(19)
(11)EP 2 408 107 B1

(12)EUROPEAN PATENT SPECIFICATION

(45)Mention of the grant of the patent:
08.03.2017 Bulletin 2017/10

(21)Application number: 11005683.5

(22)Date of filing:  12.07.2011
(51)Int. Cl.: 
H03H 17/02  (2006.01)
H03H 21/00  (2006.01)

(54)

Method and apparatus of adaptively cancelling a fundamental frequency of an analog signal

Verfahren und Vorrichtung zur adaptiven Unterdrückung einer Grundfrequenz eines analogen Signals

Procédé et appareil d'annulation adaptative d'une fréquence fondamentale d'un signal analogique


(84)Designated Contracting States:
AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

(30)Priority: 12.07.2010 US 834105

(43)Date of publication of application:
18.01.2012 Bulletin 2012/03

(73)Proprietor: Eaton Corporation
Cleveland, OH 44114-2584 (US)

(72)Inventors:
  • Nowak, Michael P.
    Milwaukee, WI 53202 (US)
  • Dimino, Steven A.
    Wauwatosa, WI 53213 (US)

(74)Representative: Wimmer, Hubert 
WAGNER & GEYER Gewürzmühlstrasse 5
80538 München
80538 München (DE)


(56)References cited: : 
EP-A1- 0 023 056
US-A1- 2003 155 922
US-A- 4 878 188
US-A1- 2008 100 245
  
      
    Note: Within nine months from the publication of the mention of the grant of the European patent, any person may give notice to the European Patent Office of opposition to the European patent granted. Notice of opposition shall be filed in a written reasoned statement. It shall not be deemed to have been filed until the opposition fee has been paid. (Art. 99(1) European Patent Convention).


    Description

    BACKGROUND


    Field



    [0001] The disclosed concept pertains generally to filters and, more particularly, to filters for removing a fundamental frequency from an analog signal, such as, for example, a motor current. The disclosed concept also pertains to methods of removing a fundamental frequency from an analog signal. The disclosed concept further pertains to systems for removing a fundamental frequency from an analog signal.

    Background Information



    [0002] In many situations, current components indicative of system faults are of a much smaller magnitude than the magnitude of a line frequency component. When implemented on low-cost digital signal processors, the performance of fault detection algorithms is significantly impaired by the loss of resolution of such current components after the analog-to-digital conversion (ADC) process. This problem can be alleviated by removing the line frequency component prior to ADC and by utilizing the full dynamic range of the ADC for the current components indicative of system faults. Known conventional techniques involving the removal of sinusoidal components often utilize notch filters set at the particular frequency of interest. However, these notch filters, in addition to canceling the desired frequency component, often remove or attenuate signal components of interest that are sufficiently close to the desired frequency. This is primarily due to the fact that the supply frequency from the utility can vary from the nominal value (e.g., without limitation, 50 Hz; 60 Hz). The conventional filters also cannot be used in applications where variable frequency motor drives are employed.

    [0003] US 4 878 188 A relates to a selective active cancellation system for repetitive phenomena. The system has a processor having single adaptive filters adapting its filtering characteristic as a function of a phenomena signal and a phenomena timing signal and a phase circuit to maintain the adaptation of the filtering characteristics within 90 degrees of the phenomena signal.

    [0004] Furthermore, EP 0 023 056 A relates to an arrangement having a non-recursive filter having an input circuit and means coupled thereto for generating a sequence of delayed versions of a signal applied to the input circuit. The arrangement has means for weighting a sequence of delayed versions of the input signal in accordance with a sequence of coefficients which are adjusted iteratively by positive and negative correction steps, respectively with variable step sizes. The step sizes are selected for each iteration and for each coefficient by means of a run length detector to which the sign of each correction step is applied and which selects a larger or smaller step size depending on the number of correction steps having the same sign and preceding the relevant iteration.

    [0005] There is room for improvement in filters for removing a fundamental frequency from an analog signal.

    [0006] There is also room for improvement in methods of removing a fundamental frequency from an analog signal.

    [0007] There is further room for improvement in systems for removing a fundamental frequency from an analog signal.

    SUMMARY



    [0008] These needs and others are met by embodiments of the disclosed concept, which provide a high-resolution, fundamental frequency cancellation method. In accordance with the invention, a method of canceling a fundamental frequency from an analog signal as set forth in claim 1, 2 or 3 is provided. Further embodiments are inter alia disclosed in the dependent claims.

    [0009] The method may be used in a system which comprises: a first powered apparatus including a first analog signal having a fundamental frequency; and a second apparatus structured to provide load diagnostics or power quality assessment of the first powered apparatus from a second digital signal, the second apparatus comprising: an input structured to input the first analog signal, an output structured to output the second digital signal, a processor, an adaptive filter routine executed by the processor, a digital-to-analog converter comprising an input and an output, and an analog-to-digital converter comprising an input and an output, wherein the adaptive filter routine is structured to output a third digital signal as a function of the second digital signal and a plurality of adaptive weights, wherein the digital-to-analog converter is structured to input the third digital signal and output a fourth analog signal representative of an estimate of a fundamental frequency component of the first analog signal, and wherein the analog-to-digital converter is structured to input a fifth analog signal, which is a difference between the first analog signal and the fourth analog signal, and output the second digital signal representative of the first analog signal with the fundamental frequency component removed.

    [0010] The second apparatus is a fundamental frequency cancellation apparatus using the method; and the adaptive filter routine may be structured to cancel the fundamental frequency from the first analog signal without corrupting spectral content proximate the fundamental frequency.

    [0011] The first powered apparatus may be a motor; the fundamental frequency may be a line frequency; and the first analog signal may be supply current to the motor.

    [0012] The first powered apparatus may receive power; the fundamental frequency may be a line frequency; the first analog signal may be supply current to the first powered apparatus; and the second apparatus may be a power sensing apparatus structured to sense power from the supply current to the first powered apparatus.

