Power loss segregation in electrical machines through calorimetry and inverse thermal modelling

Abstract

This study combines calorimetric measurement of power loss of an induction motor with inverse thermal mapping of its temperatures to segregate the net power loss into its constituent components. Constrained least square fitting is the tool used to achieve the inverse mapping. The lumped thermal network of the motor serves as the forward thermal model, establishing the physical relationship between the motor's power losses and its resultant temperature rises. The objective of the iterative minimisation is to find the actual loss components that correspond to the temperatures measured, and which sum up to the total loss of the motor that is measured with a calorimeter. Data ranges and constraints are imposed to attain a reasonable, unique solution. Each loss component predicted by the inverse mapping inherently includes the contribution from stray load losses as it is fitted to the measured temperatures of the motor. Given the challenges, this inverse thermal routine achieves the loss segregation in a fairly good fashion.

Modeling and Analysis of Electromagnetic Field and Temperature Field of Permanent-Magnet Synchronous Motor for Automobiles

“…The temperature distribution of a permanent-magnet linear motor was analyzed by using the FEM and heat grid model under continuous working conditions to study the influence of the temperature rise on the motor thrust force and efficiency, but the influence of the electromagnetic field on the temperature field was ignored [13]. An inverse approach based on the machine's measured temperatures and its lumped thermal network was used to estimate the actual loss components in the motor [14]. However, the iron loss analyzed by the model was quite different from the test results.…”

Core loss determination in electric machines using short‐time transient thermal measurements

“…In contrast, inverse modelling techniques based on temperature measurements have also been used extensively for estimating core losses, as demonstrated in studies by [16][17][18][19][20], as well as for fault monitoring, as shown in [21,22]. However, the accuracy of the measured temperature plays a crucial role in determining the accuracy of the solution to an inverse problem.…”

“…The methods commonly used to measure temperature rise for inverse modelling applications can be categorised into three groups: (i) calorimetric measurements [20,[23][24][25], (ii) infrared thermographic measurements [18,19,26,27], and (iii) thermal sensor measurements with thermistors/thermocouples [28,29]. Each method has its advantages and disadvantages.…”

Determination of Core Losses Using an Inverse Modeling Technique

“…Therefore, by measuring the temperature rise at any point in a machine, the losses can be inversely determined. This principle was applied in [8]- [12] to estimate the power losses and thermal parameters of electrical machine. Calorimetric method described in [9], [11] was used to determine the losses directly from the heat dissipation of the machine.…”

“…Using the lumped parameter thermal network (LPTN) in combination with the experimental measured temperature rise, the net power losses in an induction machine were segregated in [12] by inverse thermal method. This approach was used in [10], [13], [14] to identify the thermal parameters used in real time prediction of stator and rotor temperature variations for condition motoring of electrical machine.…”