Parameter Identification Based Online Noninvasive Estimation of Rotor Temperature in Induction Motors

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Abstract
This article proposes a rotor temperature estimation method for in-service induction machine (IM) based on parameter identification, which combines the advantage of recursive least squares (RLS) and model reference adaptive system (MRAS). The RLS with forgetting factor is firstly adopted to identify the parameters of motor inductances. Then, the online identification of rotor resistance can be realized via the MRAS based on the instantaneous reactive power (IRP) and the proportional integral (PI) regulation adaptive law designed by the Popov hyper-stable theory. Thereby, the rotor temperature is computed by the resistance-temperature relationship of metals. To achieve a fast convergence of the parameter identification, rotor slot harmonics are extracted from the stator current and used to determine the rotor speed in real time. Furthermore, to obtain a more accurate initial value of the stator and rotor leakage inductances and resistances, the first 5-15 cycles of the IM starting process are used to mimic the locked-rotor test condition. A merit of the proposed method is that it requires significantly less time to estimate the rotor resistance than the traditional methods. With a novel test bench, experimental validation is performed on a 22-kW IM. In the test, the rotor temperature is measured in three different ways: 1) wireless sensors inserted in the rotating rotor core and rotor end rings, 2) infrared sensor for rotor end ring temperature, and 3) PT100 installed in stator end winding. With this test bench, the real rotor temperature was revealed, and the effectiveness of the presented method is also verified.
Funding Information
  • National Natural Science Foundation of China (51977077)
  • Natural Science Foundation of Jiangsu Province (BK20180283)
  • Fundamental Research Funds for the Central Universities (2020MS073)
  • China Scholarship Council
  • Florida International University
  • Energy Systems Research Laboratory
  • University Graduate School, Florida International University

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