Electrical signal parameter estimation method and device, electronic equipment and storage medium

Abstract

The invention relates to an electrical signal parameter estimation method and device, electronic equipment and a storage medium, and relates to the technical field of electrical signal processing. The method comprises the following steps: carrying out cyclic estimation on a fundamental wave signal based on a received signal by adopting a variable step size minimum mean square algorithm until the fundamental wave signal meets a preset condition; wherein the fundamental wave signal is a difference value between the error signal and the receiving signal, and the error signal is an output signal after the receiving signal passes through the wave trap; the step length factor of the variable step length minimum mean square algorithm is adjustable, and the step length factor is adjusted based on the square of the autocorrelation function time average value of the error signals at two adjacent moments; and acquiring a signal parameter of the fundamental wave signal. When the electrical signal frequency changes rapidly, good response speed and estimation precision can be obtained at the same time, and the method is more suitable for parameter estimation of non-stationary signals.

Description

Technical Field

[0001] The present application relates to the technical field of electrical signal processing, and in particular to an electrical signal parameter estimation method, device, electronic device and storage medium. Background Art

[0002] In the related art, in the parameter estimation of non-stationary and rapidly changing signals in power systems, the existing estimation methods have the problem of being difficult to simultaneously obtain a faster convergence speed and a smaller steady-state measurement error, which are contradictory relationships. Summary of the invention

[0003] In order to overcome the problems existing in the related art, the present application provides an electrical signal parameter estimation method, device, electronic device and storage medium, which can simultaneously obtain good response speed and estimation accuracy, and solve the problem in the related art that it is difficult to simultaneously obtain a faster convergence speed and a smaller steady-state measurement error.

[0004] According to a first aspect of an embodiment of the present application, there is provided a method for estimating electrical signal parameters, comprising:

[0005] A variable step-size minimum mean square algorithm is used to cyclically estimate the fundamental signal based on the received signal until the fundamental signal meets a preset condition; wherein the fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter; the step-size factor of the variable step-size minimum mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments;

[0006] Acquire signal parameters of the fundamental wave signal.

[0007] In some implementations, the step of using a variable step size least mean square algorithm to cyclically estimate a fundamental signal based on a received signal includes:

[0008] Based on the error signal at the K-1th moment and the error signal at the K-2th moment, a time average of the autocorrelation function at the K-th moment is obtained; wherein K is a positive integer greater than or equal to 3, and the K-1th moment and the K-2th moment are adjacent moments;

[0009] Obtaining a step size factor at the Kth moment based on the square of the time average value of the autocorrelation function at the Kth moment;

[0010] Based on the step size factor at the Kth moment, obtaining an adaptive parameter at the Kth moment;

[0011] Calculating an error signal at the Kth moment based on the received signal and the adaptive parameter at the Kth moment;

[0012] The K is adjusted to K+1.

[0013] In some implementations, obtaining the time average of the autocorrelation function at the Kth moment based on the error signal at the K-1th moment and the error signal at the K-2th moment comprises:

[0014] Through the first formula, based on the error signal at the K-1th moment and the error signal at the K-2th moment, the time average value of the autocorrelation function at the Kth moment is obtained; wherein the first formula is expressed as follows:

[0015] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0016] Among them, φ(K) is the time average value of the autocorrelation function at the Kth moment, λ is the time average value coefficient of the autocorrelation function, y(K-1) is the error signal at the K-1th moment, and y(K-2) is the error signal at the K-2th moment.

[0017] In some implementations, obtaining the step size factor at the Kth moment based on the square of the time average of the autocorrelation function at the Kth moment includes:

[0018] The step size factor at the Kth moment is obtained by the second formula based on the square of the time average value of the autocorrelation function at the Kth moment; wherein the second formula is expressed as follows:

[0019] μ(K)=pμ(K-1)+βφ^2(K)

[0020] Among them, μ(K) is the step size factor at the Kth moment, p and β are the step size factor coefficients, and φ(K) is the time average of the autocorrelation function at the Kth moment.

[0021] In some implementations, the step factor μ(K) at the Kth moment is greater than a step factor lower limit threshold, and the step factor μ(K) at the Kth moment is less than a step factor upper limit threshold.

[0022] In some implementations, obtaining the adaptive parameter at the Kth moment based on the step size factor at the Kth moment includes:

[0023] Through the third formula, based on the step size factor at the Kth moment, the adaptive parameter at the Kth moment is obtained; wherein the third formula is expressed as follows:

[0024] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0025] Among them, α(k) is the adaptive parameter at the Kth moment, μ(K) is the step factor at the Kth moment, y(K-1) is the error signal at the K-1th moment, and x(k-2) is the received signal at the K-2th moment.

