Data-driven discovery of the heat equation in an induction machine via sparse regression

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)

Data-driven online temperature compensation for robust field-oriented torque-controlled induction machines

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

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

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

Inverse methodology for the parameter identification of a lumped parameter thermal network for an induction machine

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

A multi-channel temperature monitoring system for inverter-fed electrical machines

The demand for ever increasing efficiency keeps challenging the design and control of electrical machines. Thermal monitoring is in that perspective an important addition to the electromagnetic aspect. Temperature measurements in electrical machines can be challenging, especially in high-frequency inverter-fed motors. High dv/dt ratios in the stator windings give rise to noise exhibiting high amplitude and frequency. In this paper a multi-channel temperature monitoring system is proposed, implemented and experimentally tested for Resistance Temperature Detectors embedded at various locations in a 5.5 kW inverter-fed induction motor. The system ensures a galvanic isolation between the sensors in the motor and the data-acquisition system. After calibration, the linearity error, common-mode rejection ratio and signal-to-noise ratio are measured to be 0.12 % of full scale, -79.2 dB and 0.125 mV/V respectively. Fiber-Bragg Gratings thermal measurements are performed to confirm the accuracy of the proposed temperature monitoring system. For various operating conditions the temperature measurements have noise amplitudes that remain limited to 0.1 ° C-0.2 °C. The presented temperature monitoring system has the potential to further enhance motor performance when integrated into real-time motor controllers

Experimental identification of the induction machine frame convection coefficient for varying ventilator speeds

Thermal modeling of induction machines is becoming increasingly important to improve the operational efficiency and performance. Lumped-Parameter Thermal Networks are both flexible and suitable for online use, but their parameters must be well identified to obtain accurate temperature predictions. The convection coefficient between the motor frame and the environment is a particulary difficult parameter to obtain analytically because it summarizes complex fluid flows across the motor. In this paper, the frame convection coefficient and frame convection resistance of a 5.5 kW induction machine is determined experimentally for different external ventilator speeds. It is shown that the average frame convection coefficient increases for increasing ventilator speeds, whereas the convection resistance is inversely proportional to the fan speed. Furthermore, the local convection coefficient changes as function of the axial frame position due to a decreasing local air speed further away from the fan end