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Neural plug-in Neural plug-in motor coil thermal motor coil thermal

modelingmodeling

Mo-Yuen Chow; Tipsuwan YMo-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 byPresented bySuwatchai KamonsantirojSuwatchai Kamonsantiroj

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ContentsContents

IntroductionIntroduction Background KnowledgeBackground Knowledge Experimental Set-up and Data Experimental Set-up and Data

gatheringgathering Modeling ApproachesModeling Approaches Result and ConclusionResult and Conclusion DiscussionDiscussion

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IntroductionIntroduction

Winding heat losses are major factors in Winding heat losses are major factors in the design of motors.the design of motors.

There are 2 popular way to protect motor There are 2 popular way to protect motor winding thermal faults.winding thermal faults.I. Thermal relaysI. Thermal relays II. Over-current relaysII. Over-current relays

So find a math model of the motor So find a math model of the motor thermal.thermal.

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Introduction Introduction (con’t)(con’t)

The models are classified into 3 The models are classified into 3 groups.groups.– Component-base Component-base ( take much computing ( take much computing

power, time and use for off-line )power, time and use for off-line )

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Introduction Introduction (con’t)(con’t)

– Distributed parameters Distributed parameters (partial (partial differential form are solved by finite differential form are solved by finite element take much computing power element take much computing power and time)and time)

– Lumped parameters Lumped parameters (fast calculations (fast calculations and can be used for on-line application)and can be used for on-line application)

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Introduction Introduction (con’t)(con’t)

Therefore the propose is using the Therefore the propose is using the ANN model to increase the accuracy ANN model to increase the accuracy of Lumped-parameter model.of Lumped-parameter model.

By using the ANN model learning the By using the ANN model learning the differences between the actual differences between the actual temperature and the temperature temperature and the temperature prediction from the Lumped-prediction from the Lumped-parameter model.parameter model.

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Background KnowledgeBackground Knowledge

The MotorsThe Motors

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Background Knowledge Background Knowledge (con’t)(con’t) Lumped-Parameter Heat ModelLumped-Parameter Heat Model

– Heat transfer out from Heat transfer out from winding to stator core by winding to stator core by conductionconduction

– Heat transfer out to the Heat transfer out to the environment by environment by convectionconvection

QQinin – Q – Qoutout = Q = Qstorestore

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Background Knowledge Background Knowledge (con’t)(con’t) Feedforward Artificial Neural NetworksFeedforward Artificial Neural Networks

– Basic structure of a multi-layer feedforward ANN Basic structure of a multi-layer feedforward ANN

Summation functionSummation function Activation functionActivation function

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Feedforward ANN ExampleFeedforward ANN Example

p=1

t=1.707

a1(1)=0.321

a1(2)=0.368

e=1.261s2=-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

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Experimental Set-up and Experimental Set-up and Data gatheringData gathering

A two-phase 10 Oh permanent magnet A two-phase 10 Oh permanent magnet stepping motor.stepping motor.

The motor winding and corresponding The motor winding and corresponding schematic diagram.schematic diagram.

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Experimental Set-up and Experimental Set-up and Data gathering Data gathering (con’t)(con’t) The winding temperature and core The winding temperature and core

temperature were measured by RTDs.temperature were measured by RTDs.

The ambient temperature is measured The ambient temperature is measured by IC sensor inside the NI system. by IC sensor inside the NI system.

Three DC voltages 6,8, and 10 V were Three DC voltages 6,8, and 10 V were chosen as input voltage for 3600 chosen as input voltage for 3600 seconds.seconds.

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Modeling ApproachesModeling Approaches

Three thermal model are compared.Three thermal model are compared.

– Convention Lump-parameter.Convention Lump-parameter.

– Convention neural network.Convention neural network.

– Neural Plug-in Approach.Neural Plug-in Approach.

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Modeling Approaches Modeling Approaches (con’t)(con’t) Conventional Lumped-Parameter.Conventional Lumped-Parameter.

– The measurements areThe measurements are

– The input of thermal model isThe input of thermal model is

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Modeling Approaches Modeling Approaches (con’t)(con’t) Convention neural network.Convention neural network.

– The inputs to ANN areThe inputs to ANN are

– The target outputs areThe target outputs are

– The ANN model have three layer with 10 The ANN model have three layer with 10 hidden node.hidden node.

– The activation function at the hidden is The activation function at the hidden is hyperbolic tangent.hyperbolic tangent.

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Modeling Approaches Modeling Approaches (con’t)(con’t)

– The activation function at the output is linear The activation function at the output is linear function.function.

– Training by Levenberg-Marquardt algorithms.Training by Levenberg-Marquardt algorithms.

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Modeling Approaches Modeling Approaches (con’t)(con’t)

Neural plug-in Approach.Neural plug-in Approach.– The neural plug-in is learning the The neural plug-in is learning the

difference between the actual winding difference between the actual winding temperature and the predicted value temperature and the predicted value from Lumped model.from Lumped model.

– The inputs to ANN are The inputs to ANN are

– The target outputs are The target outputs are

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Modeling Approaches Modeling Approaches (con’t)(con’t)

– The schematic diagram of the neural The schematic diagram of the neural plug-in motor winding thermal modeling.plug-in motor winding thermal modeling.

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Result and ConclusionResult and Conclusion The compare winding temperature of The compare winding temperature of

three motor thermal estimate model. three motor thermal estimate model.

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Result Result (con’t)(con’t)

Modeling error in time domain.Modeling error in time domain.

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Result Result (con’t)(con’t) Three norm measures.Three norm measures.

The compare errors of 3 modelsThe compare errors of 3 models

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Conclusion Conclusion (con’t)(con’t)

The neural plug-in approach is The neural plug-in approach is superior than all.superior than all.

The neural plug-in makes Lumped-The neural plug-in makes Lumped-parameter approach more accurate.parameter approach more accurate.

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DiscussionDiscussion

The convention neural network The convention neural network approach is not as good as the others.approach is not as good as the others.

– This paper do not change to the different This paper do not change to the different factors such as the network size, the factors such as the network size, the training method, etc.training method, etc.

The winding temperature are raised by The winding temperature are raised by the only electric power. It don’t include the only electric power. It don’t include heat rise from load torque. heat rise from load torque.