{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:33:55Z","timestamp":1779384835575,"version":"3.53.1"},"reference-count":27,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T00:00:00Z","timestamp":1636329600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Doble","award":["18-135"],"award-info":[{"award-number":["18-135"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The reliability and health of bushings in high-voltage (HV) power transformers is essential in the power supply industry, as any unexpected failure can cause power outage leading to heavy financial losses. The challenge is to identify the point at which insulation deterioration puts the bushing at an unacceptable risk of failure. By monitoring relevant measurements we can trace any change that occurs and may indicate an anomaly in the equipment\u2019s condition. In this work we propose a machine-learning-based method for real-time anomaly detection in current magnitude and phase angle from three bushing taps. The proposed method is fast, self-supervised and flexible. It consists of a Long Short-Term Memory Auto-Encoder (LSTMAE) network which learns the normal current and phase measurements of the bushing and detects any point when these measurements change based on the Mean Absolute Error (MAE) metric evaluation. This approach was successfully evaluated using real-world data measured from HV transformer bushings where anomalous events have been identified.<\/jats:p>","DOI":"10.3390\/s21217426","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T22:08:41Z","timestamp":1636409321000},"page":"7426","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":24,"title":["Data-Driven Anomaly Detection in High-Voltage Transformer Bushings with LSTM Auto-Encoder"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9169-835X","authenticated-orcid":false,"given":"Imene","family":"Mitiche","sequence":"first","affiliation":[{"name":"Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tony","family":"McGrail","sequence":"additional","affiliation":[{"name":"Doble Engineering, Bere Regis BH20 7LA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Philip","family":"Boreham","sequence":"additional","affiliation":[{"name":"Doble Engineering, Bere Regis BH20 7LA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan","family":"Nesbitt","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronic Engineering, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gordon","family":"Morison","sequence":"additional","affiliation":[{"name":"Department of Computing, School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow G4 0BA, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Krzysztof, W., and Jaroslaw, G. (2021). Temperature Distribution in the Insulation System of Condenser-Type HV Bushing-Its Effect on Dielectric Response in the Frequency Domain. Energies, 14.","DOI":"10.3390\/en14134016"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1109\/MEI.2014.6749569","article-title":"Power Transformer Disruptions\u2014A Case Study","volume":"30","author":"Marques","year":"2014","journal-title":"IEEE Electr. Insul. Mag."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Subocz, J., Mrozik, A., Bohatyrewicz, P., and Zenker, M. (2020). Condition Assessment of HV Bushings with Solid Insulation based on the SVM and the FDS Methods. Energies, 13.","DOI":"10.3390\/en13040853"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kumar, M., and Rao, M.M. (2017). Online Condition Monitoring of High-Voltage Bushings Through Leakage Current Measurement. Int. J. Power Energy Syst., 203.","DOI":"10.2316\/Journal.203.2017.2.203-6360"},{"key":"ref_5","first-page":"47","article-title":"On-line Monitoring for Bushing of Power Transformer","volume":"8","author":"Suwnansri","year":"2004","journal-title":"GMSARN Int. J."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1442","DOI":"10.1016\/j.rser.2017.05.165","article-title":"Causes of Transformer Failures and Diagnostic Methods\u2014A Review","volume":"82","author":"Christina","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Badicu, L.-V., Broniecki, U., Koltunowicz, W., Subocz, J., Zenker, M., and Mrozik, A. (2016, January 25\u201328). Detection of bushing insulation defects by diagnostic monitoring. Proceedings of the International Conference on Condition Monitoring and Diagnosis (CMD), Xi\u2019an, China.","DOI":"10.1109\/CMD.2016.7757764"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"608","DOI":"10.1109\/TDEI.2006.1657975","article-title":"On-line Monitoring and Diagnoses of Power Transformer Bushings","volume":"13","author":"Setayeshmehr","year":"2006","journal-title":"IEEE Trans. Dielectr. Electr. Insul."