    [0013] The method may also be used in a system which comprises: a first apparatus including a first analog signal having a fundamental frequency; and a second apparatus comprising: an input structured to input the first analog signal, an output structured to output a second digital signal, a processor, a routine executed by the processor, a digital-to-analog converter (DAC) comprising an input, an output and a delay between the input and the output of the digital-to-analog converter, and an analog-to-digital converter (ADC) comprising an input, an output and a delay between the input and the output of the analog-to-digital converter, wherein the digital-to-analog converter is structured to input a third digital signal and output a fourth analog signal representative of an estimate of a fundamental frequency component of the first analog signal, wherein the analog-to-digital converter is structured to input a fifth analog signal and output the second digital signal representative of the first analog signal with the fundamental frequency component removed, wherein the routine is structured to provide the third digital signal being y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon), wherein the routine is further structured to provide a first adaptive weight being ws(n) = ws(n - 1) + µcIF(n - 1)xs(n - Δ - 1), wherein the routine is further structured to provide a second adaptive weight being wc(n) = wc(n - 1) + µcIF(n - 1)xc(n - Δ - 1), wherein ωo is frequency of the fundamental frequency component, wherein n is an integer representative of a sample number, wherein µc is a positive constant, wherein IF(n -1) is the second digital signal for the sample number represented by n - 1, wherein xs(n - Δ - 1) = sin(ωo(n - Δ - 1)), wherein xc(n - Δ - 1) = cos(ωo(n - Δ - 1)), wherein Δ is a sum of the delay of the analog-to-digital converter and the delay of the digital-to-analog converter, and wherein the routine is further structured to provide the fifth analog signal being a function of a difference between the first analog signal and the fourth analog signal.

    [0014] A fundamental frequency cancellation filter used in the method may comprise: a processor comprising: an input structured to input a first analog signal, an output structured to output a second digital signal, a routine, a digital-to-analog converter comprising an input, an output and a delay between the input and the output of the digital-to-analog converter, and an analog-to-digital converter comprising an input, an output and a delay between the input and the output of the analog-to-digital converter, the digital-to-analog converter is structured to input a third digital signal and output a fourth analog signal representative of an estimate of a fundamental frequency component of the first analog signal, wherein the analog-to-digital converter is structured to input a fifth analog signal and output the second digital signal representative of the first analog signal with the fundamental frequency component removed, wherein the routine is structured to provide the third digital signal being y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon), wherein the routine is further structured to provide a first adaptive weight being ws(n) = ws(n - 1) + µcIF(n - 1)xs(n - Δ - 1), wherein the routine is further structured to provide a second adaptive weight being wc(n) = wc(n - 1) + µcIF(n - 1)xc(n - Δ - 1), wherein ωo is frequency of the fundamental frequency component, wherein n is an integer representative of a sample number, wherein µc is a positive constant, wherein IF(n - 1) is the second digital signal for the sample number represented by n - 1, wherein xs(n - Δ - 1) = sin(ωo(n - Δ - 1)), wherein xc(n - Δ - 1) = cos(ωo(n - Δ - 1)), wherein Δ is a sum of the delay of the analog-to-digital converter and the delay of the digital-to-analog converter, and wherein the routine is further structured to provide the fifth analog signal being a function of a difference between the first analog signal and the fourth analog signal.

    [0015] The routine may be further structured to scale xs(n) by the first adaptive weight and to scale xc(n) by the second adaptive weight to provide the third digital signal.

    [0016] The method of canceling a fundamental frequency from an analog signal inter alia comprises: inputting a first analog signal; outputting a second digital signal; employing a digital-to-analog converter comprising an input and an output; employing an analog-to-digital converter comprising an input and an output; inputting a third digital signal to and outputting a fourth analog signal representative of an estimate of a fundamental frequency component of the first analog signal from the digital-to-analog converter; inputting a fifth analog signal to the analog-to-digital converter and outputting from the analog-to-digital converter the second digital signal representative of the first analog signal with the fundamental frequency component removed; providing a first adaptive filter weight, ws(n), and a second adaptive filter weight, wc(n); providing a first digital sine signal, xs(n) = sin(ωon), and a second digital cosine signal, xc(n) = cos(ωon); providing the third digital signal being y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon); employing ωo as frequency of the fundamental frequency component; employing n as an integer representative of a sample number; and providing the fifth analog signal as a function of a difference between the first analog signal and the fourth analog signal.

    [0017] The method may further comprise providing an optimum value of the first adaptive filter weight as being ws* = (A / GDACo))cos(θA - θDACo)); providing an optimum value of the second adaptive filter weight as being wc* = (A / GDACo))sin(θA - θDACo)); employing

    employing θA = tan-1(wc* / ws*) + θDACo); employing GDACo) as magnitude of a transfer function of the digital-to-analog converter at the frequency of the fundamental frequency component; and employing θDACo) as phase of the transfer function of the digital-to-analog converter at the frequency of the fundamental frequency component.

    BRIEF DESCRIPTION OF THE DRAWINGS



    [0018] A full understanding of the disclosed concept can be gained from the following description of the preferred embodiments when read in conjunction with the accompanying drawings in which:

    Figure 1 is a block diagram of a fundamental frequency cancellation filter in accordance with embodiments of the disclosed concept.

    Figure 2 is a block diagram of a simplified fundamental frequency cancellation filter in accordance with another embodiment of the disclosed concept.

    Figure 3 is a block diagram in schematic form of a system including the fundamental frequency cancellation filter of Figure 1.


    DESCRIPTION OF THE PREFERRED EMBODIMENTS



    [0019] As employed herein, the term "number" shall mean one or an integer greater than one (i.e., a plurality).

    [0020] As employed herein, the term "processor" means a programmable analog and/or digital device that can store, retrieve, and process data; a computer; a workstation; a personal computer; a digital signal processor (DSP); a microprocessor; a microcontroller; a microcomputer; a central processing unit; a mainframe computer; a mini-computer; a server; a networked processor; or any suitable processing device or apparatus.

    [0021] The disclosed concept is described in association with an adaptive filter implemented by a digital signal processor (DSP) to remove a line frequency of a line current from a motor supply current of a motor, although the disclosed concept is applicable to a wide range of processors to remove a fundamental frequency of an analog signal of a wide range of apparatus.

    [0022] Referring to Figure 1, the cancellation of a fundamental frequency, (ωo, can be achieved through the use of an adaptive filter 2 comprising two adaptive weights 4,6. H 8 is an estimate (in the digital domain) of an unknown analog domain transfer function, Hs (not shown). The Hs analog domain transfer function can be considered to exist between an analog-to-digital converter (ADC) 10 and an optional gain function (G) 12. The optional gain function (G) 12 can be disposed after a difference 14 between analog signal I(t) 16 and analog signal Ioest(t) 18. Here, t is the time portion of an analog signal. Analog signal I(t) 16 represents, for example and without limitation, analog motor supply current. A digital-to-analog converter (DAC) 20 and the ADC 10 represent respective digital-to-analog conversion and analog-to-digital conversion processes on a suitable processor, such as the example digital signal processor (DSP) 22 of Figure 3. The adaptive filter weights are ws(n) 4 and wc(n) 6, where n is a sample number of a digital domain signal or value. The two inputs to the filter, signal xs(n) 24 and signal xc(n) 26, are respective digital sine and digital cosine signals with a frequency equal to the fundamental frequency of analog signal I(t) 16, ωo, given by xs(n) = sin(ωon) and xc(n) = cos(ωon), respectively.

