# Deep Learning Method for Fault Detection of Wind Turbine Converter
## 5 Wind Turbine Power Converter Fault Detection  
This article applied the improved AOctConv structure to the ResNet50 backbone network, and proposes the AOC-ResNet50 network. Based on the radar charts of converter fault indicator variables data which occurred 24 h, 3 days, 7 days and 15 days before a failure, this paper used ResNet50, Oct-ResNet50 and AOC-ResNet50 network structures to train and validate the fault detection models. After validation, each model's structure was determined including the weight of each link. Then, the models were applied to fault detection using SCADA system data. The fault detection performance indices were compared among these models using the test sample data. The effectiveness of these network models was then verified. 1. Sample data  
As a pure electric component, the wind turbine converter has a high frequency of failures due to frequent starting and braking. For the faulty operation data, the data that occurred 24 h, 3, 7 and 15 days before the occurrence of a converter failure were extracted, the sampling frequency was every 10 min and 100 sets of the data were collected during each fault time period (each fault time period refers to 1, 3, 7 and 15 days, respectively). The normal operation data are the data when the system has no fault, and the extracted quantity is the same as the failure operation data. In each group of the data collected, the normal operation data dimensions were 14,400 \(\times\) 7; and corresponding to each fault time period, the faulty operation data dimensions were 14,400 \(\times\) 7 as well. Taking the wind speed as the reference index, after normalization of each fault indicator variable values, the radar charts were drawn. Corresponding to each fault time period, 17,001 radar charts representing the normal operation of converter and 17,001 radar charts corresponding to the faulty operation status of the converters were generated, totaling 34,002. Among them, 11,900 normal and faulty status radar charts were selected for training, and 5101 normal and faulty status radar charts were selected for testing. 2. Implementation of the training and testing method  
In order to adapt to the task of fault detection, the output dimension of the final fully connected layer of the AOC-ResNet50 network was set as 2 in this paper. In the training process, the original radar image size of 256 \(\times\) 256 pixels was first scaled to the universal size 224 \(\times\) 224 pixels, and then the whitening operation was performed after expansion to three channels, and no other data augmentation operations were performed. Finally, the picture was used as the input of the model training. The loss function of the task training used cross-entropy loss, batch size was 64, stochastic gradient descent (SGD) method was used for optimization and the training framework was Pytorch. Hardware platform utilized  
Training GPU: Single card RTX2080 Ti. CPU Intel(R) Xeon(R) CPU E5-2650 v4 @ 2.20 GHz. Memory: 256 G.  
Analysis of fault detection results for converter  
In order to verify the effectiveness of the proposed AOC-ResNet50 network, ResNet50, Oct-ResNet50 and AOC-ResNet50 networks were used for converter fault detection, and the fault detection results obtained are shown in Tables 3-7 below. In Tables 3 and 5, TP denotes True Positive, which means that the true value is true and the detected/predicted value is true; FN is False Negative, which means that the true value is true and the detected/predicted value is false; FP is False Positive, meaning that the true value is false and the detected/predicted value is true; and TN is True Negative, which means that the true value is false and the detected/predicted value is false. In Tables 4, 6 and 7, accuracy = (TP + TN)/(TP + TN + FP + FN); precision = TP/(TP + FP); recall = TP/(TP + FN); specificity = TN/(TN + FP); and negative precision = TN/(TN + FN).