A Convolutional Neural Network-Based Model for Multi-Source and Single-Source Partial Discharge Pattern Classification Using Only Single-Source Training Set

Sara Mantach, Ahmed Ashraf, Hamed Janani and Behzad Kordi
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Sara Mantach: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Ahmed Ashraf: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada
Hamed Janani: Verint Systems, Vancouver, BC V6E 4E6, Canada
Behzad Kordi: Department of Electrical & Computer Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada

Energies, 2021, vol. 14, issue 5, 1-16

Abstract: Classification of the sources of partial discharges has been a standard procedure to assess the status of insulation in high voltage systems. One of the challenges while classifying these sources is the decision on the distinct properties of each one, often requiring the skills of trained human experts. Machine learning offers a solution to this problem by allowing to train models based on extracted features. The performance of such algorithms heavily depends on the choice of features. This can be overcome by using deep learning where feature extraction is done automatically by the algorithm, and the input to such an algorithm is the raw input data. In this work, an enhanced convolutional neural network is proposed that is capable of classifying single sources as well as multiple sources of partial discharges without introducing multiple sources in the training phase. The training is done by using only single-source phase-resolved partial discharge (PRPD) patterns, while testing is performed on both single and multi-source PRPD patterns. The proposed model is compared with single-branch CNN architecture. The average percentage improvements of the proposed architecture for single-source PDs and multi-source PDs are 99.6% and 96.7% respectively, compared to 96.2% and 77.3% for that of the traditional single-branch CNN architecture.

Keywords: partial discharges; phase resolved partial discharge; insulation systems; automated pattern recognition; deep Learning; convolution neural network (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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