Study on the Partial Discharge Characteristics and Development Process in Use of the Multiple Discharge Patterns for the Typical Defects in Gas-Insulated Switchgear

Yao, Rui; Si, Gangquan; Yuan, Yiwei

doi:10.1109/cjece.2016.2594193

Cited by 9 publications

(4 citation statements)

References 39 publications

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“…Scholars such as Bartakova [14], Chu [15], and Van Brunt [16][17][18] explored the PD decomposition of SF 6 and the corresponding products under metal protrusion defects. Furthermore, an existing work [19] and our preliminary study [20,21] explored PD characteristics and SF 6 decomposition by four insulation defect models under AC, and established corresponding fault diagnosis methods. Van Brunt R.J. studied the SF 6 decomposition, bringing up 'Plasma Chemical Model' and the mechanism of SF 6 decomposition under negative DC by protrusion model [22,23].…”

Section: Introductionmentioning

confidence: 99%

SF ₆ positive DC partial discharge decomposition components under four typical insulation defects

Cao

Tang

Zeng

et al. 2018

IET Generation, Transmission & Distribution

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Section: Introductionmentioning

confidence: 99%

SF ₆ positive DC partial discharge decomposition components under four typical insulation defects

Cao

Tang

Zeng

et al. 2018

IET Generation, Transmission & Distribution

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“…UHF signals are extracted from OCU7 and OCU11. The phase of the signal has the characteristics of bipolarity and randomness, and the discharge signal had a relatively large amplitude [ 20 , 21 , 22 ], thus the type of discharge is floating discharge.…”

Section: Methodsmentioning

confidence: 99%

Improving the Error of Time Differences of Arrival on Partial Discharges Measurement in Gas-Insulated Switchgear

Jiang

Wang

Zhang

et al. 2018

Sensors

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Partial Discharge (PD) detection based on Ultra-High-Frequency (UHF) measurements in Gas-Insulated Switchgear (GIS) is often used for fault location based on extraction of Time Differences of Arrival (TDoA), and the core technique is to obtain the precise time difference of each UHF signal. Usually, TDoA extraction algorithms can be categorized as cross-correlation function method (CCF), minimum energy method (ME), and threshold value method (TV) are not qualified to analyze the time difference with high accuracy and efficiency, especially the complicated UHF PD signals in the field. In this paper, multiple tests were carried out based on the real GIS UHF signals. Three typical algorithms (CCF, ME, and TV) were used to extract and calculate the TDoA of UHF signals. Afterwards, depending on the disassembly of equipment, the accuracy and effective range of the algorithms are analyzed by means of error and variance. To minimize the error and the variance, an average method with the combination (CA) and portfolio of traditional algorithms is proposed and verified in different situations. The results demonstrate that the improved algorithm could increase the accuracy of time difference extraction, less than 4.0%.

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“…As one of the main manifestations of GIS insulation defects, the occurrence of PD will further aggravate the aging of GIS equipment. A large number of experiments have shown that the UHF method is insensitive to the detection of insulator surface contamination defects [28][29][30][31]. To this end, this paper selects four typical GIS PD defects other than the surface defects of insulator surface contamination for pattern recognition fault classification.…”

Section: Data Acquisitionmentioning

confidence: 99%

GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context

Wang

Yan

Yang

et al. 2020

IET sci. meas. technol.

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The construction of the ubiquitous power internet of things (UPIoT) provides a new feasible solution for gas‐insulated switchgear (GIS) online monitoring and fault diagnosis, but it also puts forward greater requirements for time and accuracy. How to find an effective real‐time model that can be applied to the UPIoT mobile terminals has become an urgent problem needing to be solved. To this end, this study proposes a lightweight convolutional neural network (LCNN) for GIS partial discharge (PD) pattern recognition using three lightweight convolutional blocks, and introduces the lowest recognition accuracy of single‐class faults as the primary indicator for selecting the optimal model under the UPIoT. First, three lightweight convolutional blocks are introduced for constructing an LCNN. Then, the optimal model constructed by the lightweight blocks is sought. Next, criteria for determining the best model are introduced, and the best model under the UPIoT is selected. This study provides a reference standard for the construction of GIS PD pattern recognition under the UPIoT. Meanwhile, through the balance of evaluation indicators, this study verifies that the minimum recognition accuracy of the MnasNet model is 98.8%, which is obviously better than other methods and lays a solid foundation for GIS PD pattern recognition.

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Study on the Partial Discharge Characteristics and Development Process in Use of the Multiple Discharge Patterns for the Typical Defects in Gas-Insulated Switchgear

Cited by 9 publications

References 39 publications

SF ₆ positive DC partial discharge decomposition components under four typical insulation defects

SF ₆ positive DC partial discharge decomposition components under four typical insulation defects

Improving the Error of Time Differences of Arrival on Partial Discharges Measurement in Gas-Insulated Switchgear

GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context

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Study on the Partial Discharge Characteristics and Development Process in Use of the Multiple Discharge Patterns for the Typical Defects in Gas-Insulated Switchgear

Cited by 9 publications

References 39 publications

SF 6 positive DC partial discharge decomposition components under four typical insulation defects

SF 6 positive DC partial discharge decomposition components under four typical insulation defects

Improving the Error of Time Differences of Arrival on Partial Discharges Measurement in Gas-Insulated Switchgear

GIS partial discharge pattern recognition via lightweight convolutional neural network in the ubiquitous power internet of things context

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SF ₆ positive DC partial discharge decomposition components under four typical insulation defects

SF ₆ positive DC partial discharge decomposition components under four typical insulation defects