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    Proceedings of the 2008 International Conference on Electrical Machines Paper ID 898

    978-1-4244-1736-0/08/$25.00 2008 IEEE

    Real time parameter determination in saturated inductors submitted to non-sinusoidalexcitations.

    Ramon Bargall Perpi1, Manuel Roman Lumbreras2, Alfonso Conesa Roca2, Guillermo Velasco Quesada2

    1: Electrical Engineering Department., 2: Electronic Engineering Department. Polytechnic University of Catalonia;

    EUETIB, C/Urgell, 187, 08036 Barcelona, Spain

    Tel: (+34)-934137323, fax: (+34)-934137401

    e-mail: [email protected]

    Abstract- Parameters determination on saturated systems is a

    complex task. Sometimes you can determine these parameters bycalculation but building errors must be checked to verify the finalvalues of them. You can use accurate off-line models but the time ofprocess could be excessive for a factory process. In this paper wedescribe a recursive algorithm to calculate the parameters in realtime and buried in a generic measurement system made to test threephase inductors.

    I. INTRODUCTION

    In order to have a fast measurement of the parameters the flux

    linkage must be known. It is, however, expensive and difficult to

    measure the flux. Instead, the flux can be estimated based on

    measurements of voltage and current. After discussion we

    present a Luenbergers observer for the flux and a recursive

    algorithm to calculate the parameters of the considered model.

    Also the measurement systems calculate the total losses on the

    inductor and using our model we can separate it on joule and

    magnetic losses.

    II. FLUX ESTIMATION AND OBSERVATION

    There are some possibilities to estimate the flux. The

    following table summarizes the most commons.

    TABLE I. FLUX ESTIMATORS

    Model Governing equation

    voltage = dtiru )(

    current iL =

    These estimators have some problems:

    a) If the resistance (r) is not correctly determined the estimated

    flux is different to the correct one.

    b) If the inductance (L) is not correctly determined the

    estimated flux differs to the correct one.

    For these reasons it is better to use a closed loop observed. The

    proposed Luenbergers observer is:

    ( )

    +

    =

    +=

    n

    NN

    N baIi

    iiirudt

    d

    (1)

    For values of > 1 is absolutely stable and the convergence ofthe observed values (^) to the real ones is guaranteed.

    In the above expression, INis the current that produces the flux

    N; it isnt the nominal current of the tested coil or transformer.

    The unknown parameters are: r, a, and b. Using the above

    expression it is possible to determine the inductance:

    +

    =

    =

    n

    NN

    N baI

    IL (2)

    Or

    +

    =

    =

    Nn

    n

    N bnaId

    dIL

    1

    11 (3)

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    Proceedings of the 2008 International Conference on Electrical Machines Paper ID 898

    978-1-4244-1736-0/08/$25.00 2008 IEEE

    We use the above observer in addition to a recursive estimator to

    obtain the unknown parameters. The figure 1 shows the structure

    of that.

    III. DISCRETESYSTEM

    By discretization the equation 1 becomes:

    (4)

    With T in the sampling period and k represents the k sampling

    interval (actual time could be calculate by t = k*T) To obtain the

    unknown parameters we solve recursively the above equation.

    Fig. 1. Flux observer + Recursive parameter estimator

    IV. LEASTSQUARESRECURSIVEMETHOD

    The recursive least squares method (RLS) tries to determinethe unknown parameters of the following matrix equation:

    )()()( kekky T += (5)

    y(k) are the measured set on instant k, )(kT are knownfunctions, e(k) are the measurement errors (unknown) and arethe unknown parameters. The RLS method calculates estimation

    for the actual time (k) based on the measurements until k and the

    estimation for the earlier time (k-1). The whole algorithm is:

    ( ))1()()()()1()( += kkkykKkk T

    (6))()()( kkPkK = (7)

    +

    =

    )()1()(

    )1()()()1(

    )1(1

    )(

    kkPk

    kPkkkP

    kP

    kPT

    T

    (8)

    For the penalizing factor the suitable values are from 0.95 to0.995 and the losses function to be minimized will be:

    ( )21

    )()(2

    1),( =

    =

    iiykV Tikk

    i

    (9)

    The expression

    Tikik

    ikik

    ee

    e

    /)()1()(

    ln)(

    =

    =

    (10)

    As a result of approximation of (10) at the surround of = 1.T is a pseudo-time constant

    (11)

    Suggested values for are 0.95 to 0.995 and these valuesindicates values for T for 20 to 200. It is accepted that values

    older that 2*T arent any influence for the final result.

