TY - GEN
T1 - Data Driven Temperature Estimation of PMSM with Regression Models
AU - Sharma, Parul
AU - Vijayvargiya, Ankit
AU - Singh, Bharat
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Monitoring temperature parameters in Permanent Magnet Surface Machine (PMSM) is crucial since the machine possesses applications in several diverse areas, for instance, traction drives, electric vehicles, besides applications that require control of rotor temperature in order to ensure safety and cost-effectiveness. The thermal losses produced in the permanent magnet synchronous machine such as copper, iron, and mechan-ical loss and the cooling modes are responsible for temperature rise in the PMSM. The basic method to evaluate the temper-ature of internal components like Lumped Parameter Thermal Networks (LPTN) does not provide the degree of freedom in terms of choosing model parameters, physical comprehensibility, and real-time requirements. Hence, in this work a real-time data collected from the Germany laboratory is considered to perform the various machine learning techniques in which the effect of stator temperature, coolant temperature, and ambient temperature are contemplated to estimate the permanent magnet surface temperature. This paper has accomplished a data-driven temperature estimation of four regression models that are Linear Regression, Stochastic Gradient Regression, Random Sample Consensus (RANSAC) Regression, and Random Forest Regression training models by considering Mean Absolute Error (MAE), Mean Square Error (MSE), and R2-Score as measuring parameters and for unambiguous understanding, the predicted and the actual results are elaborated. This study is evident that Random Forest Regression model is proved to have the best outcomes out of all four regressoion models exercised in this research.
AB - Monitoring temperature parameters in Permanent Magnet Surface Machine (PMSM) is crucial since the machine possesses applications in several diverse areas, for instance, traction drives, electric vehicles, besides applications that require control of rotor temperature in order to ensure safety and cost-effectiveness. The thermal losses produced in the permanent magnet synchronous machine such as copper, iron, and mechan-ical loss and the cooling modes are responsible for temperature rise in the PMSM. The basic method to evaluate the temper-ature of internal components like Lumped Parameter Thermal Networks (LPTN) does not provide the degree of freedom in terms of choosing model parameters, physical comprehensibility, and real-time requirements. Hence, in this work a real-time data collected from the Germany laboratory is considered to perform the various machine learning techniques in which the effect of stator temperature, coolant temperature, and ambient temperature are contemplated to estimate the permanent magnet surface temperature. This paper has accomplished a data-driven temperature estimation of four regression models that are Linear Regression, Stochastic Gradient Regression, Random Sample Consensus (RANSAC) Regression, and Random Forest Regression training models by considering Mean Absolute Error (MAE), Mean Square Error (MSE), and R2-Score as measuring parameters and for unambiguous understanding, the predicted and the actual results are elaborated. This study is evident that Random Forest Regression model is proved to have the best outcomes out of all four regressoion models exercised in this research.
KW - estimation
KW - permanent magnet surface temperature
KW - PMSM
KW - regression model
KW - temperature
UR - http://www.scopus.com/inward/record.url?scp=85132145580&partnerID=8YFLogxK
U2 - 10.1109/ICPC2T53885.2022.9776991
DO - 10.1109/ICPC2T53885.2022.9776991
M3 - Conference contribution
AN - SCOPUS:85132145580
T3 - ICPC2T 2022 - 2nd International Conference on Power, Control and Computing Technologies, Proceedings
BT - ICPC2T 2022 - 2nd International Conference on Power, Control and Computing Technologies, Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Conference on Power, Control and Computing Technologies, ICPC2T 2022
Y2 - 1 March 2022 through 3 March 2022
ER -