[0026] In some implementations, the calculating the error signal at the Kth moment based on the received signal and the adaptive parameter at the Kth moment includes:

[0027] The error signal at the Kth moment is calculated based on the received signal and the adaptive parameter at the Kth moment by the fourth formula; wherein the fourth formula is expressed as follows:

[0028] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0029] Wherein, y(K) is the error signal at the Kth moment, x(K) is the received signal at the Kth moment, and α(k) is the adaptive parameter at the Kth moment.

[0030] According to a second aspect of an embodiment of the present application, there is provided an electrical signal parameter estimation device, comprising:

[0031] A signal estimation module, configured to use a variable step-size minimum mean square algorithm to cyclically estimate a fundamental signal based on a received signal until the fundamental signal satisfies a preset condition; wherein the fundamental signal is a difference between an error signal and the received signal, and the error signal is an output signal of the received signal after passing through a notch filter; the step-size factor of the variable step-size minimum mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments;

[0032] The parameter acquisition module is used to acquire the signal parameters of the fundamental wave signal.

[0033] In some implementations, the signal estimation module includes:

[0034] An autocorrelation parameter acquisition unit, configured to obtain a time average of an autocorrelation function at a Kth moment based on the error signal at the K-1th moment and the error signal at the K-2th moment; wherein K is a positive integer greater than or equal to 3, and the K-1th moment and the K-2th moment are adjacent moments;

[0035] A step factor acquisition unit, used to obtain the step factor at the Kth moment based on the square of the time average value of the autocorrelation function at the Kth moment;

[0036] An adaptive parameter acquisition unit, used for obtaining an adaptive parameter at the Kth moment based on the step size factor at the Kth moment;

[0037] an error signal acquisition unit, configured to calculate an error signal at the Kth moment based on the received signal and the adaptive parameter at the Kth moment;

[0038] A parameter adjustment unit is used to adjust the K to K+1.

[0039] In some implementations, the autocorrelation parameter acquisition unit is specifically configured to:

[0040] Through the first formula, based on the error signal at the K-1th moment and the error signal at the K-2th moment, the time average value of the autocorrelation function at the Kth moment is obtained; wherein the first formula is expressed as follows:

[0041] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0042] Among them, φ(K) is the time average value of the autocorrelation function at the Kth moment, λ is the time average value coefficient of the autocorrelation function, y(K-1) is the error signal at the K-1th moment, and y(K-2) is the error signal at the K-2th moment.

[0043] In some implementations, the step factor acquisition unit is specifically configured to:

[0044] The step size factor at the Kth moment is obtained by the second formula based on the square of the time average value of the autocorrelation function at the Kth moment; wherein the second formula is expressed as follows:

[0045] μ(K)=pμ(K-1)+βφ^2(K)

[0046] Among them, μ(K) is the step size factor at the Kth moment, p and β are the step size factor coefficients, and φ(K) is the time average of the autocorrelation function at the Kth moment.

[0047] In some implementations, the step factor μ(K) at the Kth moment is greater than a step factor lower limit threshold, and the step factor μ(K) at the Kth moment is less than a step factor upper limit threshold.

[0048] In some implementations, the adaptive parameter acquisition unit is specifically configured to:

[0049] Through the third formula, based on the step size factor at the Kth moment, the adaptive parameter at the Kth moment is obtained; wherein the third formula is expressed as follows:

[0050] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0051] Among them, α(k) is the adaptive parameter at the Kth moment, μ(K) is the step factor at the Kth moment, y(K-1) is the error signal at the K-1th moment, and x(k-2) is the received signal at the K-2th moment.

[0052] In some implementations, the error signal acquisition unit is specifically configured to:

[0053] The error signal at the Kth moment is calculated based on the received signal and the adaptive parameter at the Kth moment by the fourth formula; wherein the fourth formula is expressed as follows:

[0054] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0055] Wherein, y(K) is the error signal at the Kth moment, x(K) is the received signal at the Kth moment, and α(k) is the adaptive parameter at the Kth moment.

[0056] According to a third aspect of an embodiment of the present application, there is provided an electronic device, including:

[0057] at least one processor; and

[0058] a memory communicatively connected to the at least one processor; wherein,

[0059] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the electrical signal parameter estimation method described in the first aspect above.

[0060] According to a fourth aspect of an embodiment of the present application, a computer-readable storage medium is provided, comprising:

[0061] The storage medium stores a computer program, and when the computer program is executed by the processor, the electrical signal parameter estimation method described in the first aspect is implemented.

[0062] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0063] When the frequency of the electrical signal changes rapidly, it can simultaneously obtain good response speed and estimation accuracy, which is more suitable for parameter estimation of non-stationary signals. And it can be realized with the assistance of existing load equipment without adding additional costs.

