{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T18:23:43Z","timestamp":1783103023770,"version":"3.54.6"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,19]],"date-time":"2021-03-19T00:00:00Z","timestamp":1616112000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Partial discharge (PD) is a common indication of faults in power systems, such as generators and cables. These PDs can eventually result in costly repairs and substantial power outages. PD detection traditionally relies on hand-crafted features and domain expertise to identify very specific pulses in the electrical current, and the performance declines in the presence of noise or of superposed pulses. In this paper, we propose a novel end-to-end framework based on convolutional neural networks. The framework has two contributions: First, it does not require any feature extraction and enables robust PD detection. Second, we devise the pulse activation map. It provides interpretability of the results for the domain experts with the identification of the pulses that led to the detection of the PDs. The performance is evaluated on a public dataset for the detection of damaged power lines. An ablation study demonstrates the benefits of each part of the proposed framework.<\/jats:p>","DOI":"10.3390\/s21062154","type":"journal-article","created":{"date-parts":[[2021,3,21]],"date-time":"2021-03-21T23:47:41Z","timestamp":1616370461000},"page":"2154","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Interpretable Detection of Partial Discharge in Power Lines with Deep Learning"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6882-2906","authenticated-orcid":false,"given":"Gabriel","family":"Michau","sequence":"first","affiliation":[{"name":"Swiss Federal Institute of Technology, ETH Z\u00fcrich, 8093 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6919-8882","authenticated-orcid":false,"given":"Chi-Ching","family":"Hsu","sequence":"additional","affiliation":[{"name":"Swiss Federal Institute of Technology, ETH Z\u00fcrich, 8093 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9546-1488","authenticated-orcid":false,"given":"Olga","family":"Fink","sequence":"additional","affiliation":[{"name":"Swiss Federal Institute of Technology, ETH Z\u00fcrich, 8093 Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1109\/TIE.2017.2721922","article-title":"Combined fault location and classification for power transmission lines fault diagnosis with integrated feature extraction","volume":"65","author":"Chen","year":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"24","DOI":"10.1109\/MEI.2006.1678355","article-title":"Medium voltage cable defects revealed by off-line partial discharge testing at power frequency","volume":"22","author":"Mashikian","year":"2006","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.1109\/TPAS.1969.292445","article-title":"Partial-discharge measurement on high-voltage power transformers","volume":"88","author":"Kawaguchi","year":"1969","journal-title":"IEEE Trans. Power Appar. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1971","DOI":"10.1109\/TDEI.2012.6396955","article-title":"Realization of partial discharge signals in transformer oils utilizing advanced computational techniques","volume":"19","author":"Ibrahim","year":"2012","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1109\/MEI.2019.8735667","article-title":"Partial discharge detection and diagnosis in gas insulated switchgear: State of the art","volume":"35","author":"Khan","year":"2019","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"McGreevy, D., Giussani, R., Seltzer-Grant, M., Singh, A., Patel, A., Calladine, S., and Gibb, G. (2017, January 16\u201318). Deployment of an online partial discharge monitoring system for power station with focus on gas turbine generators. Proceedings of the 2017 INSUCON-13th International Electrical Insulation Conference, Birmingham, UK.","DOI":"10.23919\/INSUCON.2017.8097188"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1109\/TDEI.2017.006135","article-title":"A complex classification approach of partial discharges from covered conductors in real environment","volume":"24","author":"Fulnecek","year":"2017","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1109\/TDEI.2005.1430399","article-title":"Partial discharge signal interpretation for generator diagnostics","volume":"12","author":"Hudon","year":"2005","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1109\/T-DEI.2008.4446759","article-title":"Knowledge-based diagnosis of partial discharges in power transformers","volume":"15","author":"Strachan","year":"2008","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1109\/TII.2019.2915685","article-title":"Feasibility Study on Simultaneous Detection of Partial Discharge and Axial Displacement of HV Transformer Winding Using Electromagnetic Waves","volume":"16","author":"Karami","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Raymond, W.J.K., Illias, H.A., and Abu Bakar, A.H. (2017). Classification of partial discharge measured under different levels of noise contamination. