Feature extraction and recognition of UHF partial discharge signals in GIS based on dual‐tree complex wavelet transform

Tang, Ju; Zhou, Qian

doi:10.1002/etep.345

Cited by 13 publications

(9 citation statements)

References 27 publications

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“…This indicates that the proposed classification helps one to classify the discharges in GIS operated at different pressures and voltages. Xie et al [29] classified corona discharge, surface discharge, and particle movement by extracting the features from the UHF signal generated by the discharges by adopting a dual-tree complex wavelet transform. The efficiency of classification process is up to 92%.…”

Section: Selection Of Optimal Rbf Kernel Parametersmentioning

confidence: 99%

Identification of Partial Discharges in Gas-insulated Switchgear by Ultra-high-frequency Technique and Classification by Adopting Multi-class Support Vector Machines

Umamaheswari

Sarathi

2011

Electric Power Components and Systems

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Ultra-high-frequency signals are generated due to particle movement, floating conductors, corona, and surface discharges in gas-insulated switchgear. The ultra-high-frequency signal generated due to particle movement is independent of operating pressure and applied voltage. The bandwidth of the ultra-high-frequency signal formed due to corona and surface discharges vary with applied voltage. In a ternary plot, each type of discharge has a unique location. If a variety of discharge occurs simultaneously, it is difficult to classify the type of discharges through the ternary plot. It is demonstrated that the type of discharge can be classified efficiently by adopting a multi-class support vector machine.

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Section: Selection Of Optimal Rbf Kernel Parametersmentioning

confidence: 99%

Identification of Partial Discharges in Gas-insulated Switchgear by Ultra-high-frequency Technique and Classification by Adopting Multi-class Support Vector Machines

Umamaheswari

Sarathi

2011

Electric Power Components and Systems

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“…This method has the benefit to effectively capture the amplitude and frequency variations in non-stationary signals compared to existing signal decomposition methods such as Empirical Mode Decomposition (EMD) [16]. PD signals are reported to be non-stationary [17], thus the obtained IMFs by ALIF may help to efficiently retrieve the unique time-frequency characteristics of each EMI event. Permutation Entropy (PE) is popular in biomedical application for electrocardiogram-type signals analysis and classification [18].…”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

Mitiche

Morison

Nesbitt

et al. 2018

Sensors

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Electromagnetic Interference (EMI) is a technique for capturing Partial Discharge (PD) signals in High-Voltage (HV) power plant apparatus. EMI signals can be non-stationary which makes their analysis difficult, particularly for pattern recognition applications. This paper elaborates upon a previously developed software condition-monitoring model for improved EMI events classification based on time-frequency signal decomposition and entropy features. The idea of the proposed method is to map multiple discharge source signals captured by EMI and labelled by experts, including PD, from the time domain to a feature space, which aids in the interpretation of subsequent fault information. Here, instead of using only one permutation entropy measure, a more robust measure, called Dispersion Entropy (DE), is added to the feature vector. Multi-Class Support Vector Machine (MCSVM) methods are utilized for classification of the different discharge sources. Results show an improved classification accuracy compared to previously proposed methods. This yields to a successful development of an expert’s knowledge-based intelligent system. Since this method is demonstrated to be successful with real field data, it brings the benefit of possible real-world application for EMI condition monitoring.

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“…The first is based on the statistical regularities of the discharge on power cycles, such as figures in two or three dimensions [2][3][4][5], while the second is based on the real-time characteristics of PD signals [6]. The former reflects better on PD's physical features.…”

Section: Introductionmentioning

confidence: 99%

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

Tang¹,

Zhuo²,

Wang³

et al. 2016

Journal of Electrical Engineering and Technology

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-With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultrahigh frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

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Feature extraction and recognition of UHF partial discharge signals in GIS based on dual‐tree complex wavelet transform

Cited by 13 publications

References 27 publications

Identification of Partial Discharges in Gas-insulated Switchgear by Ultra-high-frequency Technique and Classification by Adopting Multi-class Support Vector Machines

Identification of Partial Discharges in Gas-insulated Switchgear by Ultra-high-frequency Technique and Classification by Adopting Multi-class Support Vector Machines

Classification of Partial Discharge Signals by Combining Adaptive Local Iterative Filtering and Entropy Features

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

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