Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks

This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a WVD technique is developed to transfer a 1D voltage disturbance signal into a 2D image file, followed by a CNN model...

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Main Authors: Kewei Cai, Wenping Cao, Lassi Aarniovuori, Hongshuai Pang, Yuanshan Lin, Guofeng Li
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/8811462/
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author Kewei Cai
Wenping Cao
Lassi Aarniovuori
Hongshuai Pang
Yuanshan Lin
Guofeng Li
author_facet Kewei Cai
Wenping Cao
Lassi Aarniovuori
Hongshuai Pang
Yuanshan Lin
Guofeng Li
author_sort Kewei Cai
collection DOAJ
description This paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a WVD technique is developed to transfer a 1D voltage disturbance signal into a 2D image file, followed by a CNN model developed for the image classification. Then, the feature maps are extracted automatically from the image file and different patterns are extracted from variables on CNN. A set of synthetic signals, as well as real-world measurement data, are used to test the proposed method. The high classification accuracy of test results is achieved to confirm the effectiveness of the proposed method. Furthermore, the model is simplified and optimized by visualizing the output of convolutional layers. On this basis, one visualizing technique called the class activation map (CAM) is used to identify the location and shape of “hotspots (PQDs)”. The effect of incorrect classification of the model is analyzed with the CAM. Therefore, the proposed method is proved to have the capability of providing necessary and accurate information for PQDs, which will then be used to determine the subsequent PQ remedy actions accordingly.
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spelling doaj.art-8d9a783457a743a68742cc7e3134683f2022-12-21T19:51:39ZengIEEEIEEE Access2169-35362019-01-01711909911910910.1109/ACCESS.2019.29371938811462Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural NetworksKewei Cai0https://orcid.org/0000-0003-4144-3898Wenping Cao1https://orcid.org/0000-0002-8133-3020Lassi Aarniovuori2Hongshuai Pang3Yuanshan Lin4Guofeng Li5https://orcid.org/0000-0002-6687-112XCollege of Information Engineering, Dalian Ocean University, Dalian, ChinaCollege of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, ChinaLUT School of Energy Systems, LUT University, Lappeenranta, FinlandCollege of Information Engineering, Dalian Ocean University, Dalian, ChinaCollege of Information Engineering, Dalian Ocean University, Dalian, ChinaSchool of Electrical Engineering, Dalian University of Technology, Dalian, ChinaThis paper proposes a hybrid approach combining Wigner-Ville distribution (WVD) with convolutional neural network (CNN) for power quality disturbance (PQD) classification. Firstly, a WVD technique is developed to transfer a 1D voltage disturbance signal into a 2D image file, followed by a CNN model developed for the image classification. Then, the feature maps are extracted automatically from the image file and different patterns are extracted from variables on CNN. A set of synthetic signals, as well as real-world measurement data, are used to test the proposed method. The high classification accuracy of test results is achieved to confirm the effectiveness of the proposed method. Furthermore, the model is simplified and optimized by visualizing the output of convolutional layers. On this basis, one visualizing technique called the class activation map (CAM) is used to identify the location and shape of “hotspots (PQDs)”. The effect of incorrect classification of the model is analyzed with the CAM. Therefore, the proposed method is proved to have the capability of providing necessary and accurate information for PQDs, which will then be used to determine the subsequent PQ remedy actions accordingly.https://ieeexplore.ieee.org/document/8811462/Classificationconvolutional neural network (CNN)deep learningpower quality disturbancespower systemsWigner-Ville distribution (WVD)
spellingShingle Kewei Cai
Wenping Cao
Lassi Aarniovuori
Hongshuai Pang
Yuanshan Lin
Guofeng Li
Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
IEEE Access
Classification
convolutional neural network (CNN)
deep learning
power quality disturbances
power systems
Wigner-Ville distribution (WVD)
title Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
title_full Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
title_fullStr Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
title_full_unstemmed Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
title_short Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks
title_sort classification of power quality disturbances using wigner ville distribution and deep convolutional neural networks
topic Classification
convolutional neural network (CNN)
deep learning
power quality disturbances
power systems
Wigner-Ville distribution (WVD)
url https://ieeexplore.ieee.org/document/8811462/
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AT wenpingcao classificationofpowerqualitydisturbancesusingwignervilledistributionanddeepconvolutionalneuralnetworks
AT lassiaarniovuori classificationofpowerqualitydisturbancesusingwignervilledistributionanddeepconvolutionalneuralnetworks
AT hongshuaipang classificationofpowerqualitydisturbancesusingwignervilledistributionanddeepconvolutionalneuralnetworks
AT yuanshanlin classificationofpowerqualitydisturbancesusingwignervilledistributionanddeepconvolutionalneuralnetworks
AT guofengli classificationofpowerqualitydisturbancesusingwignervilledistributionanddeepconvolutionalneuralnetworks