An Approach to Estimate the Temperature of an Induction Motor under Nonlinear Parameter Perturbations Using a Data-Driven Digital Twin Technique

Yu Luo, Liguo Wang (), Denis Sidorov (), Aliona Dreglea and Elena Chistyakova
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Yu Luo: School of Electrical Engineering & Automation, Harbin Institute of Technology, Harbin 150001, China
Liguo Wang: School of Electrical Engineering & Automation, Harbin Institute of Technology, Harbin 150001, China
Denis Sidorov: School of Electrical Engineering & Automation, Harbin Institute of Technology, Harbin 150001, China
Aliona Dreglea: School of Electrical Engineering & Automation, Harbin Institute of Technology, Harbin 150001, China
Elena Chistyakova: Scientific Research Department, Irkutsk National Research Technical University, 664074 Irkutsk, Russia

Energies, 2024, vol. 17, issue 19, 1-16

Abstract: To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging.

Keywords: data-driven digital twin; temperature estimation; analytic solutions; induction motor; nonlinear differential equations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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