Thermal Modelling Investigation of Heat Paths Due to Iron Losses in Synchronous Machines 04528833

8/7/2019 Thermal Modelling Investigation of Heat Paths Due to Iron Losses in Synchronous Machines 04528833

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Thermal Modelling Investigation ofHeat Paths due to Iron Lossesin Synchronous Machinesc. Mejuto M.Mueller, M. Shanel t, A. Mebarki t, D. Staton

The University of Edinburgh, Institute for Energy Systems, The King's Buildings, Mayfield Road, EH9 3JL, Scotlandt Cummins Generator Technologies, Barnack Road, Stamford, Lincolnshire, PE9 2NB, EnglandMotor Design Ltd., Lloyds Bank Chambers, 4 Scotland Street, Ellesmere, Shropshire, SY12 OEG, England

Keywords: Synchronous machine, Finite element analysis,Iron loss, Loss and Thermal distribution, Lumped circuit.AbstractThe prediction and distribution of operational iron losses insynchronous machines are of great importance in order toimprove the design procedure and yield more efficientgenerators. To achieve this, finite element analysis wasutilised to analyse a Cummins Generator Technologiesalternator and, using the required test data and materialproperties, the iron loss distribution across the machine'scross-section was established. From this, Lumped CircuitCoefficients (LCCs) were created. LCCs are used to translatethe generator's thermal model to a lumped circuit thermalnetwork.1 IntroductiopThe prediction and exact distribution of iron losses undertransient conditions across a synchronous machine'slamination has proven to be a challenging area. In the past,several methods have been applied in the frequency domain,but analysis in the time domain still requires significant work[1]. Therefore, the objective of the study was to utilise finiteelement analysis (PEA) to determine iron losses in the timedomain.PEA is a powerful design tool that allows mirroring electricalmachine tests and extracting important electromagnetic andthermal information, with a high degree of accuracy. Onceprecise geometries of the machine under investigation havebeen established, feeding experimentally obtained data relatedto the field and armature of the machine permits for thedetermination of useful data such as the vector potential,current density, magnetic flux density and magnetic fieldstrength distribution. Therefore, PEA provides a level ofinsight and detail of the machine's inner behaviour otherwiseunattainable by experimental methods, at the expense ofmeticulous model calibration and extensive computing times.Modelling synchronous generators using PEA carries anumber of differences with regards to motor simulations. Themain machine characterising parameters required for asynchronous machine model relate to the rotor, where thefield voltage and winding impedance are required as an input.On the other hand, no voltage input settings are required for

the armature winding, since these will be generated by thesimulation itself. Only the armature winding resistance andinductance are required for the stator settings.Magnetisation properties of the rotor and stator laminationmaterials, as well as the shaft, are of vital importance andneed to be accurately established. Furthermore, boundaryconditions (fixed potential edges) around particular machinecomponents need to be carefully considered. [2]2 Model DevelopmentThe geometries of the generator windings, shaft, laminationsand other design components need to be precisely extractedfrom reliable machine drawings. From them, the 2dimensional cross-section of the alternator can be created asshown in Figure 1.

Figure 1: Synchronous machine 2D PEAmodel.The model presented is that of the Cummins GeneratorTechnologies (CGT) BCI184E 4-pole synchronous generator.It is composed of 36 stator slots, in which a 2/3 pitcheddouble layer concentric winding is used. There are 3 slots perpole per phase and 98 turns per phase. The generator'sarmature is series star wound and machine rotation is in theclockwise direction. The particular alternator operating pointconsidered in the analysis is 22.5 kVA, 31.3 A, 415 V, 50 Hz.It is important to note that for the CGT alternator modelshown, fixed potential boundaries are exclusively utilisedaround the stator lamination.Rotor and stator lamination properties are set in accordancewith material suppliers data. However, in the study,lamination suppliers lack or are unable to provide certainmaterial data for particular operating conditions. In these

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situations reasonable approximations, from BH curves forsimilar materials on comparable machines, need to be made.In the study, the crucial fragment of the BH curve, the point atwhich the 'knee' occurs (see BH curves in Figures 2 and 3),was provided by the material supplier and the'graph wasextrapolated further to achieve the required complete BHcurve. The 'knee' section of the BH curve characterises thesaturation region. Curve points below the BH curve 'knee' arenormally not critical for the simulation's outcome. In thestudy, the BH curves used for the synchronous machinelaminations, Figure 2, and for the steel shaft, Figure 3, aredisplayed below. These were extracted from data provided bymaterial suppliers, but had to be slightly approximated due tolimited information being available for the desired frequencyrange and operational condition. Selected BH curves werevalidated by comparing them with previously utilised CGTBH curves for the same machine range.