    [0023] The noise-free sinusoidal signals 24,26 are typically unavailable and can be generated on the example DSP 22 (Figure 3). These signals 24,26 can be efficiently computed using a conventional coupled-form digital oscillator (not shown) based on an estimate of the fundamental frequency (or by using a look-up table (not shown) or by any other suitable method). The digital oscillator can be implemented using a pair of recursive equations:

    and



    [0024] The recursive generation employs the initial conditions:

    and



    [0025] The basic operation of the adaptive filter 2 is as follows. The inputs 24,26 are scaled by the respective adaptive weights 4,6 and are combined to form the signal y(n) 28 as shown by Equation 1.



    [0026] The signal y(n) 28 is converted by the DAC 20 providing the analog signal, Ioest(t) 18, that is an estimate of the fundamental frequency component of analog signal I(t) 16, which can be, for example and without limitation, a motor supply current. The estimate Ioest(t) 18 is subtracted from the analog signal I(t) 16 to produce an example current signal 30 with the fundamental frequency component removed. This example current signal 30, which can be amplified by the gain function (G) 12, is digitized by the ADC 10 to produce a digital output signal IF(n) 32, and which can be further processed by the DSP 22 (Figure 3) for fault detection purposes. For example, the digital output signal IF(n) 32 can also be employed as an error or correction signal to adapt the filter weights 4,6.

    [0027] The cancellation of the fundamental frequency component occurs when the filter weights 4,6 are set such that the filter output, y(n) 28, consists of a sinusoid with magnitude and phase exactly equal to magnitude and phase of the fundamental frequency component of analog signal I(t) 16. The weight values resulting in optimal cancellation are derived as follows. The fundamental frequency component, Io(t), of the example analog signal I(t) 16 is defined by Equation 2.

    wherein:

    A is a constant;

    θA is phase.



    [0028] Using Equation 1, the estimated fundamental frequency component Ioest(t) 18 is given by Equation 2.

    wherein:

    GDACo) and θDACo) are the respective magnitude and phase of the DAC transfer function at frequency, ω = ωo,

    ws(t) is the time domain equivalent of the digital domain adaptive weight ws(n) 4, and

    wc(t) is the time domain equivalent of the digital domain adaptive weight wc(n) 6.



    [0029] In Equations 1 and 3, the optimum weight values are adjusted to their optimum values using a filtered least-mean-square (Filtered-X LMS as is defined, below, after Equation 5) algorithm, and simplified in some of the following equations. In a typical setup, the input to the LMS algorithm (Equations 4A and 4B, below) is labeled 'x' and "filtered-x" refers to the fact that you need to filter the input, or 'x', before using it to update the adaptive weights. In Equations 2 and 3, the discrete time index, n, is replaced by the continuous time variable, t, since these components are after the DAC 20 and, therefore, are analog signals.

    [0030] If the fundamental frequency component Io(t) of the example supply current is represented in the equivalent form:

    then by using trigonometric identities it can be expressed as:



    [0031] Therefore, applying Equation 3, the optimum weight values resulting in cancellation of the fundamental frequency component Io(t) are:

    and



    [0032] The magnitude and phase can be represented directly in terms of the filter weights 4,6 by:

    and



    [0033] Since the magnitude and phase of Io(t) are not known and may vary over time, the filter weights 4,6 can be adapted according to a conventional least-mean-square (LMS) algorithm. The LMS algorithm is a stochastic gradient-based algorithm where the updated value of the filter weights 4,6 at time n + 1 are computed using the recursive relations:



    wherein:

    µ is a positive step-size constant that controls the size of the incremental correction applied to the weight at each iteration;

    J(n) is the squared-error signal at time n given by J(n) = |IF(n)|2; and

    ∂J(n)/∂ws(n) and ∂J(n)/∂wc(n) are the partial derivatives of the squared-error signal J(n) with respect to the filter weight ws(n) and wc(n), respectively.



    [0034] The update rule for the filter weight, ws, is derived as follows. First, IF(n) 32 can be expressed using Equations 2 and 3 as follows:

    wherein:

    GADC(ωo) and θADCo) are the respective magnitude and phase of the transfer function of ADC 10 at frequency ω = ωo.



    [0035] The partial derivative of J(n) with respect to ws(n) equals



    [0036] Therefore, the update rule for ws(n) 4 is given by Equation 5.



    [0037] The weight update rule of Equation 5 is a version of the LMS algorithm that compensates for transfer functions present in the output path of the adaptive filter 2. A conventional Filtered-X LMS adaptive algorithm can be expressed as:

    wherein:

    xfs(n) is equal to the filter input xs(n) 24 filtered by an estimate of the combined transfer function of DAC 20 and ADC 10.



    [0038] In the frequency domain, xfs(ω) is given by:

    wherein:

    DAC(ω) is the transfer function of the DAC 20 in the frequency domain, and

    ADC(ω) is the transfer function of the ADC 10 in the frequency domain.



    [0039] In a similar manner, the update rule for the second weight 6 is given by:

    wherein:



    [0040] The weight adaptation process is illustrated in Figure 1. The disclosed concept can be applied to remove multiple frequencies by simply adding multiple instances of the left side of the filter 2 in Figure 1. The application of this would be to remove the system frequency as well as some of the dominant harmonics. For example, for a power system (not shown), such a filter can remove the fundamental frequency (e.g., without limitation, 60 Hz) and, for example, the fifth harmonic (e.g., without limitation, 300 Hz) from the analog signal I(t) 16, which can be, for example, a power supply current.

    [0041] Taking advantage of the uniform response of the transfer functions of the DAC 20 and the ADC 10, however, one can make an important simplification to the adaptation algorithm. Assuming that the DAC 20 and the ADC 10 have uniform gain and linear phase over the frequency band of interest or:

    and

    and creating a new step-size parameter, µc = µGADCGDAC, the update rule for ws(n) (Equation 5) can be simplified as:

    or

    wherein:

    Δ = α + β represents the total delay through the ADC 10 and the DAC 20, and

    α and β represent the delay through the DAC 20 and the ADC 10, respectively.



    [0042] A similar update rule can be applied to wc(n) 6:



    [0043] The block diagram of a simplified adaptive filter 2' is shown in Figure 2. The term z-1 (not shown) represents a unit delay element. The symbol z 34 of Figure 2 represents a delay of Δ sample periods, as defined above, in the digital domain. This delay is a suitable digital model of the unknown H analog domain transfer function, which was discussed, above, in connection with Figure 1.