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"IEEE Trans. Neural Comput."},{"key":"ref_10","unstructured":"Baldi, P. (July, January 26). Autoencoders, unsupervised learning, and deep architectures. Proceedings of the ICML Workshop on Unsupervised and Transfer Learning, Edinburgh, UK."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D Nonlinear Phenom."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"6481","DOI":"10.1109\/JIOT.2019.2958185","article-title":"Anomaly Detection for IoT Time-Series Data: A Survey","volume":"7","author":"Cook","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Bl\u00e1zquez-Garc\u00eda, A., Conde, A., Mori, U., and Lozano, J.A. (2020). A review on outlier\/anomaly detection in time series data. arXiv.","DOI":"10.1145\/3444690"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Breunig, M.M., Kriegel, H.-P., Ng, R.T., and Sander, J. (2000, January 16\u201318). LOF: Identifying Density-based Local Outliers. Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, TX, USA.","DOI":"10.1145\/342009.335388"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.knosys.2015.10.014","article-title":"A non-parameter outlier detection algorithm based on Natural Neighbor","volume":"92","author":"Huang","year":"2016","journal-title":"Knowl.-Based Syst."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/978-981-10-8636-6_34","article-title":"Development of an ARIMA Model for Monthly Rainfall Forecasting over Khordha District, Odisha, India","volume":"708","author":"Swain","year":"2018","journal-title":"Recent Find. Intell. Comput. Tech."},{"key":"ref_17","first-page":"161","article-title":"Vector Autoregressive Model-Based Anomaly Detection in Aviation Systems","volume":"13","author":"Melnyk","year":"2016","journal-title":"J. Aerosp. Inf. Syst."},{"key":"ref_18","unstructured":"Heathcote, M.J. (1992). Electrical Systems and Equipment, Pergamon. [3rd ed.]."},{"key":"ref_19","unstructured":"Lachman, M.F., Walter, W., and von Guggenberg, P.A. (1998, January 21\u201325). Experience with Application of Sum Current Method to On-Line Diagnostics of High-Voltage Bushings and Current Transformers. Proceedings of the Sixty-Fifth Annual International Conference of Doble Clients, Boston, MA, USA."},{"key":"ref_20","unstructured":"Lachman, M.F., Walter, W., and Skinner, J.S. (1999, January 12\u201316). Experience with On-line Diagnosis and Life Management of High-Voltage Bushings. Proceedings of the Sixty-Sixth Annual International Conference of Doble Clients, Boston, MA, USA."},{"key":"ref_21","unstructured":"Bahr, P., Christensen, J., and Brusetti, R.C. (2007, January 25\u201330). On-line Diagnostic Case Study Involving a General Electric Type U Bushing. Proceedings of the Seventy-Fourth Annual International Conference of Doble Clients, Boston, MA, USA."},{"key":"ref_22","unstructured":"Wancour, R., Molter, S., Brusetti, R.C., and Weatherbee, E. (April, January 29). Chronicling The Degradation of A 345kV General Electric Type U Bushing. Proceedings of the Seventy-Sixth Annual International Conference of Doble Clients, Boston, MA, USA."},{"key":"ref_23","unstructured":"Wyper, K., MacKay, G., and McGrail, T. (2013, January 7\u201312). Condition Monitoring in the Real World. Proceedings of the Eightieth Annual International Conference of Doble Clients, Boston, MA, USA."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1109\/72.279181","article-title":"Learning long-term dependencies with gradient descent is difficult","volume":"5","author":"Bengio","year":"1994","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Raeesy, R., Gillespie, K., Yang, Z., Ma, C., Drugman, T., Gu, J., Maas, R., Rastrow, A., and Hoffmeister, B. (2018). LSTM-based Whisper Detection. arXiv.","DOI":"10.1109\/SLT.2018.8639614"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_27","unstructured":"Kingma, D.P., and Ba, J. (2015). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7426\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:27:44Z","timestamp":1760167664000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/21\/7426"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,8]]},"references-count":27,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21217426"],"URL":"https:\/\/doi.org\/10.3390\/s21217426","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,8]]}}}