Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Zeng, Fuping; Tang, Ju; Zhang, Xiaoxing; Zhou, Siyuan; Pan, Cheng

doi:10.5772/intechopen.79090

Cited by 19 publications

(14 citation statements)

References 19 publications

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“…Gas insulated apparatus has a complex structure that internal defects may happen during the processes of manufacturing, transferring, and installing [29]. The worldwide in‐service experiences have shown that the main defects in GIS/GIL are metal pollution on spacer surface, metal protrusion on electrode, free metal particles, spacer void and loose shields lead to floating electrode defect, as shown in Fig.…”

Section: Pd Detection With Optical Methodsmentioning

confidence: 99%

Optical sensing in condition monitoring of gas insulated apparatus: a review

et al. 2019

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Health monitoring of gas insulated apparatus is critical to improve the reliability and to reduce the life cycle cost. Among the various methods for monitoring, optical sensing developed fast in recent years due to their highly sensitive and antielectromagnetic interference behaviour. This study reviews the optical monitoring methods for partial discharge, gas concentration and temperature of gas insulted apparatus. In addition, the challenges of sensor development are carefully discussed here. The future prospects of optical monitoring of gas insulated apparatus are also analysed to inspire new ideas.

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Section: Pd Detection With Optical Methodsmentioning

confidence: 99%

Optical sensing in condition monitoring of gas insulated apparatus: a review

et al. 2019

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“…They can provide conductive local path and localized arcing which, in turn, would decrease the dielectric strength between high voltage conductor and outer enclosure. Factors like number of free particles, their size and shape, location (in vicinity of high/low field gradient) represent severity of PD pulses and their detectability [79]. The behavior of floating metals inside the GIS is similar to the corona discharge, in which case charges will accumulate at the sharp spots of the particle and PD would happen due to the electric field enhancement [80].…”

Section: ) Free-moving Metallic Particlesmentioning

confidence: 99%

A Review of Knowledge-Based Defect Identification via PRPD Patterns in High Voltage Apparatus

et al. 2021

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Partial discharge (PD) as one of well-known signatures of defects in the insulation system of high voltage equipment has been studied over eight decades. It is of a great interest to power grid operators and owners to identify and distinguish local weak spots in insulation system of the key components of power system by using the obtained PD information to make timely decision thus to avoid any unexpected failures in the system. This article reviews the research progress of PD in high voltage apparatus, including motors and generators, cables, transformers and gas insulated equipment, with attempts to assess the severity of different types of PD initiating defects as well as key PD features that can be selected for classifying defects. Experimental and on-site test results for each type of discharge and defect associated are summarized and discussed. Also, development of these defects during operation of equipment caused by changes in physical and chemical parameters of defect and/or insulation, and its impact on phaseresolved PD pattern variation are thoroughly explained. It is expected that this paper can provide a comprehensive guideline for PD data analysis of available PD information extracted by conventional PD acquisition instruments for both engineers and asset managers of high voltage apparatus. INDEX TERMSPartial Discharge (PD), high voltage motor, power cable, power transformer, GIS, GIL. Tohid Shahsavarian received the Bachelor's degree in Electrical Engineering from the University of Tabriz in 2011, and the Master's degree from the University of Science and Technology (IUST) in 2013. Then, he worked in the Department of Transmission and Distribution in Monenco Iran Consulting Engineers Company.He is currently working toward the Ph.D. degree at the Electrical and Computer Engineering Department of the University of Connecticut, and his research interests include the partial discharge analysis and risk assessment of AC and DC power cables, enhancement of insulation performance in electrical and electronic components for aerospace application, reliability analysis and protection of HVAC/HVDC grids, geomagnetic disturbances (GMD).

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“…However, GISs have internal defects during manufacture, transportation, and assembly [2]. In GISs, partial discharges (PDs) are not only the precursors of potential internal defects but also lead to gradual deterioration of insulation and ultimately break down or lead to surface discharge [3], [4]. Therefore, PD detection and diagnosis for GISs are required to ensure the safety and the reliability of power grid assets [5].…”

Section: Introductionmentioning

confidence: 99%

Deep Ensemble Model for Unknown Partial Discharge Diagnosis in Gas-Insulated Switchgears Using Convolutional Neural Networks

et al. 2021

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Deep neural networks (DNNs) are widely used for fault classification using partial discharges (PDs) to evaluate various electrical apparatuses and achieve high classification accuracy pertaining to trained PD faults. However, there is a risk of false alarm in the case of untrained PD faults because it is difficult for DNNs to predict data that were not included in the training process. In this paper, we research classification problems of unknown classes using PDs in gas-insulated switchgears (GISs) and propose a deep ensemble model to obtain the confidence of output probability and determine thresholds to detect unknown fault classes. The proposed model was verified by real-world phase-resolved PD (PRPD) experiments using online ultra-high frequency (UHF) PD measurement systems. The experimental results show that the proposed model achieves better unknown detection performance for the untrained PD faults and retains the classification performance for the trained PD faults.INDEX TERMS Fault diagnosis, convolutional neural network (CNN), ensemble model, partial discharges (PDs), gas-insulated switchgear (GIS).

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Typical Internal Defects of Gas-Insulated Switchgear and Partial Discharge Characteristics

Cited by 19 publications

References 19 publications

Optical sensing in condition monitoring of gas insulated apparatus: a review

Optical sensing in condition monitoring of gas insulated apparatus: a review

A Review of Knowledge-Based Defect Identification via PRPD Patterns in High Voltage Apparatus

Deep Ensemble Model for Unknown Partial Discharge Diagnosis in Gas-Insulated Switchgears Using Convolutional Neural Networks

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