    [0044] The value of Δ can be determined using the frequency response specifications of the DAC 20 and the ADC 10. The theoretical range of the step-size parameter µc resulting in the convergence of the algorithm to the optimum weights ws* and wc* is known and is given by:



    [0045] Figure 3 shows a system 40 including the adaptive filter 2 of Figure 1 for canceling a fundamental frequency. The system 40 includes a first apparatus 42 including the first analog signal I(t) 16 having the fundamental frequency ωo, and a second apparatus 44 including an input 46 structured to input the first analog signal I(t) 16, an output 48 structured to output the second digital signal IF(n) 48, a processor, such as the example DSP 22, a routine, such as adaptive routine 50, executed by the example DSP 22, the DAC 20 having an input 52, an output 54 and the delay α between the input 52 and the output 54, and the ADC 10 having an input 56, an output 58 and the delay β between the input 56 and the output 58.

    [0046] The disclosed concept can be applied to a wide range of applications, such as for example and without limitation, the second apparatus 44 can be a fundamental frequency cancellation apparatus or a power sensing module for the first apparatus 42, which can be, for example and without limitation, a motor powered by a power line having a line frequency.

    [0047] Although a technique for updating the adaptive weights 4,6 is disclosed, an alternative, known recursive least squares (RLS) algorithm can be employed.

    [0048] Although a technique for the adaptive filter 2 is disclosed, this, alternatively, could also be implemented using a known Kalman filter.

    [0049] Alternative estimation techniques for the unknown H analog domain transfer function (not shown) could also be employed. The disclosed concept simplifies the problem by modeling this transfer function with a simple delay line as was discussed, above, in connection with symbol z 34 of Figure 2, but other rational filter models can be employed.

    [0050] The disclosed adaptive filters 2,2' can be employed, for example, to ensure that a motor current representation employed for motor current signature analysis is of suitable high fidelity. This is achieved by removing the fundamental frequency component of the example motor current (e.g., without limitation, motor current at 50 Hz; 60 Hz; 400 Hz; any suitable system frequency) and applying a suitable gain (G) 12 to the residual signal prior to the input 56 of the ADC 10 to minimize quantization error. Since this process is adaptive, relatively large and relatively small deviations from the nominal system frequency can be removed as well.

    [0051] The disclosed adaptive filters 2,2' improve signal acquisition (e.g., increase resolution and/or decrease quantization noise) by nulling a fundamental line frequency component prior to analog to digital conversion for use in, for example, motor/load diagnostic applications. These adaptive filters 2,2' null out a single frequency (the fundamental component of the line frequency), track and null the fundamental component in the presence of small frequency changes (e.g., less than about 1 Hz) in applications where a load, such as a motor, is connected directly to a power line, and/or track and null the fundamental component in the presence of relatively large frequency changes (e.g., about 20 Hz to about 400 Hz) in applications where the motor is variable frequency AC drive (VFD) driven.

    [0052] The disclosed concept provides efficient fundamental frequency cancellation filters 2,2' based on estimated frequency (e.g., estimated line frequency) Ioest(t) 18 that can precisely cancel, for example, the line frequency from a supply current without corrupting the spectral content proximate that line frequency.

    [0053] The disclosed concept can be employed in any motor or other load diagnostics or power quality assessment of an electrical system application, where it is advantageous to remove a single, dominant frequency component that obscures a number of signals of interest. In this manner, the example second apparatus 44 can readily provide known load diagnostics or known power quality assessment of the first powered apparatus 42 by making advantageous use of the second digital signal 32 (IF(n)), which does not include a fundamental frequency component.

    [0054] Although the DSP 22 and the example routine 50 are disclosed, the functions of the filters 2,2' can be implemented by a wide range of hardware and/or software components. For example and without limitation, the ADC 10 and the DAC 20 can be part of or separate from the DSP 22 or another suitable processor. Although the example routine 50 can provide the various signals 24,26,28, functions 8,12,14,34, and weights 4,6, those can be implemented by a wide range of hardware and/or software components.

    [0055] For example, the disclosed concept can be applied to a motor wellness system. This provides the ability to increase the resolution of ADC conversion by eliminating the fundamental component of the line current and applying a gain to the residual signal (where the motor wellness information is contained) prior to analog to digital conversion. Also, the ability to track relatively small changes in the power supply frequency in directly connected systems or track relatively large frequency changes are of benefit when used with a VFD.

    [0056] While specific embodiments of the disclosed concept have been described in detail, it will be appreciated by those skilled in the art that various modifications and alternatives to those details could be developed in light of the overall teachings of the disclosure. Accordingly, the particular arrangements disclosed are meant to be illustrative only and not limiting as to the scope of the disclosed concept which is defined by the appended claims.

    REFERENCE NUMERICAL LIST



    [0057] 
    2
    adaptive filter
    2'
    simplified adaptive filter
    4
    adaptive weight
    6
    adaptive weight
    8 H,
    estimate (in the digital domain) of an unknown analog domain transfer function, Hs
    10
    analog-to-digital converter (ADC)
    12
    gain function (G)
    14
    difference
    16
    analog signal I(t)
    18
    analog signal Ioest(t)
    20
    digital-to-analog converter (DAC)
    22
    processor, such as the example digital signal processor (DSP)
    24
    signal xs(n)
    26
    signal xc(n)
    28
    signal y(n)
    30
    current signal
    32
    digital output signal IF(n)
    34
    symbol z
    40
    system
    42
    first apparatus
    44
    second apparatus
    46
    input
    48
    output
    50
    routine
    52
    input
    54
    output
    56
    input
    58
    output



    Claims

    1. A method of canceling a fundamental frequency from an analog signal, said method comprising:

    inputting a first analog signal (16);

    outputting a second digital signal (32);

    employing a digital-to-analog converter (20) comprising an input (52) and an output (54);

    employing an analog-to-digital converter (10) comprising an input (56) and an output (58);

    providing a first adaptive filter weight, ws(n) (4), and a second adaptive filter weight, wc(n) (6);

    providing a first digital sine signal, xs(n) = sin(ωon) (24), and a second digital cosine signal, xc(n) = cos(ωon) (26);

    providing a third digital signal being y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon) using an adaptive filter;

    employing ωo as frequency of the fundamental frequency component;

    employing n as an integer representative of a sample number;

    inputting the third digital signal (28) to said digital-to-analog converter (20) and outputting a fourth analog signal (18) representative of an estimate of a fundamental frequency component (Io(t)) of the first analog signal (16) from said digital-to-analog converter (20);

    providing a fifth analog signal (30) as a function of a difference (14) between the first analog signal (16) and the fourth analog signal (18);

    inputting the fifth analog signal (30) to said analog-to-digital converter (10) and outputting from said analog-to-digital converter (10) the second digital signal (32) representative of the first analog signal (16) with the fundamental frequency component removed; wherein:

    an optimum value of the first adaptive filter weight is provided as ws* = (A / GDACo))cos(θA - θDACo)); and

    an optimum value of the second adaptive filter weight is provided as wc* = (A / GDACo))sin(θA - θDACo));

    the method further comprising:

    employing

    employing θA = tan-1(wc* / ws*) + θDACo);

    employing GDACo) as magnitude of a transfer function of the digital-to-analog converter at the frequency of the fundamental frequency component; and

    employing θDACo) as phase of the transfer function of the digital-to-analog converter at the frequency of the fundamental frequency component.


