Version 1
: Received: 12 June 2023 / Approved: 13 June 2023 / Online: 13 June 2023 (13:55:13 CEST)
How to cite:
Kluge, P.; Łasica, A.; Starzyński, J. Detection and Classification of Partial Discharge by Hybrid Neural Network Algorithms in Air-insulated MV Switchgear. Preprints2023, 2023060942. https://doi.org/10.20944/preprints202306.0942.v1
Kluge, P.; Łasica, A.; Starzyński, J. Detection and Classification of Partial Discharge by Hybrid Neural Network Algorithms in Air-insulated MV Switchgear. Preprints 2023, 2023060942. https://doi.org/10.20944/preprints202306.0942.v1
Kluge, P.; Łasica, A.; Starzyński, J. Detection and Classification of Partial Discharge by Hybrid Neural Network Algorithms in Air-insulated MV Switchgear. Preprints2023, 2023060942. https://doi.org/10.20944/preprints202306.0942.v1
APA Style
Kluge, P., Łasica, A., & Starzyński, J. (2023). Detection and Classification of Partial Discharge by Hybrid Neural Network Algorithms in Air-insulated MV Switchgear. Preprints. https://doi.org/10.20944/preprints202306.0942.v1
Chicago/Turabian Style
Kluge, P., Andrzej Łasica and Jacek Starzyński. 2023 "Detection and Classification of Partial Discharge by Hybrid Neural Network Algorithms in Air-insulated MV Switchgear" Preprints. https://doi.org/10.20944/preprints202306.0942.v1
Abstract
The correct classification of defects originating from partial discharges (PD) in medium-voltage (MV) switchgears with air insulation (AIS) remains a challenging research topic for scientists worldwide. In this article, the authors simulated four possible defects occurring in the power industry, including one that is a simultaneous combination of two commonly ones. In addition, the correctness of the algorithm was checked by adding a classification class without any fault. The measurement signals were recorded with TEV sensors. The effectiveness of various hy-brid-connected neural networks was tested and discussed: GoogleNet and SqueezeNet based on spectrograms, SAE with FNN, 2D-CNN with LSTM, and hybrid AE combined with CNN and LSTM. The highest effectiveness – approximately 97% – was demonstrated by the GoogleNet and SqueezeNet networks. The research results are expected to form the basis for the development of a universal and wireless capacitive sensor for monitoring the level of PD in switchgears.
Keywords
deep learning; partial discharge; convolutional neural network; medium voltage switchgear; air-insulated switchgear; autoencoder; long short-term memory
Subject
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