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0170111"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Dong, M., Sun, Z., and Wang, C. (2019, January 5\u20138). A pattern recognition method for partial discharge detection on insulated overhead conductors. Proceedings of the 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE), Edmonton, AB, Canada.","DOI":"10.1109\/CCECE.2019.8861809"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3277","DOI":"10.1109\/TIE.2019.2908580","article-title":"A Novel Application of Deep Belief Networks in Learning Partial Discharge Patterns for Classifying Corona, Surface, and Internal Discharges","volume":"67","author":"Karimi","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_14","unstructured":"Li, G., Rong, M., Wang, X., Li, X., and Li, Y. (2016, January 25\u201328). Partial discharge patterns recognition with deep Convolutional Neural Networks. Proceedings of the 2016 International Conference on Condition Monitoring and Diagnosis (CMD), Xi\u2019an, China."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Banno, K., Nakamura, Y., Fujii, Y., and Takano, T. (2018, January 23\u201326). Partial Discharge Source Classification for Switchgears with Transient Earth Voltage Sensor Using Convolutional Neural Network. Proceedings of the 2018 Condition Monitoring and Diagnosis (CMD), Perth, Australia.","DOI":"10.1109\/CMD.2018.8535913"},{"key":"ref_16","unstructured":"Wang, G., Yang, F., Peng, X., Wu, Y., Liu, T., and Li, Z. (2018, January 6\u20139). Partial discharge pattern recognition of high voltage cables based on the stacked denoising autoencoder method. Proceedings of the 2018 International Conference on Power System Technology (POWERCON), Guangzhou, China."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Nguyen, M.T., Nguyen, V.H., Yun, S.J., and Kim, Y.H. (2018). Recurrent neural network for partial discharge diagnosis in gas-insulated switchgear. Energies, 11.","DOI":"10.3390\/en11051202"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"106318","DOI":"10.1016\/j.epsr.2020.106318","article-title":"Partial discharge detection on aerial covered conductors using time-series decomposition and long short-term memory network","volume":"184","author":"Dong","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"87060","DOI":"10.1109\/ACCESS.2020.2992790","article-title":"Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge","volume":"8","author":"Qu","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1109\/MEI.2015.7303259","article-title":"An overview of state-of-the-art partial discharge analysis techniques for condition monitoring","volume":"31","author":"Wu","year":"2015","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Barrios, S., Buldain, D., Comech, M.P., Gilbert, I., and Orue, I. (2019). Partial discharge classification using deep learning methods\u2014Survey of recent progress. Energies, 12.","DOI":"10.3390\/en12132485"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"389","DOI":"10.1016\/j.compbiomed.2017.08.022","article-title":"A deep convolutional neural network model to classify heartbeats","volume":"89","author":"Acharya","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Susto, G.A., Cenedese, A., and Terzi, M. (2018). Time-Series Classification Methods: Review and Applications to Power Systems Data. Big Data Application in Power Systems, Elsevier.","DOI":"10.1016\/B978-0-12-811968-6.00009-7"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1007\/s10618-019-00619-1","article-title":"Deep learning for time series classification: A review","volume":"33","author":"Fawaz","year":"2019","journal-title":"Data Min. Knowl. Discov."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Zeng, Q., Pan, H., Chen, B., and Liao, Z. (2019, January 12\u201315). Research on STLF Method Based on One-Dimensional Convolution and Slope Feature. Proceedings of the 2019 IEEE International Conference on Power Data Science (ICPDS), Taizhou, China.","DOI":"10.1109\/ICPDS47662.2019.9017181"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.ijinfomgt.2018.08.006","article-title":"Real-Time Big Data Processing for Anomaly Detection: A Survey","volume":"45","author":"Habeeb","year":"2019","journal-title":"Int. J. Inf. Manag."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.jvcir.2016.11.003","article-title":"Understanding convolutional neural networks with a mathematical model","volume":"41","author":"Kuo","year":"2016","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_28","unstructured":"ENET Centre at VSB\u2014Technical University of Ostrava (2020, July 04). VSB Power Line Fault Detection. Available online: https:\/\/www.kaggle.com\/c\/vsb-power-line-fault-detection."},{"key":"ref_29","first-page":"536","article-title":"On the theory of filter amplifiers","volume":"7","author":"Butterworth","year":"1930","journal-title":"Wirel. Eng."},{"key":"ref_30","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_32","unstructured":"Lin, M., Chen, Q., and Yan, S. (2013). Network in network. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A. (2016, January 27\u201330). Learning deep features for discriminative localization. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.319"},{"key":"ref_34","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1016\/0005-2795(75)90109-9","article-title":"Comparison of the predicted and observed secondary structure of T4 phage lysozyme","volume":"405","author":"Matthews","year":"1975","journal-title":"Biochim. Biophys. Acta BBA Protein Struct."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2154\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:37:57Z","timestamp":1760161077000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/6\/2154"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,19]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["s21062154"],"URL":"https:\/\/doi.org\/10.3390\/s21062154","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,19]]}}}