Ultra-High Frequency (UHF) Based Partial Discharge Measurement in Gas Insulated Switchgear (GIS)

Rao, Mandava Mohana; Kumar, M.Sarath

doi:10.1109/ichvet.2019.8724361

Cited by 4 publications

(3 citation statements)

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“…To prevent this excessive current quickly and timely from causing unpredictable damage, gas-insulated switchgears (GIS) are used, which are electromechanical devices consisting of high-voltage components, such as circuit breakers and disconnectors. Current is blocked in a circuit automatically when a potential spike is detected in power grids [1]. However, GISs have internal defects during manufacture, transportation, and assembly [2].…”

Section: Introductionmentioning

confidence: 99%

“…There are several methods to detect PDs, such as ultra-high frequency (UHF), current induction, and acoustic emission [7]- [9]. The UHF method measures electromagnetic signals within the frequency range of 300-3000 MHz and has been widely used owing to the advantages of high sensitivity, immunity to external interference, ability to locate PD sources, and recognize fault types [1]. Therefore, this study utilizes the UHF method for the PD measurement system [5].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

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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“…Various faults that cause an insulation breakdown of a GIS can be detected while using partial discharges (PDs) before insulation breakdown [ 2 ]. Therefore, detecting PDs in a GIS is necessary to ensure the safety and reliability of grid assets [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear

Tuyet-Doan

Tran-Thi

et al. 2020

Sensors

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In recent years, deep learning has been successfully used in order to classify partial discharges (PDs) for assessing the condition of insulation systems in different electrical equipment. However, fault diagnosis using deep learning is still challenging, as it requires a large amount of training data, which is difficult and expensive to obtain in the real world. This paper proposes a novel one-shot learning method for fault diagnosis using a small dataset of phase-resolved PDs (PRPDs) in a gas-insulated switchgear (GIS). The proposed method is based on a Siamese network framework, which employs a distance metric function for predicting sample pairs from the same PRPD class or different PRPD classes. Experimental results over the small PRPD dataset that was obtained from an ultra-high-frequency sensor in the GIS show that the proposed method achieves outstanding performance for PRPD fault diagnosis as compared with the previous methods.

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Detection and Localization of Partial Discharge in Switchgear using Online Partial Discharge Identification

Muhammad,

Rahiman,

Majid

et al. 2024

2024 20th IEEE International Colloquium on Signal Processing &Amp;amp; Its Applications (CSPA)

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Ultra-High Frequency (UHF) Based Partial Discharge Measurement in Gas Insulated Switchgear (GIS)

Cited by 4 publications

References 0 publications

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

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

One-Shot Learning for Partial Discharge Diagnosis Using Ultra-High-Frequency Sensor in Gas-Insulated Switchgear

Detection and Localization of Partial Discharge in Switchgear using Online Partial Discharge Identification

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