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Abstract
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.
Highlights
Many different stresses degrade the life of the insulation system in an electrical machine
The operating temperature of a winding is the main cause of the thermal stress, which results from Joule losses plus additional heating due to core eddy currents
Since aerospace and transportation industries are shifting toward electrified solutions, quicker thermal qualification and faster manufacturing of electrical machines are becoming very important
Summary
Many different stresses degrade the life of the insulation system in an electrical machine. Insulation lifetime models, based on the Arrhenius laws, are suitable for evaluating the lifetime consumption of electrical machines operating with the continuous-duty cycle, where the winding temperature is mostly constant throughout the working period. The designer, acts on the design parameters so that the hot-spot temperature always remains below the thermal class of the adopted insulation [13], [14], which is provided by the manufacturer This ensures that the electrical machine will survive at least 20,000 hours of continuous operation [15]. The proposed approach employs the supervised feedforward neural network trained with Bayesian Regularisation Backpropagation (BRP) for the thermal qualification of electrical machine that reduces the experimental time of thermal aging tests while reaching good predictive accuracy. The predicted results from the neural network are validated and compared with the experimental measurements in order to evaluate the effectiveness of the proposed methodology
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