    The only limitation of this method is that the parameters mustbe expressed as coefficient of known functions. In our case thisis true because the exponent (n) for the current-flux function can

    be considered known (they can be determined by early test andusually it is 5, 7 or 9 depending to the saturation level of thematerial.

    There is another limitation of this method but this is due to thecharacteristics of the applied signal: It must be a called persistentexcitation of order N, and the following conditions must beguaranteed:

    These limits exists

    =

    +=L

    t

    T

    Lu tutuL

    r1

    )()(1

    lim)( (12)

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    Proceedings of the 2008 International Confe

    978-1-4244-1736-0/08/$25.00 2008 IEEE

    These matrix be positive definite

    =

    =L

    t

    Lu tuL

    r1

    (1

    lim)(

    These expressions are true if the harmon

    applied signal contains at least N/2 harmonic

    V. EXPERIMENTALTE

    Fig 2 shows the experimental setup. The

    with the following parts:

    - Current controlled PWM three phase

    - Current and voltage sensors co

    acquisition card.

    - Data acquisition card: 16 di

    1.2Msamples.

    -

    PC with labview v8.2 to show and cavalues (current, voltage, power loss

    The user could select what data is sho

    Fig 2. Experimental setup.

    The following pictures show the resul

    transformer (1.3 kVA, 220/380 V, 5.9/3.5 A

    W, R1= 0.942 , R2= 1.202 , N= 0.9with PWM alimentation.

    ence on Electrical Machines Paper

    + T tu )() (13)

    ic component of the

    s.

    ST

    hole system is made

    onverter.

    nected to a data

    ferential channels;

    lculate the interestedes, parameters, etc.)

    wn.

    ts for a one-phase

    , PJ= 40 W, Po= 18

    9 Wb, IN= 1.41 A)

    Fig 3. Measured and calc

    Fig 4. a

    Fig 5.

    ID 898

    ulated current on a transformer

    (k) parameter.

    (k) parameter

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    Proceedings of the 2008 International Conference on Electrical Machines Paper ID 898

    978-1-4244-1736-0/08/$25.00 2008 IEEE

    The magnetic material of this transformer has an exponent n =

    7 (these value was calculated earlier) Final values for a and b are

    0.48 and 0.52 respectively; they are agree with the values

    calculated by off-line methods. The following picture shows the

    comparison between the experimental saturation characteristic

    and fitted equation using the above values for a, b, and n.

    Fig 6. Experimental and fitted saturation characteristic.

    Fig 7. Calculated and estimated inductances

    The figure 7 shows the comparison between calculated

    inductance using experimental measurements, calculated

    inductance using expressions (2) and (3) and by using 2D FE

    calculation.

    VI.

    CONCLUSSIONS

    The explained method allows the determination of

    parameters in saturated systems, and the algorithm is

    adapted to saturation level.

    For sinusoidal applied voltage the results are bad: thefinal values for the parameters a and b have offset.

    These are due the limited harmonic component of

    these signal (1 harmonic only allows to determine

    only 2 parameters under theorist assumptions: no

    measuring errors, etc.) As a conclusion it is necessary

    to apply a signal with some harmonics to avoid or

    minimize measuring errors)

    ACKNOWLEDGMENT

    This work was partially granted by FIT-030000-2007-79

    (MITYC) and Grupo PREMO S.A.

    REFERENCES

    [1] Ljung. System Identification: Theory for the user. Prentice Hall, NewJersey, 1987

    [2] R. Bargallo. Aportacin a la determinacin de parmetros de los modelos en

    la mquina asncrona para una mejor identificacin de variables no mensurables

    incidentes en su control. PhD dissertation. UPC, Barcelona, 2001.

    [3] Justus. Dynamisches Verhalten Elektrischer Maschinen. Vieweg Publishing,

    Braunschweig/Wiesbaden, 1991.

    [4] B. Peterson. Induction machine speed estimation. Observation on observers.

    Doctoral Dissertation. Lund Technical University. 1996. Lund. Sweden.

    [5] O. Nelles. Nonlinear System Identification. Springer. Berlin. 2001.

    i=a*flux+b*flux^7

    0 0,3 0,6 0,9 1,2

    flux/fluxN

    0

    0,2

    0,4

    0,6

    0,8

    1

    1,2

    1,4

    1,6

    1,8

    2

    2,2

    i/iN