Partial discharge recognition through an analysis of SF<sub>6</sub> decomposition products part 2: feature extraction and decision tree-based pattern recognition

Tang, Ju; Liu, Fan; Meng, Qing-Hao; Zhang, Xiaoxing; Tao, Jiagui

doi:10.1109/tdei.2012.6148500

Cited by 129 publications

(98 citation statements)

References 16 publications

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“…In [8], four PD types are recognized in gas-insulated substations using decision trees, based on the relative concentrations of products of SF 6 gas decomposition due to partial discharges. Ma et al [9] compare the recognition performances of different combinations of artificial intelligence algorithms and input features.…”

mentioning

confidence: 99%

A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

Oliveira

Araújo

Barros

et al. 2017

J. Microw. Optoelectron. Electromagn. Appl.

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mentioning

confidence: 99%

A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

Oliveira

Araújo

Barros

et al. 2017

J. Microw. Optoelectron. Electromagn. Appl.

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“…Based on the three-ratio method in oil chromatographic analysis, C(SOF 2 )/C(SO 2 F 2 ), C(CF 4 )/C(CO 2 ), and C(SO 2 F 2 + SOF 2 )/C(CF 4 + CO 2 ) were selected as characteristic quantities to recognize the PD types [21,22]. These three concentration ratios have definite physical meaning.…”

Section: Concentration Ratios Of Sf 6 Decomposed Componentsmentioning

confidence: 99%

“…In [17][18][19], the SF6 decomposed products under arc, spark, and corona discharges were studied. Tang et al compared and analyzed the decomposition characteristics of SF6 under two types of common PDs [20], and recognized four typical PD types [21,22]. A large number of studies have shown that the type, concentration, and formation law of SF6 decomposed components are closely related to the type and severity of insulation faults in GIE.…”

Section: Introductionmentioning

confidence: 99%

Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions

Tang

Yang

et al. 2017

Energies

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Four typical types of artificial defects are designed in conducting the decomposition experiments of SF 6 gas to obtain and understand the decomposition characteristics of SF 6 gas-insulated medium under different types of negative DC partial discharge (DC-PD), and use the obtained decomposition characteristics of SF 6 in diagnosing the type and severity of insulation fault in DC SF 6 gas-insulated equipment. Experimental results show that the negative DC partial discharges caused by the four defects decompose the SF 6 gas and generate five stable decomposed components, namely, CF 4 , CO 2 , SO 2 F 2 , SOF 2 , and SO 2 . The concentration, effective formation rate, and concentration ratio of SF 6 decomposed components can be associated with the PD types. Furthermore, back propagation neural network algorithm is used to recognize the PD types. The recognition results show that compared with the concentrations of SF 6 decomposed components, their concentration ratios are more suitable as the characteristic quantities for PD recognition, and using those concentration ratios in recognizing the PD types can obtain a good effect.

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“…There are a variety of diagnostic approaches based on pulse current, acoustic, chemical, and ultra-high frequency (UHF) detection techniques [2][3][4][5][6][7][8][9][10]. However, each of these approaches can be performed effectively to some extent with its own limitations.…”

Section: Introductionmentioning

confidence: 99%

Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis

Li¹,

Tang²,

Li³

2015

Journal of Electrical Engineering and Technology

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-The integrity of the gas insulated switchgear (GIS) is vital to the safety of an entire power grid. However, there are some limitations on the techniques of detecting and diagnosing partial discharge (PD) induced by insulation defects in GIS. This paper proposes a joint electro-chemical detection method to resolve the problems of incomplete PD data source and also investigates a new unique fault diagnosis method to enhance the reliability of data processing. By employing ultra-high frequency method for online monitoring and the chemical method for detecting SF6 decomposition offline, the acquired data can form a more complete interpretation of PD signals. By utilizing DS evidence theory, the diagnostic results with tests on the four typical defects show the validity of the new fault diagnosis system. With higher accuracy and lower computation cost, the present research provides a promising way to make a more accurate decision in practical application.

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Partial discharge recognition through an analysis of SF₆ decomposition products part 2: feature extraction and decision tree-based pattern recognition

Cited by 129 publications

References 16 publications

A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions

Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis

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Partial discharge recognition through an analysis of SF6 decomposition products part 2: feature extraction and decision tree-based pattern recognition

Cited by 129 publications

References 16 publications

A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

A System Based on Artificial Neural Networks for Automatic Classification of Hydro-generator Stator Windings Partial Discharges

Decomposition Characteristics of SF6 and Partial Discharge Recognition under Negative DC Conditions

Application of Joint Electro-Chemical Detection for Gas Insulated Switchgear Fault Diagnosis

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Partial discharge recognition through an analysis of SF₆ decomposition products part 2: feature extraction and decision tree-based pattern recognition