Machine Learning for Sensorless Temperature Estimation of a BLDC Motor

Abstract

In this article, the authors propose two models for BLDC motor winding temperature estimation using machine learning methods. For the purposes of the research, measurements were made for over 160 h of motor operation, and then, they were preprocessed. The algorithms of linear regression, ElasticNet, stochastic gradient descent regressor, support vector machines, decision trees, and AdaBoost were used for predictive modeling. The ability of the models to generalize was achieved by hyperparameter tuning with the use of cross-validation. The conducted research led to promising results of the winding temperature estimation accuracy. In the case of sensorless temperature prediction (model 1), the mean absolute percentage error MAPE was below 4.5% and the coefficient of determination R2 was above 0.909. In addition, the extension of the model with the temperature measurement on the casing (model 2) allowed reducing the error value to about 1% and increasing R2 to 0.990. The results obtained for the first proposed model show that the overheating protection of the motor can be ensured without direct temperature measurement. In addition, the introduction of a simple casing temperature measurement system allows for an estimation with accuracy suitable for compensating the motor output torque changes related to temperature.

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“…close to 1 by RES or reactive power consumption by the inverter [50,51]. The developed results (data) will be used in the future to build machine learning for the purposes of power and voltage regulation in the LV grid [52][53][54].…”

Methods of Condition Monitoring and Fault Detection for Electrical Machines

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Influence of the Placement Accuracy of the Brushless DC Motor Hall Sensor on Inverter Transistor Losses

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