Evaluation of strand‐to‐strand capacitance and dissipation factor in thermally aged enamelled coils for low‐voltage electrical machines

Madonna, Vincenzo; Giangrande, Paolo; Galea, Michael

doi:10.1049/iet-smt.2019.0071

Cited by 23 publications

(20 citation statements)

References 33 publications

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“…Therefore, in the future it is likely that in addition to the constant temperature ageing tests, further tests will have to be carried out in order to take into account the mechanical stresses due to the rapid expansion and thermal compression of the windings' insulating enamel. This could be done by providing temperature profiles characterized by thermal gradients similar to those expected in the specific application [20], or with mechanical fatigue tests at defined operating temperatures.…”

Section: Discussionmentioning

confidence: 99%

Unconventional accelerated thermal ageing test for traction electric motors in vehicles

Lusuardi

Cavallini

Madonna

et al. 2020

2020 IEEE Electrical Insulation Conference (EIC)

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Car manufacturers are increasingly working to make the all-electric car a reliable and affordable mobility option for a larger section of the population. However, several issues need to be addressed before achieving this goal. One of the problems is represented by the qualification of the insulation systems employed in electric motors. Unfortunately, the tests to be performed are time consuming and the design of such measurements very often is done by trial and error, further lengthening the validation phase. In addition, since cars are exposed to variable torque and speed operations (especially when driven in city traffic), the stator winding temperature is expected to vary in a wide range. Nevertheless, indications regarding the impact that highly variable temperature conditions play on the insulation reliability are not available, at the moment. In this work, the results of accelerated thermal aging tests using temperature profiles (i.e. variable temperature ageing) are presented and discussed. The experiments are accomplished on simplified insulation systems for electric motor (i.e. specimens) and cycling temperature profiles, ranging between 200°C-260°C and featuring different thermal gradients, are applied as ageing stress. Finally, the possibility of relying on statistical techniques for improving the quality of prediction, while shortening the testing time, is explored.

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Section: Discussionmentioning

confidence: 99%

Unconventional accelerated thermal ageing test for traction electric motors in vehicles

Lusuardi

Cavallini

Madonna

et al. 2020

2020 IEEE Electrical Insulation Conference (EIC)

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“…Environmental factors, such as ambient temperature and relative humidity, also play a vital role in the measurement process of the diagnostic properties [43]. Thus, the diagnostic cycles must be carried out in a controlled environment i.e.…”

Section: B Environmental Factorsmentioning

confidence: 99%

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

et al. 2020

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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. INDEX TERMS Neural network, aging time, thermal life of insulation, accelerated lifetime test.

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“…However, in reality, this is not the case due to the non-idealities of the insulating material. Hence, the leakage current flows through the resistor R, indicating losses within the insulation [14]. The relationship of dissipation factor, in terms of insulation resistance and capacitance, can be obtained by applying the Pythagorean Theorem and Ohm's Law to the phasor diagram in Fig.…”

Section: A Diagnostic Propertymentioning

confidence: 99%

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

Turabee

Cosma

Madonna

et al. 2020

2020 International Joint Conference on Neural Networks (IJCNN)

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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 back propagation 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.

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Evaluation of strand‐to‐strand capacitance and dissipation factor in thermally aged enamelled coils for low‐voltage electrical machines

Cited by 23 publications

References 33 publications

Unconventional accelerated thermal ageing test for traction electric motors in vehicles

Unconventional accelerated thermal ageing test for traction electric motors in vehicles

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

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

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