Studies on Contact Degradation Process and Failure Mechanism of GIB Plug-In Connector

Guan, Guan; Qin, Jingshu; Shu, Naiqiu; Peng, Hui

doi:10.1109/tcpmt.2019.2930415

Cited by 3 publications

(2 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many switchgear failures are caused by gradual degradation [1,2] of the devices, such as the insulators [3][4][5][6][7], switches [8][9][10][11][12][13], and connectors [14][15][16][17][18][19][20]. At the preliminary stage, these electrical faults, as shown in Figure 1, can produce noises that are detectable in the frequency range (20 kilohertz (kHz) to 100 kHz) by an ultrasonic detection system [21,22].…”

Section: Introductionmentioning

confidence: 99%

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

Currently, the existing condition-based maintenance (CBM) diagnostic test practices for ultrasound require the tester to interpret test results manually. Different testers may give different opinions or interpretations of the detected ultrasound. It leads to wrong interpretation due to depending on tester experience. Furthermore, there is no commercially available product to standardize the interpretation of the ultrasound data. Therefore, the objective is the correct interpretation of an ultrasound, which is one of the CBM methods for medium switchgears, by using an artificial neural network (ANN), to give more accurate results when assessing their condition. Information and test results from various switchgears were gathered in order to develop the classification and severity of the corona, surface discharge, and arcing inside of the switchgear. The ultrasound data were segregated based on their defects found during maintenance. In total, 314 cases of normal, 160 cases of the corona, 149 cases of tracking, and 203 cases of arcing were collected. Noise from ultrasound data was removed before uploading it as a training process to the ANN engine, which used the extreme learning machine (ELM) model. The developed AI-based switchgear faults classification system was designed and incorporated with the feature of scalability and can be tested and replicated for other switchgear conditions. A customized graphical user interface (GUI), Ultrasound Analyzer System (UAS), was also developed, to enable users to obtain the switchgear condition or classification output via a graphical interface screen. Hence, accurate decision-making based on this analysis can be made to prioritize the urgency for the remedial works.

show abstract

Section: Introductionmentioning

confidence: 99%

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Even overheating on several seriously deteriorated contact elements could induce melting, partial discharge and flashover of GIB equipment, then cause catastrophic consequences such as GIB equipment damage and power grid knock down [2][3]. Influenced by the pressure-vessel insulation design, and cyclic mechanical and current loads during GIB equipment operation, contact interface of the GIB plug-in connector is subject to complex current-carrying wear processes [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

et al. 2020

Self Cite

View full text Add to dashboard Cite

Plug-in connector of gas insulated bus (GIB) could be subject to the wear and electrical contact resistance (ECR) deterioration processes under cyclic mechanical and current loads. A specific test platform is designed to simulate the current-carrying wear and ECR degradation process of Ag-plated GIB electrical contact spot inside SF6, N2 and Air insulation mediums under current loads. Worn surface morphology and chemical composition are observed and analyzed. Influence of insulation medium and current load on deterioration mechanism of GIB electrical contact are discussed. Raw ECR curve obtained from current-carrying experiment is smoothed by the exponentially weighted moving average (EWMA) method. A long short-term memory (LSTM) neural network is then proposed to realize both single-step and multi-step ECR and failure predictions of GIB electrical contact. Results show that adhesive wear on contact surface happened due to materials transfer. Deterioration of contact spot inside air medium is much severer than those in SF6 and N2 due to oxidation effect. Deterioration of contact spot inside SF6 medium under high current load is severer than those under low current load due to the change of wear behavior and chemical reactions. Compared with other methods, LSTM could achieve better performance in both singlestep and multi-step failure prediction of GIB electrical contact. INDEX TERMS electrical contact resistance (ECR); gas insulated bus (GIB); insulation gases; LSTM.

show abstract

On-Line Fault Diagnosis for Structural Health of Wind Power Dedicated Busbar Trunking

Tian

et al. 2022

IEEE Trans. Instrum. Meas.

View full text Add to dashboard Cite

Studies on Contact Degradation Process and Failure Mechanism of GIB Plug-In Connector

Cited by 3 publications

References 23 publications

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Fault Classification System for Switchgear CBM from an Ultrasound Analysis Technique Using Extreme Learning Machine

Deterioration Behavior Analysis and LSTM-Based Failure Prediction of GIB Electrical Contact Inside Various Insulation Gases

On-Line Fault Diagnosis for Structural Health of Wind Power Dedicated Busbar Trunking

Contact Info

Product

Resources

About