8 (113 . 0 ~

- l ~ f o _ ~ : O _ .. ~ : O .. i O O O O : i j ~ ..50oiii-o... ~ ~ ..Figure 2: BH curve for alternator's rotor lamination.

Figure 3: BH curve alternator's rotor steel shaft.Once material geometries and properties have beensuccessfully logged into the FEA model, the designer'sattention shifts to the characterisation of the particularalternator operating point being analysed. At this stage, thearmature wiring configuration needs to be confirmed. For thisparticular machine, the stator is wound in a series starconfiguration, with a double layer concentric winding layoutof 2/3 pitch and 98 turns per phase. Once the electricalmachine's armature winding details are established, FEAexternal circuit simulation settings can be determined. Forthis machine utilising 6 external circuits, with 2 externalcircuits per phase, is appropriate as illustrated in Figure 4.

/ "~ _ m ~ , ~ + J' ,Figure 4: Alternator's external circuit equivalent circuit.

At this point, rotor field and stator armature simulationsettings need to be fed into the simulator. For a synchronousgenerator this refers to the field voltage, field resistance,armature resistance and armature inductance. These arecomputed using the CGT test data provided.Operating conditions: 22.5 kVA, 31.3 A, 415 V, 50 HzRotor Field Settings:Field current, IF 35 AField resistance, RF =0.863Field voltage, V 30.205 VStator Armature Settings:Armature line voltage, V 415 VArmature current, I 31.3 AArmature resistance, R 0.8896Armature phase voltage, Vp =239.6 VArmature phase load, Xp 6.12397 + 4.59298 QExternal R per external circuit 3.506785External L per external circuit 7.30995mH2.1Simulation Results ValidationOnce the synchronous generator material properties havebeen established and the simulation settings adjusted, thePEA simulator can be initialised. A reasonable simulationtolerance and time step for the simulation are required.Setting an adaptive time step manages the simulatorscomputing time effectively, focusing on the most sensitivesimulation segments. The desired output times, at which thesimulator will subsequently generate data, also needs to bedetermined.The initial task, once the simulation has terminated and theresults are unveiled, is to validate the data obtained. In orderto do this, experimental test data supplied by CGT for theparticular operation model under consideration is required. Inparticular, field and armature currents need to be carefullyexamined. The graphical data displayed in Figures 5 and 6confirms an acceptable level of agreement between the PEAsimulation results and the experimentally obtainedinformation supplied. Firstly, the rotor's field currentvariation with time is presented in Figure 5, followed by the3-phase stator armature current distribution, in Figure 6.


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.-....- _- Figure 5: Rotor field current variation with time.

representation of the electrical synchronous machine. Fromthis, the thermal model for of the rotor can be representedby up to 13 individual segments. Similarly, this applies to thestator, where a discretisation of up to 4 sectors is possible torepresent 1/72 of the lamination.In order to correctly understand and predict the occurrenceand allocation of power losses during synchronous generatoroperation, it is fundamental to analyse the magnetic fluxdistribution across the electrical machine. The magnetic fluxdensity plot for the alternator is displayed in Figure 8.

Both graphs exhibit an acceptable stable state conditionagreement with the experimental data supplied by CGT.Rotor field current results fully agree with the experimentaldata. Stator armature current results show an error of just over10% with respect to the test measurements. This is anacceptable mismatch given the enforced BH curve relatedassumptions made during the generator's model setup.Furthermore, there are numerous factors that could lead to adiscrepancy between simulation and practical results. Apartfrom the mentioned material properties related data, machinemanufacturing techniques can have a significant effect on theresulting armature current, since the high pressures exerted onthe laminations, or the turning process, can considerably altertheir electromagnetic behaviour.

Figure 8: Alternator's magnetic flux density distribution.It is important to note that the rotor turns in a clockwisedirection. The presented magnetic flux distribution clearlyillustrates the non-symmetrical nature of the operationalpower losses that occur during synchronous machine rotation.The most concentrated losses are situated at the 'lagging' halfof the rotor pole, with flux densities of over 2T in particularareas. Interestingly, a relatively low magnetic flux density isidentified at the 'leading' half of the rotor pole, which due toarmature reaction.4 Analysis and Results

Iron Loss,

For ferrite materials, this is then used to compute the loss perharmonic using the well-known Steinmetz formulae [1].

