New diagnosis method of unknown phase-shifted PD signals for gas insulated switchgears

Kim, Sung-wook; Jung, Jae-Ryong; Kim, Young‐Min; Kil, Gyung-Suk; Wang, Guo‐Ming

doi:10.1109/tdei.2018.006739

Cited by 13 publications

(14 citation statements)

References 13 publications

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“…The internal UHF sensor is used with a frequency range of 0.5 GHz to 1.5 GHz and a sensitivity of -14.5 dBm at 5 pC. Verification was performed by CIGRE TF 15/33.03.05 [7].…”

Section: On-site Noise and Prpd Measurementsmentioning

confidence: 99%

“…For on-site PRPD measurements, 215 cases of on-site PD data were collected from 2003 to 2015 [7]. Figs.…”

Section: B On-site Prpd Measurementsmentioning

confidence: 99%

“…A gasinsulated switchgear (GIS), applied to substations, is a major protection device for electric power facilities. A GIS is a valuable device in protecting, controlling, and isolating the equipment in a power grid in the case of an incident (e.g., power surge) [7], [8]. Internal defects can occur in a GIS during the process of transfer, installation, and operation [7].…”

Section: Introductionmentioning

confidence: 99%

“…A GIS is a valuable device in protecting, controlling, and isolating the equipment in a power grid in the case of an incident (e.g., power surge) [7], [8]. Internal defects can occur in a GIS during the process of transfer, installation, and operation [7]. When a failure occurs in a GIS, the impact of the accident is huge so recovery takes a lot of time and the power outage also increases.…”

Section: Introductionmentioning

confidence: 99%

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Anomaly Detection for Partial Discharge in Gas-Insulated Switchgears Using Autoencoder

Thi

Jung

et al. 2020

IEEE Access

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In this paper, we propose a new anomaly detection method to detect the partial discharge in a gas-insulated switchgear. An autoencoder was used for anomaly detection and was modeled on the one-class classification problem. Based on the one-class classification scenario, in which the training data exploited the noise data only, the proposed autoencoder learned the low-dimensional latent information from the high-dimensional space of the input signal. Then, the reconstruction error was used as a fault indicator, and the threshold was determined using the partial discharge data. The performance of the proposed AE was verified by on-site noise and PRPD experiments, using an online UHF PD monitoring system in the realworld environment. The results showed that the proposed autoencoder not only achieved 86.75% detection performance for the on-site noise and partial discharge data in gas-insulated switchgears but also allowed better detection performance than the one-class support vector machine learning procedure by 40.5%.INDEX TERMS Partial discharge (PD), fault detection, gas-insulated switchgear (GIS), anomaly detection, autoencoder (AE).

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“…The internal UHF sensor is used with a frequency range of 0.5 GHz to 1.5 GHz and a sensitivity of -14.5 dBm at 5 pC. Verification was performed by CIGRE TF 15/33.03.05 [7].…”

Section: On-site Noise and Prpd Measurementsmentioning

confidence: 99%

“…For on-site PRPD measurements, 215 cases of on-site PD data were collected from 2003 to 2015 [7]. Figs.…”

Section: B On-site Prpd Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Anomaly Detection for Partial Discharge in Gas-Insulated Switchgears Using Autoencoder

Thi

Jung

et al. 2020

IEEE Access

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“…Analyzing the classification performance of a DNN-based classifier is as necessary for untrained data as that for trained data. In addition, acquiring all the types of faults for trained data is a challenging task for PRPD fault diagnosis in GIS as the fault types are very diverse on-site [30]. Consequently, a fault that is not a part of the training data may occur in onsite measurements.…”

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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Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge

Sun,

Ma,

Wang

2024

IEEE Access

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Partial discharge (PD) is one of the main reasons of insulation deterioration in gas insulated switchgear (GIS). How to efficiently and accurately identify PD signals is an important guarantee for the stable operation of GIS. In this paper, an improved northern goshawk optimization (SCNGO) is proposed, which automatically optimizes parameters of variational mode decomposition (VMD) and support vector machine (SVM) to realize fault identification of GIS PD. Firstly, to overcome the shortcomings that NGO is easy to fall into local optimal solution and slow convergence speed, the opposite learning of refraction strategy and sine cosine algorithm (SCA) are introduced to optimize NGO. By comparing the test functions of various algorithms, the superiority of SCNGO algorithm is proved. Then, GIS PD experiment is designed for fault signal acquisition and algorithm verification. SCNGO-VMD is used for parameter adaptive optimization of PD signals. On this basis, the effective intrinsic mode functions (IMFs) are screened by composite index. Furthermore, time-domain, frequency-domain, and entropy features are constructed as mixed features and t-SNE is used to reduce the dimension. Finally, the feature vectors are input to SCNGO-SVM for fault identification. Through experimental analysis, compared with other algorithms, the proposed algorithm model has good state identification accuracy for GIS PD fault diagnosis. The paper provides a reference for the application of optimization algorithm in GIS PD fault identification .

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New diagnosis method of unknown phase-shifted PD signals for gas insulated switchgears

Cited by 13 publications

References 13 publications

Anomaly Detection for Partial Discharge in Gas-Insulated Switchgears Using Autoencoder

Anomaly Detection for Partial Discharge in Gas-Insulated Switchgears Using Autoencoder

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

Application of SCNGO-VMD-SVM in Identification of Gas Insulated Switchgear Partial Discharge

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