     
    2. A method of canceling a fundamental frequency from an analog signal, said method comprising:

    inputting a first analog signal (16);

    outputting a second digital signal (32);

    employing a digital-to-analog converter (20) comprising an input (52) and an output (54);

    employing an analog-to-digital converter (10) comprising an input (56) and an output (58);

    providing a first adaptive filter weight, ws(n) (4), and a second adaptive filter weight, wc(n) (6);

    providing a first digital sine signal, xs(n) = sin(ωon) (24), and a second digital cosine signal, xc(n) = cos(ωon) (26);

    providing a third digital signal being y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon) using an adaptive filter;

    employing ωo as frequency of the fundamental frequency component;

    employing n as an integer representative of a sample number;

    inputting the third digital signal (28) to said digital-to-analog converter (20) and outputting a fourth analog signal (18) representative of an estimate of a fundamental frequency component (Io(t)) of the first analog signal (16) from said digital-to-analog converter (20);

    providing a fifth analog signal (30) as a function of a difference (14) between the first analog signal (16) and the fourth analog signal (18);

    inputting the fifth analog signal (30) to said analog-to-digital converter (10) and outputting from said analog-to-digital converter (10) the second digital signal (32) representative of the first analog signal (16) with the fundamental frequency component removed; wherein:

    the first adaptive filter weight ws(n) is determined as being equal to ws(n - 1) + µGADCo)GDACo)IF(n - 1)sin(ωo(n - 1) + θDACo) + θADCo));

    the second adaptive filter weight wc(n) is determined as being equal to wc(n - 1) + µGADCo)GDACo)IF(n - 1)sin(ωo(n - 1) + θDACo) + OADC((ωo));

    the method further comprising:

    employing GDACo) as gain of said digital-to-analog converter versus the frequency of the fundamental frequency component;

    employing GADCo) as gain of said analog-to-digital converter versus the frequency of the fundamental frequency component;

    employing θDACo) as phase of said digital-to-analog converter versus the frequency of the fundamental frequency component;

    employing θADCo) as phase of said analog-to-digital converter versus the frequency of the fundamental frequency component; and

    employing µ as a positive constant.


     
    3. A method of canceling a fundamental frequency from an analog signal, said method comprising:

    inputting a first analog signal (16);

    outputting a second digital signal (32);

    employing a digital-to-analog converter (20) comprising an input (52) and an output (54);

    employing an analog-to-digital converter (10) comprising an input (56) and an output (58);

    providing a first adaptive filter weight ws(n) (4), and a second adaptive filter weight wc(n) (6);

    providing a first digital sine signal, xs(n) = sin(ωon) (24), and a second digital cosine signal, xc(n) = cos(ωon) (26);

    providing a third digital signal being y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon) using an adaptive filter;

    employing ωo as frequency of the fundamental frequency component;

    employing n as an integer representative of a sample number;

    inputting the third digital signal (28) to said digital-to-analog converter (20) and outputting a fourth analog signal (18) representative of an estimate of a fundamental frequency component (Io(t)) of the first analog signal (16) from said digital-to-analog converter (20);

    providing a fifth analog signal (30) as a function of a difference (14) between the first analog signal (16) and the fourth analog signal (18);

    inputting the fifth analog signal (30) to said analog-to-digital converter (10) and outputting from said analog-to-digital converter (10) the second digital signal (32) representative of the first analog signal (16) with the fundamental frequency component removed; wherein:

    the first adaptive filter weight is provided as

    and

    the second adaptive filter weight is provided as

    the method further comprising:

    employing DAC(ω) as a transfer function of the digital-to-analog converter as a function of frequency, ω, of the fundamental frequency component;

    employing ADC(ω) as a transfer function of the analog-to-digital converter as a function of the frequency of the fundamental frequency component;

    employing said digital-to-analog converter and said analog-to-digital converter having a uniform gain and a linear phase over a predetermined range of frequencies;

    setting DAC(ω) = GDACe-jαω;

    setting ADC(ω) = GADCe-jβω;

    employing GDAC as the uniform gain of said digital-to-analog converter;

    employing GADC as the uniform gain of said analog-to-digital converter;

    employing α as a delay between the input and the output of the digital-to-analog converter;

    employing β as a delay between the input and the output of the analog-to-digital converter;

    employing µ as a positive constant;

    employing µc = µGADCGDAC.


     
    4. The method of Claim 3 further comprising:

    employing µc as a positive constant;

    employing IF(n - 1) as the second digital signal for the sample number being n - 1;

    employing xs(n - Δ - 1) = sin(ωo(n - Δ - 1));

    employing xc(n - Δ - 1) = cos(ωo(n - Δ - 1));

    employing Δ as a sum of a delay (α) between the input and the output of the digital-to-analog converter and a delay (β) between the input and the output of the analog-to-digital converter; and


     
    5. The method of any one of the preceding claims, wherein:

    providing said fifth analog signal (30) includes multiplying a gain value with said difference.


     
    6. The method of any one of the preceding claims, further comprising:

    updating the first and second adaptive weights employing a recursive least squares (RLS) algorithm.


     
    7. The method of any one of the preceding claims, further comprising:

    employing a motor current (16) as said first analog signal; and

    employing a line frequency (ωo) as the frequency of the fundamental frequency component.


     
    8. The method of any one of the preceding claims, wherein:

    the first analog signal is input from a powered apparatus, and

    a load diagnostics or power quality assessment of said powered apparatus (42) is provided from said second digital signal (48).


