# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 7 Conclusions  
In this paper, a deep learning approach was employed to develop fault detection models for wind turbine converters. The contribution of this paper includes two aspects: first, it proposes an innovative convolutional neural network structure named the AOC-ResNet50 network based on improvement of the Octave Convolution (OctConv) network by overcoming its two shortcomings; second, the AOC-ResNet50 network model is established and applied to wind turbine converter fault detection using wind turbine SCADA system data, and its effectiveness in fault detection was verified by a comparative study with other competitive CNN models including ResNet50 and Oct-ResNet50 network models.  
The algorithm based on the AOC-ResNet50 network first replaces the downsampling and upsampling in the original OctConv structure with max pooling and max unpooling methods, and then introduces the branch of self-attention when the high-frequency domain features are fused to the low-frequency domain features, and the low-frequency domain features are fused to the high-frequency domain features. It controls the fusion process of the two frequency domains. After the AOC-ResNet50 network is developed, it is employed to extract the features of radar charts generated using the seven fault indicator variables data extracted from the wind turbine SCADA system for fault detection of the wind turbine converter. The fault detection performance indices were compared with the ResNet50 and Oct-ResNet50 networks to verify the effectiveness of the improved network. It was found that the fault detection performance using the AOC-ResNet50 network is superior to the ResNet50 and Oct-ResNet50 networks. The fault detection accuracy of AOC-ResNet50 network model can be up to 98.0%, which is 5.48% higher than Oct-ResNet50 and 7.52% higher than the ResNet50 model based on the radar charts generated using the fault indicator variables data that occurred 3 days ahead of converter failures. In the next step of our research work, the AOC-ResNet50 network will be applied to wind turbine converter failure prediction using fault indicator variables data that occur at different time periods before a failure occurs. At the same time, the AOC-ResNet50 network will be applied to fault detection and failure prediction for other wind turbine components.  
Methodology, C.X. and Z.L.; software, C.X. and X.Z.; validation, C.X., T.Z. and X.Z; formal analysis, C.X., T.Z. and X.Z.; investigation, C.X., X.Z. and Z.L.; data curation, X.Z. and C.X.; writing--original draft preparation, C.X. and T.Z. and X.Z.; writing--review and editing, T.Z. and C.X.; supervision, Z.L. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the National Natural Science Foundation of China (61703135; 61773151; 51577008); Hebei Natural Science Foundation (F2015202231); Youth Fund of Hebei Education Department (QN2019122); Key Project of North China Institute of Aerospace Engineering (ZD202003); The Excellent Going Abroad Experts Training Program in Hebei Province.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy.  
The wind turbine operation data were collected from a wind farm in Hebei Province, China. The authors are grateful to the wind farm manager and engineers for their kind support.  
The authors declare no conflict of interest.