1. Alqtish, M., Di Fatta, A., Rizzo, G., Akbar, G., Vigni, L., Imburgia, A., . . . Romano, P. (2025). A Review of Partial Discharge Electrical Localization Techniques in Power Cables: Practical Approaches and Circuit Models. Energies. doi:10.3390/en18102583 [ DOI:10.3390/en18102583] 2. Balouji, E., Hammarström, T., & McKelvey, T. (2022). Classification of Partial Discharges Originating From Multilevel PWM Using Machine Learning. IEEE Transactions on Dielectrics and Electrical Insulation, 29, 287-294. doi:10.1109/tdei.2022.3148461 [ DOI:10.1109/TDEI.2022.3148461] 3. Banjare, H. K., Sahoo, R., & Karmakar, S. (2022). Study and Analysis of Various Partial Discharge Signals Classification Using Machine Learning Application. 2022 IEEE 6th International Conference on Condition Assessment Techniques in Electrical Systems (CATCON), 52-56. doi:10.1109/CATCON56237.2022.10077703 [ DOI:10.1109/CATCON56237.2022.10077703] 4. Carvalho, I., Da Costa, E. G., Nobrega, L., & Silva, A. (2024). Identification of Partial Discharge Sources by Feature Extraction from a Signal Conditioning System. Sensors (Basel, Switzerland), 24. doi:10.3390/s24072226 [ DOI:10.3390/s24072226] 5. Chan, J. Q., Raymond, W., Illias, H., & Othman, M. (2023). Partial Discharge Localization Techniques: A Review of Recent Progress. Energies. doi:10.3390/en16062863 [ DOI:10.3390/en16062863] 6. Chang, C.-K., & Chang, H.-H. (2023). Learning Entirely Unknown Classes in Time-Series Data Using Convolutional Neural Networks for Insulation Status Assessment of Partial Discharges in Power Cable Joints. IEEE Transactions on Dielectrics and Electrical Insulation, 30, 2854-2861. doi:10.1109/TDEI.2023.3280441 [ DOI:10.1109/TDEI.2023.3280441] 7. Chen, W., Yang, Z., Song, J., Zhou, L., Xiang, L., Wang, X., . . . Fan, X. (2024). A High-Resolution Defect Location Method for Medium-Voltage Cables Based on Gaussian Narrow-Band Envelope Signals and the S-Transform. Energies, 17(9), 2218. Retrieved from https://www.mdpi.com/1996-1073/17/9/2218. [ DOI:10.3390/en17092218] 8. C. M. (2024). RF Mudule user's guide. In: COMSOL. 9. Da Silva, M., De Araújo, O., De Oliveira, D., & Lopes, R. (2024). Evaluation of the Effects of Voids in Electrical Cables Using COMSOL Multiphysics Software. IEEE Transactions on Dielectrics and Electrical Insulation, 31, 2144-2150. doi:10.1109/TDEI.2024.3395232 [ DOI:10.1109/TDEI.2024.3395232] 10. Dhandapani, R. (2024). Application of Signal Processing Techniques in High Voltage Equipment Fault Detection Using Partial Discharge Signal. doi:10.59019/vtjd9568 [ DOI:10.59019/VTJD9568] 11. Florkowski, M. (2021). Anomaly Detection, Trend Evolution, and Feature Extraction in Partial Discharge Patterns. Energies. doi:10.3390/en14133886 [ DOI:10.3390/en14133886] 12. Haiba, A., & Halawa, M. (2024). Design of partial discharge measurement model for internal cavities in high voltage power cables. Journal of King Saud University - Engineering Sciences. doi:10.1016/j.jksues.2024.02.002 [ DOI:10.1016/j.jksues.2024.02.002] 13. Hassan, W., Shafiq, M., Hussain, G., Choudhary, M., & Palu, I. (2023). Investigating the progression of insulation degradation in power cable based on partial discharge measurements. Electric Power Systems Research. doi:10.1016/j.epsr.2023.109452 [ DOI:10.1016/j.epsr.2023.109452] 14. Ishaq, A., Junaid, M., Hussain, G. A., Khan, S. U., Chen, Y., & Yu, D. (2025). Partial Discharge Defect Classification in MV Switchgear by Using CWT and Deep Learning Approach. IEEE Transactions on Instrumentation and Measurement, 74, 1-12. doi:10.1109/TIM.2025.3562981 [ DOI:10.1109/TIM.2025.3562981] 15. Janani, H., Shahabi, S., & Kordi, B. (2020). Separation and Classification of Concurrent Partial Discharge Signals Using Statistical-Based Feature Analysis. IEEE Transactions on Dielectrics and Electrical Insulation, 27, 1933-1941. doi:10.1109/TDEI.2020.009043 [ DOI:10.1109/TDEI.2020.009043] 16. Kim, J., & Kim, K.