Predicting Insulation Resistance of Enamelled Wire using Neural Network and Curve Fit Methods Under Thermal Aging

Turabee, Gulrukh; Cosma, Georgina; Madonna, Vincenzo; Giangrande, Paolo; Khowja, Muhammad Raza; Vakil, Gaurang; Gerada, Chris; Galea, Michael

Abstract

© 2020 IEEE. Health monitoring has gained a massive interest in power systems engineering, as it has the advantage to reduce operating costs, improve reliability of power supply and provide a better service to customers. This paper presents surrogate methods to predict the electrical insulation lifetime using the neural network approach and three curve fitting models. These can be used for the health monitoring of insulating systems in electrical equipment, such as motors, generators, and transformers. The curve fit models and the supervised backpropagation neural network are employed to predict the insulation resistance trend of enameled copper wires, when stressed with a temperature of 290 °C. After selecting a suitable end of life criterion, the specimens' mean time-to-failure is estimated, and the performance of each of the analyzed models is apprised through a comparison with the standard method for thermal life evaluation of enameled wires. Amongst all, the best prediction accuracy is achieved by a Backpropagation neural network approach, which gives an error of just 3.29% when compared with the conventional life evaluation method, whereas, the error is above 10% for all the three investigated curve fit models.

Citation

Turabee, G., Cosma, G., Madonna, V., Giangrande, P., Khowja, M. R., Vakil, G., Gerada, C., & Galea, M. (2020, July). Predicting Insulation Resistance of Enamelled Wire using Neural Network and Curve Fit Methods Under Thermal Aging. Presented at 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, United Kingdom

• Presentation Conference Type: Edited Proceedings
• Conference Name: 2020 International Joint Conference on Neural Networks (IJCNN)
• Start Date: Jul 19, 2020
• End Date: Jul 24, 2020
• Acceptance Date: Apr 21, 2020
• Online Publication Date: Sep 28, 2020
• Publication Date: Jul 19, 2020
• Deposit Date: Nov 19, 2020
• Publicly Available Date: Nov 24, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-7
• Book Title: 2020 International Joint Conference onNeural Networks (IJCNN): 2020 conference proceedings
• ISBN: 9781728169262
• DOI: https://doi.org/10.1109/IJCNN48605.2020.9207378
• Public URL: https://nottingham-repository.worktribe.com/output/5011889
• Publisher URL: https://ieeexplore.ieee.org/document/9207378
• Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.