Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning
Partial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time–frequency analysis (TFA) tec...
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MDPI AG
2025-05-01
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| Online Access: | https://www.mdpi.com/2076-3417/15/10/5455 |
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| author | Awad Almehdhar Radek Prochazka |
| author_facet | Awad Almehdhar Radek Prochazka |
| author_sort | Awad Almehdhar |
| collection | DOAJ |
| description | Partial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time–frequency analysis (TFA) techniques with deep learning (GoogLeNet for simulation, ResNet50 for experiments). PD data are generated through Finite Element Method (FEM) simulations and validated via laboratory experiments. The Scatter Wavelet Transform (SWT) achieves 96.67% accuracy (F1-score: 0.967) in simulation and perfect 100% accuracy (F1-score: 1.000) in experiments, outperforming other TFAs like HHT (70.00% experimental accuracy). The Wigner–Ville Distribution (WVD) also shows strong experimental performance (94.74% accuracy, AUC: 0.947), though its computational complexity limits real-time use. These results demonstrate the SWT’s superiority in handling real-world noise and multi-source PD signals, providing a robust framework for insulation diagnostics in power systems. |
| format | Article |
| id | doaj-art-d305d316872d4b278ff231e82ceb8a5e |
| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-d305d316872d4b278ff231e82ceb8a5e2025-08-20T02:33:39ZengMDPI AGApplied Sciences2076-34172025-05-011510545510.3390/app15105455Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep LearningAwad Almehdhar0Radek Prochazka1Faculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech RepublicFaculty of Electrical Engineering, Czech Technical University in Prague, Technická 2, 166 27 Prague, Czech RepublicPartial discharge (PD) analysis is critical for diagnosing insulation degradation in high-voltage equipment. While conventional methods struggle with multi-source PD classification due to signal overlap and noise, this study proposes a hybrid approach combining five time–frequency analysis (TFA) techniques with deep learning (GoogLeNet for simulation, ResNet50 for experiments). PD data are generated through Finite Element Method (FEM) simulations and validated via laboratory experiments. The Scatter Wavelet Transform (SWT) achieves 96.67% accuracy (F1-score: 0.967) in simulation and perfect 100% accuracy (F1-score: 1.000) in experiments, outperforming other TFAs like HHT (70.00% experimental accuracy). The Wigner–Ville Distribution (WVD) also shows strong experimental performance (94.74% accuracy, AUC: 0.947), though its computational complexity limits real-time use. These results demonstrate the SWT’s superiority in handling real-world noise and multi-source PD signals, providing a robust framework for insulation diagnostics in power systems.https://www.mdpi.com/2076-3417/15/10/5455partial dischargefinite element methodtime–frequency analysisdeep learningconvolutional neural networkhigh-voltage insulation |
| spellingShingle | Awad Almehdhar Radek Prochazka Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning Applied Sciences partial discharge finite element method time–frequency analysis deep learning convolutional neural network high-voltage insulation |
| title | Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning |
| title_full | Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning |
| title_fullStr | Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning |
| title_full_unstemmed | Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning |
| title_short | Classification of Multiple Partial Discharge Sources Using Time-Frequency Analysis and Deep Learning |
| title_sort | classification of multiple partial discharge sources using time frequency analysis and deep learning |
| topic | partial discharge finite element method time–frequency analysis deep learning convolutional neural network high-voltage insulation |
| url | https://www.mdpi.com/2076-3417/15/10/5455 |
| work_keys_str_mv | AT awadalmehdhar classificationofmultiplepartialdischargesourcesusingtimefrequencyanalysisanddeeplearning AT radekprochazka classificationofmultiplepartialdischargesourcesusingtimefrequencyanalysisanddeeplearning |