1. 1 Neural plug-in motor coil thermal modeling Mo-Yuen Chow; Tipsuwan Y Industrial Electronics Society, 3000. IECON 26th Annual Conference of the IEEE, Volume: 3, 22-28 Oct.2000 Page(s): 1586-1591 vol.3. Presented by Suwatchai Kamonsantiroj

2. 2 Contents Introduction Introduction Background Knowledge Background Knowledge Experimental Set-up and Data gathering Experimental Set-up and Data gathering Modeling Approaches Modeling Approaches Result and Conclusion Result and Conclusion Discussion Discussion

3. 3 Introduction Winding heat losses are major factors in the design of motors. Winding heat losses are major factors in the design of motors. There are 2 popular way to protect motor winding thermal faults. There are 2 popular way to protect motor winding thermal faults. I. Thermal relaysII. Over-current relays So find a math model of the motor thermal. So find a math model of the motor thermal.

4. 4 Introduction (con’t) The models are classified into 3 groups. The models are classified into 3 groups. –Component-base ( take much computing power, time and use for off-line )

5. 5 Introduction (con’t) –Distributed parameters (partial differential form are solved by finite element take much computing power and time) –Lumped parameters (fast calculations and can be used for on-line application)

6. 6 Introduction (con’t) Therefore the propose is using the ANN model to increase the accuracy of Lumped- parameter model. Therefore the propose is using the ANN model to increase the accuracy of Lumped- parameter model. By using the ANN model learning the differences between the actual temperature and the temperature prediction from the Lumped-parameter model. By using the ANN model learning the differences between the actual temperature and the temperature prediction from the Lumped-parameter model.

7. 7 Background Knowledge The Motors The Motors

8. 8 Background Knowledge (con’t) Lumped-Parameter Heat Model Lumped-Parameter Heat Model –Heat transfer out from winding to stator core by conduction –Heat transfer out to the environment by convection Q in – Q out = Q store

9. 9 Background Knowledge (con’t) Feedforward Artificial Neural Networks Feedforward Artificial Neural Networks –Basic structure of a multi-layer feedforward ANN Summation function Activation function

10. 10 Feedforward ANN Example Feedforward ANN Example p=1 t=1.707 a1(1)=0.321 a1(2)=0.368 e=1.261 s2=-2.522 s1(1)=-0.049 s1(2)= 0.100 0.732 0.171 -0.077 -0.140 -0.475 -0.420 -0.265 a2=0.446

11. 11 Experimental Set-up and Data gathering A two-phase 10 Oh permanent magnet stepping motor. A two-phase 10 Oh permanent magnet stepping motor. The motor winding and corresponding schematic diagram. The motor winding and corresponding schematic diagram.

12. 12 Experimental Set-up and Data gathering (con’t) The winding temperature and core temperature were measured by RTDs. The winding temperature and core temperature were measured by RTDs. The ambient temperature is measured by IC sensor inside the NI system. The ambient temperature is measured by IC sensor inside the NI system. Three DC voltages 6,8, and 10 V were chosen as input voltage for 3600 seconds. Three DC voltages 6,8, and 10 V were chosen as input voltage for 3600 seconds.

13. 13 Modeling Approaches Three thermal model are compared. Three thermal model are compared. –Convention Lump-parameter. –Convention neural network. –Neural Plug-in Approach.

14. 14 Modeling Approaches (con’t) Conventional Lumped-Parameter. Conventional Lumped-Parameter. –The measurements are –The input of thermal model is

15. 15 Modeling Approaches (con’t) Convention neural network. Convention neural network. –The inputs to ANN are –The target outputs are –The ANN model have three layer with 10 hidden node. –The activation function at the hidden is hyperbolic tangent.

16. 16 Modeling Approaches (con’t) –The activation function at the output is linear function. –Training by Levenberg-Marquardt algorithms.

17. 17 Modeling Approaches (con’t) Neural plug-in Approach. Neural plug-in Approach. –The neural plug-in is learning the difference between the actual winding temperature and the predicted value from Lumped model. –The inputs to ANN are –The target outputs are

18. 18 Modeling Approaches (con’t) –The schematic diagram of the neural plug-in motor winding thermal modeling.

19. 19 Result and Conclusion The compare winding temperature of three motor thermal estimate model. The compare winding temperature of three motor thermal estimate model.

20. 20 Result (con’t) Modeling error in time domain. Modeling error in time domain.

21. 21 Result (con’t) Three norm measures. Three norm measures. The compare errors of 3 models The compare errors of 3 models

22. 22 Conclusion (con’t) The neural plug-in approach is superior than all. The neural plug-in approach is superior than all. The neural plug-in makes Lumped- parameter approach more accurate. The neural plug-in makes Lumped- parameter approach more accurate.

23. 23 Discussion The convention neural network approach is not as good as the others. The convention neural network approach is not as good as the others. –This paper do not change to the different factors such as the network size, the training method, etc. The winding temperature are raised by the only electric power. It don’t include heat rise from load torque. The winding temperature are raised by the only electric power. It don’t include heat rise from load torque.