16T00:01:54ZengIEEEIEEE Access2169-35362024-01-011217625917626910.1109/ACCESS.2024.349692110752392Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural NetworkZhenling Li0https://orcid.org/0009-0004-5979-2716Yukun Gao1CGN New Energy Holdings Company Ltd., Shenyang, ChinaCGN New Energy Holdings Company Ltd., Shenyang, ChinaRotor imbalance in wind turbines presents a serious problem. Particularly for offshore wind turbines, aerodynamic imbalance could have a severe impact because of the large size of the rotor. A diagnosis method based on a parallel convolutional neural network with multi-scale feature fusion is proposed to diagnose rotor imbalance. It consists of two feature extractors of different scales, which are combined in the fully connected layer. Firstly, a model of a 3MW wind turbine is built and the mass imbalance and aerodynamic imbalance are added to the simulation. The signal is collected and the effects of rotor imbalance on the nacelle vibration in wind turbines are investigated and described. Secondly, the nacelle vibration is selected as the target signal. Wavelet transform is performed on the collected signals, and the 2-dimensional time-frequency map is obtained as the object dataset for the classification. Thirdly, a convolutional neural network is used to classify rotor imbalances of different magnitudes, and different convolution kernels and activation functions are tested. Finally, a new data set is built in the highly fidelity simulation model, and the trained model is loaded for test and verification. The experiments show that the proposed diagnosis model based on the time-frequency map of nacelle vibrations and a convolutional neural network can identify rotor imbalance effectively, and the accuracy is greater than 98%. The results demonstrate the satisfactory performance of the proposed method. It can diagnose rotor imbalance effectively without additional sensors.https://ieeexplore.ieee.org/document/10752392/Convolutional neural networkrotor imbalancewavelet transformwind energywind turbine |
| spellingShingle | Zhenling Li Yukun Gao Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network IEEE Access Convolutional neural network rotor imbalance wavelet transform wind energy wind turbine |
| title | Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network |
| title_full | Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network |
| title_fullStr | Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network |
| title_full_unstemmed | Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network |
| title_short | Research on Wind Turbine Unbalance Fault Diagnosis Based on Wavelet Transform and Convolutional Neural Network |
| title_sort | research on wind turbine unbalance fault diagnosis based on wavelet transform and convolutional neural network |
| topic | Convolutional neural network rotor imbalance wavelet transform wind energy wind turbine |
| url | https://ieeexplore.ieee.org/document/10752392/ |
| work_keys_str_mv | AT zhenlingli researchonwindturbineunbalancefaultdiagnosisbasedonwavelettransformandconvolutionalneuralnetwork AT yukungao researchonwindturbineunbalancefaultdiagnosisbasedonwavelettransformandconvolutionalneuralnetwork |