     


    Ansprüche

    1. Ein Verfahren zum Auslöschen einer Grundfrequenz von einem analogen Signal, wobei das Verfahren die folgenden Schritte aufweist:

    Eingeben eines ersten analogen Signals (16);

    Ausgeben eines zweiten digitalen Signals (32);

    Benutzen eines digital-zu-analog Konverters (20), wobei der Konverter einen Eingang (52) und einen Ausgang (54) aufweist;

    Benutzen eines analog-zu-digital Konverters (10), wobei der Konverter einen Eingang (56) und einen Ausgang (58) aufweist;

    Bereitstellen eines ersten adaptiven Filtergewichts, ws(n) (4), und eines zweiten adaptiven Filtergewichts wc(n) (6);

    Bereitstellen eines ersten digitalen Sinussignals, xs(n) = sin(ω0n) (24), und eines zweiten digitalen Cosinussignals xc(n) = cos(ω0n) (26);

    Bereitstellen eines dritten digitalen Signals y(n) = ws(n) * sin(ω0n) + wc(n) * cos(ω0n) unter der Verwendung eines adaptiven Filters;

    Benutzen von w0 als Frequenz der Grundfrequenzkomponente;

    Benutzen von n als Ganzzahl, die eine Samplezahl repräsentiert;

    Eingeben des dritten digitalen Signals (28) in den digital-zu-analog Konverter (20) und Ausgeben eines vierten Signals (18), das eine Schätzung einer Grundfrequenzkomponente (I0(t)) des ersten analogen Signals (16) des digital-zu-analog Konverters (20) repräsentiert;

    Bereitstellen eines fünften analogen Signals (30) als eine Funktion einer Differenz (14) zwischen dem ersten analogen Signal (16) und dem vierten analogen Signal (18);

    Eingeben des fünften analogen Signals (30) in den analog-zu-digital Konverter (10) und Ausgeben aus dem analog-zu-digital Konverter (10) das zweite digitale Signal (32), das das erste analoge Signal (16) mit der entfernten Grundfrequenzkomponente repräsentiert; wobei:

    ein optimaler Wert des ersten adaptiven Filtergewichts bereitgestellt wird als ws* = (A / GDAC0)) cos(ΘA - ΘDAC0)); und

    ein optimaler Wert des zweiten adaptiven Filtergewichts bereitgestellt wird als wc* = (A / GDAC0)) sin(ΘA - ΘDAC0));

    wobei das Verfahren ferner folgende Schritte aufweist:

    Benutzen von

    Benutzen von ΘA = tan-1 (wc* / ws*) + ΘDAC0);

    Benutzen von GDAC0) als Größe einer Transferfunktion des digital-zu-analog Konverters mit der Frequenz der Grundfrequenzkomponente; und

    Benutzen von ΘDAC0) als Phase der Transferfunktion des digital-zu-analog Konverters mit der Frequenz der Grundfrequenzkomponente.


     
    2. Ein Verfahren zum Auslöschen einer Grundfrequenz von einem analogen Signal, wobei das Verfahren die folgenden Schritte aufweist:

    Eingeben eines ersten analogen Signals (16);

    Ausgeben eines zweiten digitalen Signals (32);

    Benutzen eines digital-zu-analog Konverters (20), wobei der Konverter einen Eingang (52) und einen Ausgang (54) aufweist;

    Benutzen eines analog-zu-digital Konverters (10), wobei der Konverter einen Eingang (56) und einen Ausgang (58) aufweist;

    Bereitstellen eines ersten adaptiven Filtergewichts, ws(n) (4), und eines zweiten adaptiven Filtergewichts wc(n) (6);

    Bereitstellen eines ersten digitalen Sinussignals, xs(n) = sin(ω0n) (24), und eines zweiten digitalen Cosinussignals xc(n) = cos(w0n) (26);

    Bereitstellen eines dritten digitalen Signals y(n) = ws(n) * sin(ω0n) + wc(n) * cos(ω0n) unter der Verwendung eines adaptiven Filters;

    Benutzen von ω0 als Frequenz der Grundfrequenzkomponente;

    Benutzen von n als Ganzzahl, die eine Samplezahl repräsentiert;

    Eingeben des dritten digitalen Signals (28) in den digital-zu-analog Konverter (20) und Ausgeben eines vierten Signals (18), das eine Schätzung einer Grundfrequenzkomponente (I0(t)) des ersten analogen Signals (16) des digital-zu-analog Konverters (20) repräsentiert;

    Bereitstellen eines fünften analogen Signals (30) als eine Funktion einer Differenz (14) zwischen dem ersten analogen Signal (16) und dem vierten analogen Signal (18);

    Eingeben des fünften analogen Signals (30) in den analog-zu-digital Konverter (10) und Ausgeben aus dem analog-zu-digital Konverter (10) das zweite digitale Signal (32), das das erste analoge Signal (16) mit der entfernten Grundfrequenzkomponente repräsentiert; wobei:

    das erste adaptive Filtergewicht ws(n) bestimmt wird zu ws(n-1) + µGADC0) GDAC0) IF(n-1) sin(ω0(n-1) + ΘDAC0) + ΘADC0));

    das zweite adaptive Filtergewicht wc(n) bestimmt wird zu wc(n-1) + µGADC0) GDAC0) IF(n-1) sin(ω0(n-1) + ΘDAC0) + ΘADC0));

    wobei das Verfahren ferner folgende Schritte aufweist:

    Benutzen von GDAC0) als Gewinn des digital-zu-analog Konverters gegenüber der Frequenz der Grundfrequenzkomponente;

    Benutzen von GADC0) als Gewinn des analog-zu-digital Konverters gegenüber der Frequenz der Grundfrequenzkomponente;

    Benutzen von ΘDAC0) als Phase des digital-zu-analog Konverters gegenüber der Frequenz der Grundfrequenzkomponente;

    Benutzen von ΘADC0) als Phase des analog-zu-digital Konverters gegenüber der Frequenz der Grundfrequenzkomponente; und

    Benutzen von µ als eine positive Konstante.


     
    3. Ein Verfahren zum Auslöschen einer Grundfrequenz von einem analogen Signal, wobei das Verfahren die folgenden Schritte aufweist:

    Eingeben eines ersten analogen Signals (16);

    Ausgeben eines zweiten digitalen Signals (32);

    Benutzen eines digital-zu-analog Konverters (20), wobei der Konverter einen Eingang (52) und einen Ausgang (54) aufweist;

    Benutzen eines analog-zu-digital Konverters (10), wobei der Konverter einen Eingang (56) und einen Ausgang (58) aufweist;

    Bereitstellen eines ersten adaptiven Filtergewichts, ws(n) (4), und eines zweiten adaptiven Filtergewichts wc(n) (6);

    Bereitstellen eines ersten digitalen Sinussignals, xs(n) = sin(ω0n) (24), und eines zweiten digitalen Cosinussignals xc(n) = cos(ω0n) (26);

    Bereitstellen eines dritten digitalen Signals y(n) = ws(n) * sin(ω0n) + wc(n) * cos(ω0n) unter der Verwendung eines adaptiven Filters;

    Benutzen von ω0 als Frequenz der Grundfrequenzkomponente;

    Benutzen von n als Ganzzahl, die eine Samplezahl repräsentiert;

    Eingeben des dritten digitalen Signals (28) in den digital-zu-analog Konverter (20) und Ausgeben eines vierten Signals (18), das eine Schätzung einer Grundfrequenzkomponente (I0(t)) des ersten analogen Signals (16) des digital-zu-analog Konverters (20) repräsentiert;

    Bereitstellen eines fünften analogen Signals (30) als eine Funktion einer Differenz (14) zwischen dem ersten analogen Signal (16) und dem vierten analogen Signal (18);

