Accurate knowledge of the induction machine rotor temperature is important for both condition monitoring and motor performance. Lumped-parameter thermal networks allow to flexibly model the temperature distribution with low computational effort. However, their accuracy highly depends on the accuracy of their thermal parameters. Moreover, these parameters are determined offline and held constant during operation. In this article, a rotor temperature virtual sensing strategy is proposed, combining a lumped-parameter thermal network with stator windings temperature measurements. The latter measurement data can be easily collected and together with the thermal network serve as input to a dual Kalman filter to estimate both the rotor temperature evolutions and the thermal model parameters. The advantage of the presented approach is that the thermal model does not need exact identification for the considered motor. One can start from a thermal model identified from intrusive rotor measurement data on a comparable motor that is subsequently transferred to the motor to be monitored. This results in a fast and convenient way to estimate the rotor temperature in real-time. Experimental results on a 4-pole 5.5 kW induction machine demonstrate the validity of the presented strategy to accurately monitor the rotor temperature within an error range of approximately ±6 °C.

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.

Squirrel‐cage induction machines (IMs) with indirect field‐oriented control are widely used in industry and are frequently chosen for their accurate and dynamic torque control. During operation, however, temperature rises leading to changes in machine parameters. The rotor resistance, in particular, alters, affecting the accuracy of the torque control. The authors investigated the effect of a rotor resistance parameter mismatch in the control algorithm on the angular rotor flux misalignment and the subsequent deviation of stator currents and motor torque from their setpoints. Hence, an online, data‐driven torque compensation to eliminate the temperature effect is proposed to enable robust torque‐controlled IMs. A model‐based analysis and experimental mapping of the temperature effect on motor torque is presented. A temperature‐torque lookup‐table is subsequently implemented within the control algorithm demonstrating the ability to reduce the detrimental effect of temperature on torque control. Experimental results on a 5.5 kW squirrel‐cage induction motor show that the proposed data‐driven online temperature compensation method is able to reduce torque mismatch when compared to having no temperature compensation. Up to 17% torque mismatch is reduced at nominal torque and even up to 23% at torque setpoints that are lower than 20% of the nominal torque. A limited torque error of <1% remains in a broad operating range.

Thermal modelling of induction machines is becoming increasingly important with the demand for machines with ever increasing efficiency as well as compact design. The lumped parameter thermal model is a flexible and computationally cheap method for the temperature analysis inside an induction machine. However, there are a number of thermal parameter values which are difficult to determine analytically. This includes the air gap convection coefficient, the equivalent radial conductivity of the stator winding and the width of the equivalent air gap between the frame and the stator lamination. In this work, the identification of the thermal model values follows the inverse methodology: assign values to the thermal parameters by aligning temperature measurements at a specific location in the motor with the lumped-parameter model response. Simulation results show that the accuracy of the proposed thermal parameter identification scheme depends on the location of the thermal measurement and more specifically on the sensitivity of the temperature profile with respect to the unknown parameters.

Discovery of natural laws through input-output data analysis has been of considerable interest during the past decade. Various approach among which the increasingly growing body of sparsity-based algorithms have been recently proposed for the purpose of free-form system identification. There has however been limited discussion on the performance of these approaches when applied on experimental datasets. The aim of this paper is to study the capability of this technique for identifying the heat equation as the natural law of heat distribution from experimental data, obtained using a Totally-Enclosed-Fan-Cooled (TEFC) induction machine, with and without active cooling. The results confirm the usefulness of the algorithm as a method to identify the underlying natural law in a physical system in the form of a Partial Differential Equation (PDE).