Operational iron losses for each rotor and stator sections canbe easily extracted from the PEA RM [3] results, via aspecially designed command file. The command file performsa harmonic evaluation of the flux density waveform in eachmodel element for the time cycle under investigation (onerotor rotation). Hence, the end result is a decomposition of themagnetic flux waveform per element into its constituent parts(fundamental, 18t order, 2nd order, 3rd order, etc.). It isimportant to note that the described method considers ironlosses exclusively and excludes stray losses fromconsideration, since these should be dealt with independently.

Figure 7: Aiternator's dissected rotor and ;tator lamination.This modification allows for the evaluation of each individual C"" a and are empirical parameters obtained fromrotor/stator section presented separately and is of great aid in experimental measurement under sinusoidal condition. Bmorder to transfer the eventual power loss infonnation represents the peak magnetic flux: density and representsfrequency.discoveries made, to a lumped circuit thermal network

The main objective of the FEA Rotating Machine (RM) [3]simulation work regards the prediction of iron losses and, inparticular, the distribution of these across the CGT electricalmachine. For this reason, i t is necessary to dissect the rotorand stator section of the model as shown in Figure 7.

3 Iron Loss determination distribution

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This is applied in the iron loss calculating command file bythe following equation [4]. (LCCs) and are obtained by combining electromagneticcoefficients (EMCs) and thermal coefficients (TempCs).(2) Electromagnetic Iron Loss Coefficients (EMCs)

is the total iron loss, the hysteresis component, e theeddy-current component and Px the excess loss. kh and kerepresent the hysteresis and eddy current iron losscoefficients respectively.As a more general approach, total iron loss is the sum of thehysteresis and eddy current components, with the addition ofan excess loss component due to domain wall effects thatshould be taken into account for non-ferrite materials [5].4.1 Hysteresis and Eddy Current Coefficients

Rotor electromagnetic iron loss coefficients (EMCs) evaluatethe concentration of the magnetic flux density at specific rotorlamination sections. Rotor EMC values for the top and middlesections are illustrated in the Figure 9. For instance, the topleft rotor pole section receives an EMC value with is 4 to 8time the base lamination value.

Using Equations 3 and 4 and the alternator's PowerCore M800-65 A 0.65 mm ThyssenKrupp Stahl lamination materialinformation the following hysteresis and eddy currentcoefficients can calculated [7].

Iron Loss, p, =Ph =khB n__ keB 2 (3)denSity denSityHence, P =k k (4)

t h density e density

In order to calculate the essential hysteresis, h, and eddycurrent, e, iron loss coefficients the following techniqueshould be employed. The Steinmetz related equationdisplayed below, together with the generator's PowerCoreM800-65 A 0.65 mm ThyssenKrupp Stahl lamination materialinformation are required for this [6].

Using the PEA thermal transient results, TempC values canbe established, depending on the temperature distribution ofspecific rotor lamination areas. The TempC layout isdisplayed below for total iron losses in Figures 10.

Transient Thermal Iron Loss Coefficients (TempCs)

Figure 9: Rotor EMC value distribution.EMC values for the rotor lamination sections not displayed inFigure 9 are set to 1, due to their low magnetic flux density.Critical rotor lamination sections, mainly located at the'lagging half, have a range related to their EMC values. Thisis because magnetic flux density pattern may vary slightlydepending on machine operational conditions and on designissues such as damper bars.

kh 488.6311.598614.2 Calculating CoefficientnAs presented in Equation 2, coefficients b and c) areused as a calibrating mechanism for the iron loss calculationprocess, as outlined below.

As a standard industry practical level, n should equal around1.5 at B=OT and 2.5 at B=2T, but specific lamination materialn values will vary.

n =a b B c B 2 (5)

Figure 10: Total iron loss rotor TempC value distribution.Default Lumped Circuit Coefficients (LCCs)The two sets of coefficients (EMCs, TempCs) are weighedand averaged to generate the general Lumped CircuitCoefficients (LCCs). These general default settings caneffectively be used to create a truly representative thermalmodel. Established LCC values are illustrated in Figure 11.For example, LCCs between 6 and 8 should be used forSection 92.