    Eingeben des fünften analogen Signals (30) in den analog-zu-digital Konverter (10) und Ausgeben aus dem analog-zu-digital Konverter (10) das zweite digitale Signal (32), das das erste analoge Signal (16) mit der entfernten Grundfrequenzkomponente repräsentiert; wobei:

    das erste adaptive Filtergewicht bereitgestellt wird als

    und

    das zweite adaptive Filtergewicht bereitgestellt wird als

    wobei das Verfahren ferner folgende Schritte aufweist:

    Benutzen von DAC(ω) als Transferfunktion des digital-zu-analog Konverters als eine Funktion der Frequenz, ω, der Grundfrequenzkomponente;

    Benutzen von ADC(ω) als Transferfunktion des analog-zu-digital Konverters als eine Funktion der Frequenz der Grundfrequenzkomponente;

    Benutzen des digital-zu-analog Konverters und des analog-zu-digital Konverters, die einen gleichförmigen Gewinn aufweisen, und eine lineare Phase über einen vorbestimmten Frequenzbereich;

    Setzen von DAC(ω) = GDACe-jαω;

    Setzen von ADC(ω) = GADCe-jβω;

    Benutzen von GDAC als den einheitlichen Gewinn des digital-zu-analog Konverters;

    Benutzen von GADC als den einheitlichen Gewinn des analog-zu-digital Konverters;

    Benutzen von α als eine Verzögerung zwischen der Eingabe und der Ausgabe des digital-zu-analog Konverters;

    Benutzen von β als eine Verzögerung zwischen der Eingabe und der Ausgabe des analog-zu-digital Konverters;

    Benutzen von µ als eine positive Konstante;

    Benutzen von µc = µGADC GDAC.


     
    4. Das Verfahren nach Anspruch 3, wobei das Verfahren ferner folgende Schritte aufweist:

    Benutzen von µc als eine positive Konstante;

    Benutzen von IF(n-1) als das zweite digitale Signal für die Samplezahl n-1;

    Benutzen von xs(n-Δ-1) = sin(ω0(n-Δ-1);

    Benutzen von xc(n-Δ-1) = cos(ω0(n-Δ-1);

    Benutzen von Δ als eine Summe von einer Verzögerung (α) zwischen der Eingabe und der Ausgabe des digital-zu-analog Konverters und einer Verzögerung (β) zwischen der Eingabe und der Ausgabe des analog-zu-digital Konverters.


     
    5. Das Verfahren nach einem der vorhergehenden Ansprüche, wobei:

    das Bereitstellen des fünften analogen Signals (30) das Multiplizieren eines Gewinnwertes mit der Differenz aufweist.


     
    6. Das Verfahren nach einem der vorhergehenden Ansprüche, wobei das Verfahren ferner folgenden Schritt aufweist:

    Aktualisieren des ersten und des zweiten adaptiven Gewichts unter Verwendung eines Recursive Least Square (RLS), bzw. eines rekursiven kleinste Quadrate Algorithmus.


     
    7. Das Verfahren nach einem der vorhergehenden Ansprüche, wobei das Verfahren ferner folgende Schritte aufweist:

    Benutzen eines Motorstroms (16) als das erste analoge Signal; und

    Benutzen einer Leitungsfrequenz (ω0) als die Frequenz der Grundfrequenz.


     
    8. Das Verfahren nach einem der vorhergehenden Ansprüche, wobei:

    das erste analoge Signal eingegeben wird von einer angeschalteten Vorrichtung, und

    eine Lastdiagnose oder eine Leistungsqualitätseinschätzung der angeschalteten Vorrichtung (42) von dem zweiten digitalen Signal (48) bereitgestellt wird.


     


    Revendications

    1. Procédé pour annuler une fréquence fondamentale dans un signal analogique, le procédé comprenant :

    introduire un premier signal analogique (16) ;

    fournir un deuxième signal numérique (32) ;

    utiliser un convertisseur numérique-analogique (20) comprenant une entrée (52) et une sortie (54) ;

    utiliser un convertisseur analogique-numérique (10) comprenant une entrée (56) et une sortie (58) ;

    fournir un premier poids de filtre adaptatif, ws(n) (4), et un deuxième poids de filtre adaptatif, wc(n) (6) ;

    fournir un premier signal de sinus numérique, xs(n) = sin(ωon) (24), et un deuxième signal de cosinus numérique, xc(n) = cos(ωon) (26) ;

    fournir un troisième signal numérique y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon) en utilisant un filtre adaptatif ;

    utiliser ωo comme fréquence de la composante fréquentielle fondamentale ;

    utilisern comme entier représentatif d'un numéro d'échantillon ;

    introduire le troisième signal numérique (28) dans le convertisseur numérique-analogique (20) et sortir un quatrième signal analogique (18) représentatif d'une estimation d'une composante fréquentielle fondamentale (Io(t)) du premier signal analogique (16) à partir du convertisseur numérique-analogique (20) ;

    fournir un cinquième signal analogique (30) en fonction d'une différence (14) entre le premier signal analogique (16) et le quatrième signal analogique (18) ;

    introduire le cinquième signal analogique (30) dans le convertisseur analogique-numérique (10) et sortir à partir du convertisseur analogique-numérique (10) le deuxième signal numérique (32) représentatif du premier signal analogique (16) avec la composante fréquentielle fondamentale retirée ; dans lequel :

    une valeur optimale du premier poids de filtre adaptatif est donnée par ws* = (A / GGACo))cos(θA - θDACo)) ; et

    une valeur optimale du deuxième poids de filtre adaptatif est donnée par wc* = (A / GDACo))sin(θA - θDACo)) ;

    le procédé comprenant en outre :

    utiliser

    utiliser θA = tan-1(wc* / ws*) + θDACo) ;

    utiliser GDACo) comme amplitude d'une fonction de transfert du convertisseur numérique-analogique à la fréquence de la composante fréquentielle fondamentale ; et

    utiliser θDACo) comme phase de la fonction de transfert du convertisseur numérique-analogique à la fréquence de la composante fréquentielle fondamentale.


