Abstract
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.
Highlights
This is due to the advantage of the AOC–ResNet50 network that it avoids the information asymmetry and the damage to the original features in the high and low frequency domains in the information sampling by comparing to the Octave Convolution (OctConv) structure
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 Supervisory Control and Data Acquisition (SCADA) system data, and its effectiveness in fault detection was verified by a comparative study with other competitive convolutional neural network (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 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
Summary
Wind turbine health condition assessment has become increasingly important for the purpose of realization of condition-based maintenance and operation. A radar is a graph that shows multiple quantitative parameter value changes along different axes from the same original point. Axes The starting fromposition the same original point It can chart be used the undefined information. The relativeItposition angle of the radar chart axis chart, are usually the Kiviat chart or irregular. It is a atypical evaluation method based graphically undefined information. It is typical evaluation method based on graphically comcomparison of multiple factors
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