-I. (2021). Partial Discharge Online Detection for Long-Term Operational Sustainability of On-Site Low Voltage Distribution Network Using CNN Transfer Learning. Sustainability, 13, 4692. doi:10.3390/SU13094692 [ DOI:10.3390/su13094692] 17. Kumar, H., Shafiq, M., Kauhaniemi, K., & Elmusrati, M. (2024). A Review on the Classification of Partial Discharges in Medium-Voltage Cables: Detection, Feature Extraction, Artificial Intelligence-Based Classification, and Optimization Techniques. Energies. doi:10.3390/en17051142 [ DOI:10.3390/en17051142] 18. Li, A., Wei, G., Li, S., Zhang, J., & Zhang, C.-Y. (2024). Pattern Recognition of Partial Discharge in High-Voltage Cables Using TFMT Model. IEEE Transactions on Power Delivery, 39, 3326-3337. doi:10.1109/TPWRD.2024.3465660 [ DOI:10.1109/TPWRD.2024.3465660] 19. Li, X., Ding, D., Xu, Y., Jiang, J., Chen, X., & Yuan, M. (2024). A Denoising Method for Partial Discharge Ultrasonic Signals in GIS Based on Ultra-High Frequency Signal Synchronization. IEEE Transactions on Power Delivery, 39, 3316-3325. doi:10.1109/TPWRD.2024.3465506 [ DOI:10.1109/TPWRD.2024.3465506] 20. Lu, S., Chai, H., Sahoo, A., & Phung, B. (2020). Condition Monitoring Based on Partial Discharge Diagnostics Using Machine Learning Methods: A Comprehensive State-of-the-Art Review. IEEE Transactions on Dielectrics and Electrical Insulation, 27, 1861-1888. doi:10.1109/TDEI.2020.009070 [ DOI:10.1109/TDEI.2020.009070] 21. Michau, G., Hsu, C.-C., & Fink, O. (2021). Interpretable Detection of Partial Discharge in Power Lines with Deep Learning. Sensors (Basel, Switzerland), 21. doi:10.3390/s21062154 [ DOI:10.3390/s21062154] 22. Qin, C., Zhu, X., Zhu, P., Lin, W., Liu, L., Che, C., . . . Hua, H. (2024). Partial Discharge Signal Pattern Recognition of Composite Insulation Defects in Cross-Linked Polyethylene Cables. Sensors (Basel, Switzerland), 24. doi:10.3390/s24113460 [ DOI:10.3390/s24113460] 23. Rathod, V., Kumbhar, G., & Bhalja, B. (2020). Simulation of Partial Discharge Acoustic Wave Propagation Using COMSOL Multiphysics and Its Localization in a Model Transformer Tank. 2020 21st National Power Systems Conference (NPSC), 1-6. doi:10.1109/NPSC49263.2020.9331915 [ DOI:10.1109/NPSC49263.2020.9331915] 24. Saad, M. H., Hashima, S., Omar, A. I., Fouda, M. M., & Said, A. (2025). Deep learning approach for cable partial discharge pattern identification. Electrical Engineering, 107(2), 1525-1540. doi:10.1007/s00202-024-02571-w [ DOI:10.1007/s00202-024-02571-w] 25. Sabarshad, O., & Akbari, A. (2025). Advanced detection of multiple PD sources in cables using time-frequency transformations. Electric Power Systems Research, 246, 111699. doi: [ DOI:10.1016/j.epsr.2025.111699] 26. Sahoo, R., & Karmakar, S. (2024). Effectiveness of Wavelet Scalogram on Partial Discharge Pattern Classification of XLPE Cable Insulation. IEEE Transactions on Instrumentation and Measurement, 73, 1-10. doi:10.1109/TIM.2024.3363790 [ DOI:10.1109/TIM.2024.3363790] 27. Sun, C., Wu, G., Pan, G., Zhang, T., Li, J., Jiao, S., . . . Gao, G. (2024). Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination. Sensors (Basel, Switzerland), 24. doi:10.3390/s24082660 [ DOI:10.3390/s24082660] 28. Tian, J., Song, H., Sheng, G., & Jiang, X. (2022). Knowledge-Driven Recognition Methodology of Partial Discharge Patterns in GIS. IEEE Transactions on Power Delivery, 37, 3335-3344. doi:10.1109/tpwrd.2021.3128036 [ DOI:10.1109/TPWRD.2021.3128036] 29. Wang, Y.-B., Chang, D.-G., Qin, S.-R., Fan, Y.-H., Mu, H., & Zhang, G. (2020). Separating Multi-Source Partial Discharge Signals Using Linear Prediction Analysis and Isolation Forest Algorithm. IEEE Transactions on Instrumentation and Measurement, 69, 2734-2742. doi:10.1109/tim.2019.2926688 [ DOI:10.1109/TIM.2019.2926688] 30. Zhang, X., Pang, B., Liu, Y., Liu, S.-Y., Xu, P., Li, Y., . . . Xie, Q. (2021). Review on Detection and Analysis of Partial Discharge along Power Cables. Energies. doi:10.3390/en14227692 [ DOI:10.3390/en14227692] 31. Zhong, J., Bi, X., Shu, Q., Chen, M., Zhou, D., & Zhang, D. (2020). Partial Discharge Signal Denoising Based on Singular Value Decomposition and Empirical Wavelet Transform. IEEE Transactions on Instrumentation and Measurement, 69, 8866-8873. doi:10.1109/TIM.2020.2996717 [ DOI:10.1109/TIM.2020.2996717]
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