2019
DOI: 10.1109/access.2019.2937193
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Classification of Power Quality Disturbances Using Wigner-Ville Distribution and Deep Convolutional Neural Networks

Abstract: 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-… Show more

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Cited by 94 publications
(41 citation statements)
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“…To address the above challenge, DL methods are employed for TD. In the distribution system, CNN has been explored extensively because of its excellent local sense ability and employed in fault location [37], switch action identification [38] and disturbance classification [39] so far. Thus, considering the locally connected structure of the distribution system, CNN is selected as a base neural network to implement the mapping process from measurement data to switches status.…”
Section: Methodsmentioning
confidence: 99%
“…To address the above challenge, DL methods are employed for TD. In the distribution system, CNN has been explored extensively because of its excellent local sense ability and employed in fault location [37], switch action identification [38] and disturbance classification [39] so far. Thus, considering the locally connected structure of the distribution system, CNN is selected as a base neural network to implement the mapping process from measurement data to switches status.…”
Section: Methodsmentioning
confidence: 99%
“…With the development of computer technology, CNN as an artificial intelligence algorithm has been widely used in the research of power industry. A method for power quality disturbance classification is proposed by combining deep convolutional neural network with wigner-ville distribution [13]. Research on the application of deep learning in fault diagnosis of wind turbine gearbox and condition monitoring of wind turbine gearbox bearing highlight the excellent ability of deep learning in fault classification [14], [15].…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, there are many research works using CNN for power quality (PQ) analysis. Several works that use DL in PQ have been reported and they are interested in not detection but usually a classification of parameters or events in PQ [15–27]. In this study, a new method based on CNN is proposed for fast and accurate detection of phase and amplitude information of rapidly time‐varying harmonic components of voltages and currents of the power systems.…”
Section: Introductionmentioning
confidence: 99%