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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. [ABSTRACT FROM AUTHOR]

To control the current of a surface mounted permanent magnet synchronous machine fed by a two-level voltage source inverter, a large variety of control algorithms exists. Each of these controllers performs differently concerning dynamic performance and control- and voltage quality, but also concerning sensitivity to demagnetization faults. Therefore, this paper investigates the performance degradation of three advanced predictive controllers under a partial demagnetization fault. The three predictive controllers are: finite-set model based predictive control, deadbeat control, and a combination of both previous algorithms. To achieve this goal, the three predictive controllers are first compared under healthy conditions, and afterwards under a partial demagnetization fault. A PI controller is added to the comparison in order to provide a model-independent benchmark. Key performance indicators, obtained from both simulations and experimental results on a 4 kW axial flux permanent magnet synchronous machine with yokeless and segmented armature topology, are introduced to enable a quantification of the performance degradation of the controllers under a demagnetization fault. A general conclusion is that the deadbeat controller shows superior control quality, even under partial demagnetization. [ABSTRACT FROM AUTHOR]

In this paper, the effect of some geometrical parameters on the steady state average temperature of the stator core, the winding and the permanent magnets of the yokeless and segmented armature (YASA) axial flux permanent magnet synchronous machine (AFPMSM) is studied. The geometrical parameters selected for the study are the air gap length, the inward heat extraction fin thickness and the permanent magnet thickness. These parametric studies make it possible to obtain a better trade-off between power density and efficiency. These investigations are very helpful in correlating the values of the geometrical parameters to some specific desired performance criteria like not going below some desired minimum efficiency, limiting the temperature of specific part to some maximum value for maximization of lifetime and also determination of the allowed speed range to limit the temperatures lower than the critical values. This is important specifically for the synchronous machines due to the fact that the speed value affects both the losses and the heat transfer convection coefficients. The air gap length has a direct effect on the overall machine losses and the air gap convection coefficient and hence on the temperature of the machine. As the fins are between the stator windings, a thicker fin reduces the space for copper windings and hence increases the losses, but at the same time improves heat evacuation. In addition, the effect on the temperature is studied of the speed variation, which influences both the losses and the convection coefficients of the machine. Every study is made based on coupled electromagnetic and thermal models. The results are obtained from analytical electromagnetic and thermal models verified by finite element simulations and validated experimentally on a 4 kW yokeless and segmented armature axial flux machine. [ABSTRACT FROM AUTHOR]

To find the temperature rise for high power density yokeless and segmented armature (YASA) axial flux permanent magnet synchronous (AFPMSM) machines quickly and accurately, a 3D lumped parameter thermal model is developed and validated experimentally and by finite element (FE) simulations on a 4 kW YASA machine. Additionally, to get insight in the thermal transient response of the machine, the model accounts for the thermal capacitance of different machine components. The model considers the stator, bearing, and windage losses, as well as eddy current losses in the magnets on the rotors. The new contribution of this work is that the thermal model takes cooling via air channels between the magnets on the rotor discs into account. The model is parametrized with respect to the permanent magnet (PM) angle ratio, the PM thickness ratio, the air gap length, and the rotor speed. The effect of the channels is incorporated via convection equations based on many computational fluid dynamics (CFD) computations. The model accuracy is validated at different values of parameters by FE simulations in both transient and steady state. The model takes less than 1 s to solve for the temperature distribution. [ABSTRACT FROM AUTHOR]