4.3 Lumped Circuit CoefficientsUtilising the loss data calculated, lumped thermal networkcoefficients can derived by comparing the iron lossconcentration distribution across the machine. Thesecoefficients are of great aid in order to generate a lumpedcircuit thermal model truly representative of the synchronousgenerator's rotor and stator loss distribution. The generatedcoefficients will be called Lumped Circuit Coefficients Figure 11: Rotor LCe value distribution.

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A section with an LCC value of 2 has approximately twicethe iron loss concentration as one with an LCC value of 1.Figure 11 indicates that, across the rotor, the main thermalsources concentrated at the top left corner (rotating 'lagging'halD of the rotor pole. LCC's of 8 to 5 for sections 92 to 95signify this. Adjacent sections 96 and 100 also exhibit asignificant thermal concentration and hence receive LCCvalues of 2 to 4. With the exception of section 97, whichcould exhibit an LCC of 1, the rest of the rotor dissectionshave a LCC value of 2, denoting a low temperature in theseareas.4.4 Stator SectionStator sections are simpler to model thermally, due to theirnon-rotating nature. A similar process to that performed onthe rotor was executed. LCC values for stator sections arepresented in Figure 12.

Figure 12: Stator LCC value distribution.Figure 12 shows that, as expected, the highest concentrationof iron losses, and hence highest temperature, across thestator lamination is located at the bottom of the statorlamination tooth. For this reason, section 106 has an LCCvalue of 2, whilst the remaining stator sections have a valueof 1, indicating a low iron loss concentration.Confirmation runs performed at other operational conditionsvalidate the information presented.5 Results

LCCs were applied to the reluctance network (Figure 13) interms of the weighing factors presented in this paper andresulted in an improved synchronous generator thermalmodel, which mimics the true iron loss distribution of thealternator in a more realistic manner. As shown in Figure 14the application of LCes to the top rotor pole section (nodes 4,5 and 6 of Figure 13) affects the temperature distributionacross the rotor lamination is achieved.

Figure 14: Rotor LCC application results for pole top section.

6 ConclusionsIt is important for modern generator design methods to takeinto account precise iron loss magnitudes and distributions.FEA is a powerful tool and was utilised to determine ironlosses and their location along the cross-section. From theseresults, LCCs were calculated by considering electromagneticdistributions and the resulting thermal effects. Application ofLCCs to the alternator's reluctance thermal model yielded atruly reliable representation of the electrical machine. Thismethod was implemented on a range of CGT machines for anumber of operation conditions with satisfactory results.AcknowledgementsThe authors would like to thank The University of Edinburghand Cummins Generator Technologies, as well as MotorDesign Ltd. and Adapted Solutions for their aid and support.References

The presented findings have been effectively applied to thesynchronous machine's lumped circuit thermal networks,shown in Figure 13. By accommodating the LCCs in thethermal model, the impact of and true distribution of ironlosses and their thermal effect is achieved, allowing for anaccurate representative electrical machine thermal model.

Figure 13: Rotor and stator lumped circuit thermal networks.

[4]

[1]

[7]

[5]

[2][6][3]

A. Novinschi, N.L. Brown, A. Mebarki, et al. "The development ofanFEA design environment model and comparison with traditionaldesign and test data fo r the design ofelectrical machine", lEEConference on Power Electronics and Machines, pages 574 - 572 (1618 April 2(02).D. Lin, P. Zhou, et. al. "A Dynamic Core Loss Model for Sof tFerromagnetic and Power Ferrite Materials in Transient Finite ElementAnalysis", IEEE Transactions on Magnetics, Vol. 40, No.2, (March,2004).L. Ma, M. Sanada, et. al. "Iron loss prediction considering therotational field and flux harmonics in IPMSM and SYDRM", lEEProc.-Electr. PowerAppl., Vol. 150, No.6, (November 2003).M. A. Mueller, S. Williamson, et. al. "Calculation of Iron Losses fromTime-Stepped Finite-Element Models of Cage Induction Machines",lEE Electrical Machines and Drives, Conference Publication No. 412,(1995).S. J. Salon. "Finite Element Analysis of Electrical Machines", KluwerAcademic Publisher, (1995).ThyssenKrupp Stahl. PowerCore M 800-65 A 0.65 mm laminationmaterial information (2007).Vector Fields, Opera 2-d software manual (V.12), 2007.

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Thermal Modelling Investigation of Heat Paths Due to Iron Losses in Synchronous Machines 04528833