An efficient partial discharge pattern recognition method using texture analysis for transformer defect models

Rostaminia, Reza; Saniei, Mohsen; Vakilian, Mehdi; Mortazavi, S. S.; Darabad, Vahid Parvin

doi:10.1002/etep.2558

Cited by 11 publications

(12 citation statements)

References 28 publications

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“…The ability of MDE to characterize signal complexity depends on the selection of embedding dimension m , category number c , delay time d , and scale factor τ max . 20,21 Azami et al 21 suggest that the values of the embedded dimension m and the category c should not be too small or too large, m usually takes 2 or 3, c takes an integer between, 4,8 and the delay d generally takes 1. Therefore, we take m = 3, c = 6, and d = 1.…”

Section: Feature Extraction Process Based On Sswft–mdementioning

confidence: 99%

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

Sun

Wang

2020

Measurement and Control

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The recognition of partial discharge mode is an important indicator of the insulation condition in transformers, based on which maintenance can be arranged. Discharge feature extraction is the key to recognize discharge mode. To solve the problem of poor stability and low recognition rate of partial discharge mode, this paper proposes a feature extraction method based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy. First, the four partial discharge signals collected under laboratory conditions are decomposed by synchrosqueezed windowed Fourier transform, then a number of band-limited intrinsic mode type functions are obtained, and the original feature quantities of partial discharge signals are obtained by calculating the multi-scale dispersion entropies of each intrinsic mode type function. Based on that, original feature quantity is optimized by using the maximum relevance and minimum redundancy criteria. Finally, the classification is implemented by the support vector machine. Experimental results show that in the case of noise interference, the proposed synchrosqueezed windowed Fourier transform–multi-scale dispersion entropy method can still accurately describe the feature of different discharge signals and has a higher recognition rate than both the empirical mode decomposition–multi-scale dispersion entropy method and the direct multi-scale dispersion entropy method.

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Section: Feature Extraction Process Based On Sswft–mdementioning

confidence: 99%

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

Sun

Wang

2020

Measurement and Control

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“…As shown in Equations (5) and (6), while the parameter a approaches to 0 + , TL1 (x) approaches L0; while the parameter a approaches to infinity, TL1 (x) approaches L1 [17]. Compared with the traditional convex regularization term, TL1 norm is unbiased and has sparse solution in the optimization of the loss functions.…”

Section: Non-convex Regularization In Loss Functionmentioning

confidence: 99%

The pattern recognition of multisource partial discharge in transformers based on parallel feature domain

Xia

Xie

et al. 2020

IET Science Measure & Tech

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The traditional diagnosis methods for the multisource partial discharge in transformer have several problems, such as the poor effect of signal separation, the inaccuracy of the extracted signal features and the over fitting of the neural network algorithm used for pattern recognition. In order to solve the above problems, the improved algorithm of stacked auto-encoder is adopted to extract the adaptive features of partial discharge one dimensional defect signals in time domain and two dimensional defect signal in time frequency domain, and a parallel feature space combining the features in time domain with that the time frequency domain is constructed. For the first time, the transformed L1 norm is utilized as the regular term of the loss function which is solved by the proximally guided stochastic subgradient in the improved auto-encoder. The trained neural network model can be utilized for pattern recognition of the different multisource partial discharge signals. The results show that the proposed method has a higher recognition rate for the different multisource partial discharge defects than that in the traditional method, and alleviates the problem of over fitting to a certain extent. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…In the long-term monitoring, these power values will appear frequently, thus clustering into different categories. Cluster analysis is a kind of unsupervised learning and is a method of exploring data structure [22]. Each data value in the improved AP algorithm is a potential class representative point, so low-power data can be identified even if the data set contains high-power data.…”

Section: Electrical Appliances Pattern Recognition Processmentioning

confidence: 99%

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

Han

et al. 2019

Energies

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The electric power industry is an essential part of the energy industry as it strengthens the monitoring and control management of household electricity for the construction of an economic power system. In this paper, a non-intrusive affinity propagation (AP) clustering algorithm is improved according to the factor graph model and the belief propagation theory. The energy data of non-intrusive monitoring consists of the actual energy consumption data of each electronic appliance. The experimental results show that this improved algorithm identifies the basic and combined class of home appliances. According to the possibility of conversion between different classes, the combination of classes is broken down into different basic classes. This method provides the basis for power management companies to allocate electricity scientifically and rationally.Energies 2019, 12, 992 2 of 20 power of different electrical appliances. The power company can obtain the usual information about the user's electricity, to make a scientific and reasonable decision. This method can reduce the cost of installation and reduce the level of interference in measurement [6]. Commonly used clustering methods for identifying appliances include the following: Hart first proposed non-intrusive household appliance load monitoring and analyzed the algorithm and characteristics, in this case, the essence of this method is to decompose the aggregated load data of household appliance [7]. After decades of research, many methods have been applied to pattern recognition of NILM. Some representative methods are K-means, K-nearest neighbor, enhanced ISODATA and artificial neural network [8][9][10][11]. These methods provide some ideas for identifying the operating mode of electrical appliances, but when there is a large number of home appliances, the recognition results are often not ideal. Frey and Dueck first proposed a standard AP clustering algorithm to solve the clustering problem [12]. The standard AP clustering algorithm can be derived from the factor graph model and the belief propagation theory [12,13]. The factor graph model, proposed by Kschischang and Frey, is a graph description method where the global function is decomposed into the product of the local function [14]. The theory of belief propagation was proposed by Pearl and is a kind of message transfer algorithm for inference in the graph model [15]. Since its publication, AP clustering algorithm has been applied in many fields because of its unique clustering characteristics.

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An efficient partial discharge pattern recognition method using texture analysis for transformer defect models

Cited by 11 publications

References 28 publications

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

Partial discharge feature extraction based on synchrosqueezed windowed Fourier transform and multi-scale dispersion entropy

The pattern recognition of multisource partial discharge in transformers based on parallel feature domain

Multi-Pattern Data Mining and Recognition of Primary Electric Appliances from Single Non-Intrusive Load Monitoring Data

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