Inverse Thermal Identification of a Thermally Instrumented Induction Machine Using a Lumped-Parameter Thermal Model

Abstract

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.

Lumped-Parameters Thermal Network of PM Synchronous Machines for Automotive Brake-by-Wire Systems

“…For instance, the computation of the thermal resistance between stator copper and stator core has to consider a heat path through an agglomerate of multiple materials with different thermal properties, while the thermal resistance between machine housing and the ambient areas comprises both convection and radiation phenomena. The literature reports dc tests that allow the calibration of such parameters [8,10,24,32]. It is interesting to observe that, even though for most of the applications those dc tests represent 'ad hoc' calibrating procedures, for the electric machine analyzed in this paper such supply conditions were also representative of the real machine operations.…”

An Improved LPTN Method for Determining the Maximum Winding Temperature of a U-Core Motor

“…Since all materials and dimensions of each block are known, the value of each thermal resistance is easily determined. The contact and convective thermal resistances can also be determined by empirical formulas [25] and identification method [26]. Their specific values are included in Appendix A.…”

Transient thermal models of induction machines under Inter‐turn short‐circuit fault conditions

“…Generally, FEM and CFD models are more computationally intensive but offers more details, thus they can be used to evaluate the performance of a new cooling design [19,20] or being embedded into optimisation procedures [21,22]. Whereas, the computation of LPTN models is usually instantaneous, thus it is suitable for tasks like condition monitoring [23,24]. The two approaches can even be combined together for taking advantage of both their merits in building the thermal models of induction machines [25,26].…”