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The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines

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

Authors

Gulrukh Turabee

Muhammad Raza Khowja

Paolo Giangrande

Vincenzo Madonna

Georgina Cosma

Gaurang Vakil

Chris Gerada

Michael Galea



Abstract

© 2013 IEEE. For a continuous mode of operation, insulating material in an electrical machine is subject to constant thermal, electrical, mechanical and environmental stresses where thermal stress is a major cause of gradual insulation deterioration, which leads to ultimate winding failure. To guarantee a satisfactory lifetime, electrical machines are designed to operate winding temperatures well below their thermal class, which results in an oversized design. Standard methods for thermal lifetime evaluation of electrical machines are based on accelerated aging tests that require several months of testing. This paper proposes an alternative approach relying on a supervised neural network that significantly shortens the time demanded by accelerated aging tests for thermal lifetime evaluation of electrical machines. The supervised neural network is based on a feedforward neural network trained with Bayesian Regularisation Backpropagation (BRP) algorithm. The network predicts the wire insulation resistance with respect to its aging time at aging temperatures of 250°C, 270°C and 290°C, which reveals a good match of prediction outcomes against the experimental findings. The mean time-to-failure at each aging temperature is extracted using the Weibull probability plot in order to compare the Arrhenius curves for both conventional and proposed method and a relative error of 0.125% is achieved in terms of their temperature indexes. In addition, the analysis shows a time saving of 1680 hours (57% time saved of experimental test procedure) when the thermal life of the insulating material is predicted using BRP neural network.

Journal Article Type Article
Publication Date Feb 24, 2020
Journal IEEE Access
Electronic ISSN 2169-3536
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 8
Pages 40283-40297
APA6 Citation Turabee, G., Khowja, M. R., Giangrande, P., Madonna, V., Cosma, G., Vakil, G., …Galea, M. (2020). The Role of Neural Networks in Predicting the Thermal Life of Electrical Machines. IEEE Access, 8, 40283-40297. https://doi.org/10.1109/ACCESS.2020.2975985
DOI https://doi.org/10.1109/ACCESS.2020.2975985
Keywords General Engineering; General Materials Science; General Computer Science
Publisher URL https://ieeexplore.ieee.org/document/9007468

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