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Inverse thermal identification of a thermally instrumented induction machine using a lumped-parameter thermal model

Journal Contribution - Journal Article

Accurate temperature estimation inside an electrical motor is key for condition monitoring, fault detection, and enhanced end-of-life duration. Additionally, thermal information can benefit motor control to improve operational performance. Lumped-parameter thermal networks (LPTNs) for electrical machines are both flexible and cost-effective in computation time, which makes them attractive for use in real-time condition monitoring and integration in motor control. However, the accuracy of these thermal networks heavily depends on the accuracy of its system parameters, some of which are difficult to calculate analytically or even empirically and need to be determined experimentally. In this paper, a methodology for the thermal condition monitoring of long-duration transient and steady-state temperatures in an induction motor is presented. To achieve this goal, a computationally efficient second-order LPTN for a 5.5 kW squirrel-cage induction motor is proposed to apprehend the dominant heat paths. A fully thermally instrumented induction motor has been prepared to collect spatial and temporal temperature information. Using the experimental stator and rotor temperature data collected at different motor operating speeds and torques, the key thermal parameter values in the LPTN are identified by means of an inverse methodology that aligns the simulated temperatures of the stator windings and rotor with the corresponding measured temperatures. Validation results show that the absolute average thermal modelling error does not exceed 1.45 °C with maximum absolute error of 2.10 °C when the motor operates at fixed speed and torque. During intermittent motor-loading operation, a mean (maximum) stator temperature error of 0.38 °C (0.92 °C) was achieved and mean (maximum) rotor errors of 2.11 °C (3.40 °C). These results show the validity of the proposed thermal model but also its ability to predict in real time the temperature variations in stator and rotor for condition monitoring and motor control.
Journal: Energies
ISSN: 1996-1073
Issue: 1
Volume: 13
Number of pages: 1
Publication year:2020
Keywords:Energy & fuels