# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 1 Introduction
### Data-Driven Methods
Considering that the operation of wind turbines is affected by many factors, such as wind load and environment conditions, the condition monitoring signals involve a certain degree of randomness, and hence the signals are related to the faulty converter status. These signals are generally weaker than mechanical signals. Certain correlations exist between the converter fault signals. They interact and influence each other. In order to explore the relationship between the fault signals and improve the accuracy of faultdetection, this research work starts with the selection of fault indicators and construction of radar charts based on the fault indicator variables data. The second step is to convert the radar charts into images which are then utilized for further processing in the fault detection. From the perspective of image processing, different features embedded in the images are extracted and analyzed to identify the normal and faulty operations of the wind turbines. This method helps to discover the correlation between signals, and can determine more characteristics from some weak signals involved in the process. As one kind of important network model of deep learning, the convolutional neural network can take the original image as data input, analyze the relationship and characteristics between pixels in the image and reduce the information loss of the image in the processing and hence reduce errors. With the continuous increase in the number of image samples, the network depth is required to be increasingly deeper in order to seek better processing results. However, with increase in the network depth, it is difficult to optimize the neural network, and the accuracy of the network is obviously \"degraded\" so that the satisfactory learning effects cannot be obtained. Residual Network (ResNet), which introduces residual learning into the convolutional neural network, can help effectively solve this problem of rapid degradation of the accuracy of the network as the depth increases. It can greatly increase the depth of the network, but does not cause a sharp increase in the number of parameters. In 2019, Facebook AI, the National University of Singapore and Qihoo 360 AI Institute, jointly proposed OctConv (Octave Convolution) based on the mixed characteristics of information at different frequencies [50]. Using it to replace the traditional convolution can greatly save the computing resources while improving the learning effect. Therefore, this present paper applies OctConv to the ResNet network to detect faults in the wind turbine power converter. At the same time, in order to verify the effectiveness of the improved convolutional neural network for fault detection, the fault detection effects are compared with the ResNet50 convolutional neural network and the OctConv (Oct-ResNet50) network based on the ResNet50 backbone. However, the OctConv structure has two shortcomings: one is that the asymmetry of the upsampling and downsampling methods causes the asymmetry of the information in modeling, and the second is that OctConv directly adds the high- and low-frequency domain features to realize the information interaction between different frequency domains, which greatly destroys the original features in the high- and low-frequency domains. Aiming to overcome the above shortcomings, an improved network structure based on the OctConv one, named the Attention Octave Convolution (AOctConv) structure, is proposed in this paper. First, this is to modify the existing sampling method of OctConv, replace the original downsampling method with max pooling and replace the original upsampling method with max unpooling to achieve symmetry of the downsampling and upsampling processes. Secondly, a self-attention mechanism is introduced to adaptively control the interaction process of information in the OctConv module, and self-supervise the output of the two branches in the high- and low-frequency domains.