Thermal Lifetime Evaluation of Electrical Machines Using Neural Network

Turabee, G.; Khowja, M. Raza; Madonna, V.; Giangrande, P.; Vakil, G.; Gerada, C.; Galea, M.

Abstract

© 2020 IEEE. This paper proposes a surrogate approach which utilises an supervised neural network to significantly shorten the time required for thermal qualification of electrical machines' insulation. The proposed approach is based on a feedforward neural network trained with Bayesian Regularization Back-Propagation (BRP) algorithm. The network predicts the winding's insulation resistance trend with respect to its thermal aging time. The predicted insulation resistance is evaluated against experimental measurements and an excellent match is found. Its trend is used for estimating the sample's time to failure under thermal stress at various temperatures. The temperature index of the insulating material, predicted by the neural network, matches with an error of just 0.4% margin against the experimental findings.

• Conference Name: 2020 IEEE Transportation Electrification Conference & Expo (ITEC)
• Conference Location: Chicago, IL, USA
• Start Date: Jun 23, 2020
• End Date: Jun 26, 2020
• Acceptance Date: Feb 28, 2020
• Online Publication Date: Aug 7, 2020
• Publication Date: 2020-06
• Deposit Date: Jan 18, 2021
• Publicly Available Date: Jan 18, 2021
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1153-1158
• Series ISSN: 2377-5483
• Book Title: 2020 IEEE Transportation Electrification Conference & Expo (ITEC)
• ISBN: 9781728146294
• DOI: https://doi.org/10.1109/ITEC48692.2020.9161662
• Keywords: Insulation, Aging, Neural networks, Electric breakdown, Resistance, Temperature measurement, Thermal stresses
• Public URL: https://nottingham-repository.worktribe.com/output/5119132
• Publisher URL: https://ieeexplore.ieee.org/document/9161662