     
    2. Procédé pour annuler une fréquence fondamentale dans un signal analogique, le procédé comprenant :

    introduire un premier signal analogique (16) ;

    fournir un deuxième signal numérique (32) ;

    utiliser un convertisseur numérique-analogique (20) comprenant une entrée (52) et une sortie (54) ;

    utiliser un convertisseur analogique-numérique (10) comprenant une entrée (56) et une sortie (58) ;

    fournir un premier poids de filtre adaptatif, ws(n) (4), et un deuxième poids de filtre adaptatif, wc(n) (6) ;

    fournir un premier signal de sinus numérique, xs(n) = sin(ωon) (24), et un deuxième signal de cosinus numérique, xc(n) = cos(ωon) (26) ;

    fournir un troisième signal numérique y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon) en utilisant un filtre adaptatif ;

    utiliser ωo comme fréquence de la composante fréquentielle fondamentale ;

    utiliser n comme entier représentatif d'un numéro d'échantillon ;

    introduire le troisième signal numérique (28) dans le convertisseur numérique-analogique (20) et sortir un quatrième signal analogique (18) représentatif d'une estimation d'une composante fréquentielle fondamentale (Io(t)) du premier signal analogique (16) à partir du convertisseur numérique-analogique (20) ;

    fournir un cinquième signal analogique (30) en fonction d'une différence (14) entre le premier signal analogique (16) et le quatrième signal analogique (18) ;

    introduire le cinquième signal analogique (30) dans le convertisseur analogique-numérique (10) et sortir à partir du convertisseur analogique-numérique (10) le deuxième signal numérique (32) représentatif du premier signal analogique (16) avec la composante fréquentielle fondamentale retirée ; dans lequel :

    le premier poids de filtre adaptatif ws(n) est déterminé comme égal à ws(n-1) + µGADCo)GDACo)IF(n-1)sin(ωo(n-1) + θDAC + θADCo)) ;

    le deuxième poids de filtre adaptatif wc(n) est déterminé comme égal à wc(n-1) + µGADCo)GDACo)IF(n-1)sin(ωo(n-1) + θDACo) + θADCo)) ;

    le procédé comprenant en outre :

    utiliser GDAC(wo) comme gain du convertisseur numérique-analogique en fonction de la fréquence de la composante fréquentielle fondamentale ;

    utiliser GADCo) comme gain du convertisseur analogique-numérique en fonction de la fréquence de la composante fréquentielle fondamentale ;

    utiliser θDACo) comme phase du convertisseur numérique-analogique en fonction de la fréquence de la composante fréquentielle fondamentale ;

    utiliser θADCo) comme phase du convertisseur analogique-numérique en fonction de la fréquence de la composante fréquentielle fondamentale ; et

    utiliser µ comme constante positive.


     
    3. Procédé pour annuler une fréquence fondamentale dans un signal analogique, le procédé comprenant :

    introduire un premier signal analogique (16) ;

    fournir un deuxième signal numérique (32) ;

    utiliser un convertisseur numérique-analogique (20) comprenant une entrée (52) et une sortie (54) ;

    utiliser un convertisseur analogique-numérique (10) comprenant une entrée (56) et une sortie (58) ;

    fournir un premier poids de filtre adaptatif, ws(n) (4), et un deuxième poids de filtre adaptatif, wc(n) (6) ;

    fournir un premier signal de sinus numérique, xs(n) = sin(ωon) (24), et un deuxième signal de cosinus numérique, xc(n) = cos(ωon) (26) ;

    fournir un troisième signal numérique y(n) = ws(n)*sin(ωon) + wc(n)*cos(ωon) en utilisant un filtre adaptatif ;

    utiliser ωo comme fréquence de la composante fréquentielle fondamentale ;

    utiliser n comme entier représentatif d'un numéro d'échantillon ;

    introduire le troisième signal numérique (28) dans le convertisseur numérique-analogique (20) et sortir un quatrième signal analogique (18) représentatif d'une estimation d'une composante fréquentielle fondamentale (Io(t)) du premier signal analogique (16) à partir du convertisseur numérique-analogique (20) ;

    fournir un cinquième signal analogique (30) en fonction d'une différence (14) entre le premier signal analogique (16) et le quatrième signal analogique (18) ;

    introduire le cinquième signal analogique (30) dans le convertisseur analogique-numérique (10) et sortir à partir du convertisseur analogique-numérique (10) le deuxième signal numérique (32) représentatif du premier signal analogique (16) avec la composante fréquentielle fondamentale retirée ; dans lequel :

    le premier poids de filtre adaptatif est donné par

    et

    le deuxième poids de filtre adaptatif est donné par

    le procédé comprenant en outre :

    utiliser DAC(ω) comme fonction de transfert du convertisseur numérique-analogique en fonction de la fréquence, ω, de la composante fréquentielle fondamentale ;

    utiliser ADC(ω) comme fonction de transfert du convertisseur analogique-numérique en fonction de la fréquence de la composante fréquentielle fondamentale ;

    utiliser le convertisseur numérique-analogique et le convertisseur analogique-numérique ayant un gain uniforme et une phase linéaire sur une plage prédéterminée de fréquences ;

    mettre DAC (ω) = GDACe-jαω ;

    mettre ADC(ω) = GADCe-jβω ;

    utiliser GDAC comme gain uniforme du convertisseur numérique-analogique ;

    utiliser GADC comme gain uniforme du convertisseur analogique-numérique ;

    utilisera comme retard entre l'entrée et la sortie du convertisseur numérique-analogique ;

    utiliser β comme retard entre l'entrée et la sortie du convertisseur analogique numérique ;

    utiliser µ comme constante positive ;

    utiliser µc = µGADCGDAC.


     
    4. Procédé selon la revendication 3, comprenant en outre :

    utiliser µc comme constante positive ;

    utiliser IF(n - 1) comme deuxième signal numérique pour le numérod'échantillon n-1 ;

    utiliser xs(n - Δ - 1) = sin(ωo(n - Δ - 1)) ;

    utiliser xc(n - Δ - 1) = cos(ωo(n - Δ - 1)) ;

    utiliser Δ comme somme d'un retard (α) entre l'entrée et la sortie du convertisseur numérique-analogique et d'un retard (β) entre l'entrée et la sortie du convertisseur analogique-numérique ; et


     
    5. Procédé selon l'une quelconque des revendications précédentes, dans lequel :

    la fourniture du cinquième signal analogique (30) comprend une multiplication d'une valeur de gain par ladite différence.


     
    6. Procédé selon l'une quelconque des revendications précédentes, comprenant en outre :

    mettre à jour les premier et deuxième poids adaptatifs en utilisant un algorithme récursif des moindres carrés (RLS).


     
    7. Procédé selon l'une quelconque des revendications précédentes, comprenant en outre :

    utiliser un courant de moteur (16) comme premier signal analogique ; et

    utiliser une fréquence de ligne (ωo) comme fréquence de la composante fréquentielle fondamentale.


     
    8. Procédé selon l'une quelconque des revendications précédentes, dans lequel :

    le premier signal analogique est introduit à partir d'un appareil alimenté, et

    un diagnostic de charge ou une évaluation de qualité d'alimentation de l'appareilalimenté (42) sontfournis à partir du deuxième signal numérique (48).


     




    Drawing









    REFERENCES CITED IN THE DESCRIPTION



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    Patent documents cited in the description