The Study on a New Method for Detecting Corona Discharge in Gas Insulated Switchgear

Lu, Binxian; Huang, W.; Xiong, Jun; Song, Lijuan; Zhang, Zhongyi; Dong, Qingyang

doi:10.1109/tim.2021.3129225

Cited by 19 publications

(5 citation statements)

References 26 publications

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“…In Fig. 3a, the discharge pulses of the corona fault [39] can be observed separately at both positive and negative half-cycles with high frequencies ranging from 255 to 315 degrees, slightly from 45 to 90 degrees, and in the vicinity of zero, which have a similar distribution to that of void discharge. However, void discharge pulses exhibit a more sparse and high pattern.…”

Section: B Prpd Measurements and Characterizationsmentioning

confidence: 88%

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Tuyet-Doan,

Youn,

Choi

et al. 2024

IEEE Access

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Recently, deep neural networks have shown remarkable success in fault diagnosis in power systems using partial discharges (PDs), thereby enhancing grid asset safety and reliability. However, the prevailing approaches often adopt centralized large-scale datasets for training, without taking into account the impact of noise environments for Intelligent Electronic Devices (IEDs). Noise environments for PD measurements in gas-insulated switchgear (GIS) introduce variations in feature distributions and class representations, challenging the generalization ability of the trained models in new and diverse conditions. In this study, we propose a Shared Knowledge-based Contrastive Federated Learning (SK-CFL) for PD diagnosis in different noise environments for IEDs. The proposed SK-CFL combines federated learning principles with contrastive learning, empowering IEDs to collaboratively learn and share knowledge as regards PD and noise patterns. The proposed framework can learn representations between the same patterns across different IEDs while ensuring data privacy. Experimental results for PD diagnosis in GIS show that the proposed SK-CFL achieves a performance improvement in fault diagnosis, particularly in new and unseen environments. Specifically, the recall for unknown noise in untrained IED 6 demonstrates 92.86% of the proposed SK-CFL, in comparison with 64.29% and 35.71% of the conventional FL and baseline method, respectively. These results suggest that the proposed SK-CFL approach promises more adaptable, and resilient data-driven approaches since it protects data privacy that can operate effectively in challenging real-world environments.

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Section: B Prpd Measurements and Characterizationsmentioning

confidence: 88%

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Tuyet-Doan,

Youn,

Choi

et al. 2024

IEEE Access

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“…Furthermore, when subjected to high-voltage stress, air, which contains electrically neutral molecules, acts as an insulator. When powerful energy travels as electrons from an electrode and strikes molecules, the molecules change [ 36 ].…”

Section: Theoretical Study and Methodsmentioning

confidence: 99%

“…While deep CNNs were initially used for image classification tasks, they have since been used for video analysis and recognition. However, the use of 1D sequence data for classification and prediction is relatively new [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 ]. The corona and non-corona categories can be seen as a sequential modeling effort, making 1D CNNs a suitable choice.…”

Section: Theoretical Study and Methodsmentioning

confidence: 99%

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Alsumaidaee

Yaw

Koh

et al. 2023

Sensors

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The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains.

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“…When the power system fails, the interconnected equipment can be stopped through an isolating switch, which protects the connected power equipment and the safety of power operators. In the process of manufacturing, distribution, installation, operation, and maintenance of the switch cabinet, various insulation defects will inevitably occur in the cabinet, such as metal burrs left on the surface of the conductor during manufacturing, loosening or poor contact performance of the parts during transportation, and residual metal particles in the cavity after maintenance. − These insulation defects are easy to cause electric field distortion during the operation of the switchgear, resulting in partial discharge and even arc. , Timely detection of insulation inside the switchgear is important to avoid huge economic losses and negative social impacts caused by safety accidents due to insulation damage in the cabinet. Many scholars have studied the discharge decomposition of SF 6 in GIS and judged the partial discharge of the equipment by analyzing the concentration, discharge volume, and gas production of the decomposed components .…”

Section: Introductionmentioning

confidence: 99%

Adsorption of a MoSe₂-Based Sensor for Fluorocarbon Gas Decomposition Products in Gas-Insulated Switchgear: A First Principles Analysis

Zhou

Liu

et al. 2023

J. Phys. Chem. C

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Gas-insulated switch cabinets play a significant role in the power industry, and eco-friendly switch cabinets are the hotspot of current development. With the research of alternative gases for SF6, fluorocarbon insulating gases possessing stable physicochemical properties provide a new insight for scientists. This work studies the sensing and adsorption properties of the Pt–MoSe2 monolayer for fluorocarbon gas characteristic decomposition products of COF2, CF4, and C2F6 in terms of stability, adsorption energy (E ad), charge transfer (Q T), band structure (BS), and density of states (DOS) as well as electronic structure based on first principles. The simulation results described Pt as an electron donor in the doping process on the MoSe2 monolayer with a binding force (E b) of −3.739 eV. The E ad values of the COF2, CF4, and C2F6 gas systems are −0.689, −0.176, and −0.266 eV, respectively. By comparing the BS and DOS of the adsorption system before and after, gas systems become more active, especially the COF2 system. Besides, gas molecules all play the role of an electron acceptor in the adsorption process and the Q T of the COF2 system is up to −0.259e. According to the analysis of this paper, Pt-doping can improve the sensing and adsorption properties of the MoSe2 monolayer and the monolayer shows different adsorption phenomena for three gases. The monolayer has a better adsorption effect on COF2, which provides a strong basis for the selective detection of gas sensors.

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The Study on a New Method for Detecting Corona Discharge in Gas Insulated Switchgear

Cited by 19 publications

References 26 publications

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Adsorption of a MoSe₂-Based Sensor for Fluorocarbon Gas Decomposition Products in Gas-Insulated Switchgear: A First Principles Analysis

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The Study on a New Method for Detecting Corona Discharge in Gas Insulated Switchgear

Cited by 19 publications

References 26 publications

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Shared Knowledge-Based Contrastive Federated Learning for Partial Discharge Diagnosis in Gas-Insulated Switchgear

Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods

Adsorption of a MoSe2-Based Sensor for Fluorocarbon Gas Decomposition Products in Gas-Insulated Switchgear: A First Principles Analysis

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Adsorption of a MoSe₂-Based Sensor for Fluorocarbon Gas Decomposition Products in Gas-Insulated Switchgear: A First Principles Analysis