A Comparative Study for Multilayer Perception versus Convolutional Neural Network Based On the Wind Turbine Benchmark Model

2021 ◽  
Author(s):  
Dapeng Zhang ◽  
Yuqing Yang ◽  
Hongwei Fang
Keyword(s):  
Neural Network ◽  
Comparative Study ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Benchmark Model ◽  
Multilayer Perception

A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification

2020 ◽  
Vol 65 (8) ◽  
pp. 2052-2061 ◽  
Author(s):  
Ting Lan ◽  
Hui Hu ◽  
Chunhua Jiang ◽  
Guobin Yang ◽  
Zhengyu Zhao
Keyword(s):  
Neural Network ◽  
Random Forest ◽  
Decision Tree ◽  
Comparative Study ◽  
Convolutional Neural Network ◽  
Spread F

scholarly journals Deep Learning Method for Fault Detection of Wind Turbine Converter

2021 ◽  
Vol 11 (3) ◽  
pp. 1280 ◽  
Author(s):  
Cheng Xiao ◽  
Zuojun Liu ◽  
Tieling Zhang ◽  
Xu Zhang
Keyword(s):  
Neural Network ◽  
Fault Detection ◽  
Convolutional Neural Network ◽  
Wind Turbine ◽  
Network Models ◽  
Detection Accuracy ◽  
Neural Network Models ◽  
Scada System ◽  
Indicator Variables ◽  
System Data

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.

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