Partial Discharge Localization in Gas-Insulated Switchgear using Various Learning Algorithms

Abu-Rub, Omar H.; Darwish, Ahmad

doi:10.1109/sgre46976.2019.9020967

Cited by 4 publications

(4 citation statements)

References 10 publications

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“…The main contribution in this paper is also to improve the PD identification and classification in power cable insulation. The same experiment was conducted in [34], however, due to the complex nature of PDs and the high number of classes (number of different voids), an accuracy of approximately 77% was observed. In [5], the maximum classification accuracy acquired was 92.2% but with decreased number of classes.…”

Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 98%

“…In [5], the maximum classification accuracy acquired was 92.2% but with decreased number of classes. Therefore, this paper attempts to keep the same number of classes or voids investigated in [34] and maintain a reasonably high classification accuracy. This task is challenging due to a certain degree of similarities in the PD characteristics of the different voids [35].…”

Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 99%

“…This task is challenging due to a certain degree of similarities in the PD characteristics of the different voids [35]. Hence, in order to achieve this task, additional features were extracted other than the statistical and polarity-based distribution features used in [5] and [34]. The Weibull distribution features were introduced in this paper as well.…”

Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 99%

“…This is because these lines emulate the behavior of antennas, instigating noise. 4) Calibrating the signal to determine the size and type of defect is also difficult [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Classification of Defects in XLPE Power Cable Insulation via Machine Learning Algorithms

Saleh

Refaat

Khatri

et al. 2022

2022 3rd International Conference on Smart Grid and Renewable Energy (SGRE)

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Due to high electric stresses in power equipment, insulation degradation has been prevalent as a result of increased PD exposure. In this paper, we study different machine learning (ML) methods for the detection and classification of partial discharges (PDs) for assessing the reliability of insulation systems. We introduce and examine a set of features using selected machine learning-based algorithms. The aim is to detect and classify PDs transpiring within insulation systems. Therefore, this paper presents tools to detect defects using suitable PD sensors and Machine Learning algorithms to facilitate diagnostics and enhance isolation system design. Experiments are being conducted on several voids in the insulator with varying shapes and sizes. A PD sensor is used for detecting the PDs taking place. Due to the presence of noise and other external interferences, appropriate filters and denoising methods are implemented. After that, the relevant PD features, such as the PD magnitude, PD repetition rate, statistical features, wavelet features, etc., are extracted. This study attempts to emphasize the importance of classifying the type of defect, as this will allow engineers to determine the severity of the fault taking place, and take the proper countermeasures.

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Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 98%

Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 99%

Section: Applications Of Machine Learning In Pd Analysismentioning

confidence: 99%

“…This is because these lines emulate the behavior of antennas, instigating noise. 4) Calibrating the signal to determine the size and type of defect is also difficult [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations