Development of switchgear life cycle cost analysis software

Loo, Yim Ling; Yaw, Chong Tak; Hashim, Amir Hisham; Mohamad, A.; Faizah, M.K.; Noormala, A.

doi:10.1109/peoco.2010.5559217

Cited by 1 publication

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“…M is the number of hidden neurons and set to 10,000 and number of data samples N, yielding Equation (2).…”

Section: Training Phasementioning

confidence: 99%

“…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%

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

et al. 2021

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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.

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“…M is the number of hidden neurons and set to 10,000 and number of data samples N, yielding Equation (2).…”

Section: Training Phasementioning

confidence: 99%