[0064] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0065] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0066] Figure 1 The figure is a flow chart of a method for estimating electrical signal parameters according to an exemplary embodiment.

[0067] Figure 2 It is an application scenario diagram of an electrical signal parameter estimation method according to an exemplary embodiment.

[0068] Figure 3 is a flow chart of a method for estimating electrical signal parameters according to another exemplary embodiment.

[0069] Figure 4 It is a block diagram of an electrical signal parameter estimation device according to an exemplary embodiment.

[0070] Figure 5 It is a block diagram of a device according to an exemplary embodiment. DETAILED DESCRIPTION

[0071] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention. Instead, they are merely examples of devices and methods consistent with some aspects of the present invention as detailed in the appended claims.

[0072] In the related art, in the fields of power system and electrical equipment operation, monitoring, control and relay protection, for example, for parameter estimation of non-stationary and fast-changing signals of power systems, adaptive filtering algorithms, such as LMS (Least Mean Square) adaptive algorithms, are usually used. However, when using a fixed-step LMS adaptive algorithm to achieve parameter estimation of non-stationary and fast-changing signals, it is difficult to simultaneously obtain a faster convergence speed and a smaller steady-state measurement error.

[0073] To solve the above problems, the embodiments of the present application provide an electrical signal parameter estimation method, device, electronic device and storage medium, which uses an LMS adaptive algorithm with a variable step size factor to calculate the notch filter frequency parameters, thereby estimating the frequency parameters of the non-stationary signal of the power system. Compared with the existing methods, this method is suitable for parameter estimation of non-stationary signals, and when the frequency of the electrical signal changes rapidly, it can simultaneously obtain good response speed and frequency measurement accuracy. Therefore, this method is more suitable for parameter estimation of non-stationary rapidly changing signals.

[0074] Figure 1 is a flow chart of a method for estimating electrical signal parameters according to an exemplary embodiment. Figure 1 As shown, the electrical signal parameter estimation method can be used in electronic devices such as terminals, and may include the following steps.

[0075] In step S101, a variable step-size minimum mean square algorithm is used to cyclically estimate the fundamental signal based on the received signal until the fundamental signal meets a preset condition; wherein the fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter; the step-size factor of the variable step-size minimum mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments.

[0076] like Figure 2 As shown, it is an application scenario of the electrical signal parameter estimation method of an embodiment of the present application, and x(k) is the input signal of the trap filter H(z), that is, the received signal. As an example, x(k)=Asin(ω0k+b0)+v(K), v(K) represents higher harmonics, and y(K) is the error signal output after the input signal x(k) passes through the trap filter H(z). s(K) is x(K) after passing through the trap filter H(z) and subtracting the original input signal x(K) to obtain the fundamental signal s(K), that is, s(K)=y(k)-x(k). The function of the trap filter is to filter out unnecessary frequency signals. In the embodiment of the present application, its function is to filter out higher harmonics. Higher harmonics can be understood as noise signals. The noise signal is filtered out to obtain a pure fundamental signal.

[0077] A variable step-size least mean square algorithm is used to cyclically estimate the fundamental signal based on the received signal until the fundamental signal meets a preset condition. Meeting the preset condition means reaching a stable value. After reaching the stable value, the obtained fundamental signal is the estimated electrical signal.

[0078] The step size factor of the variable step size least mean square algorithm of the present embodiment is adjustable, and the step size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments, that is, the step size factor is dynamically adjusted according to the time average value of the autocorrelation function of the error signal at two adjacent moments. The smaller the time average value of the autocorrelation function, the smaller the step size, and the larger the time average value of the autocorrelation function, the larger the step size, thereby improving the convergence speed and stability of the algorithm.

[0079] It can be understood that when the fixed-step least mean square algorithm updates the coefficients of the filter, there is a performance degradation problem in a non-stationary environment. The variable-step least mean square algorithm of the embodiment of the present application improves performance by dynamically adjusting the step size factor to adapt to changes in the non-stationary environment.

[0080] In step S102, signal parameters of the fundamental wave signal are acquired.

[0081] After obtaining the estimated electrical signal, for example, Asin(ω0k+b0), the signal parameters of the electrical signal, including the frequency parameters, can be obtained.

[0082] The electrical signal parameter estimation method of the embodiment of the present application uses a variable step-size least mean square algorithm to perform cyclic estimation of the fundamental signal based on the received signal, and at the same time, the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments. When the frequency of the electrical signal changes rapidly, it is possible to obtain good response speed and estimation accuracy at the same time, solving the problem in the related art that it is difficult to simultaneously obtain a faster convergence speed and a smaller steady-state measurement error, and is more suitable for parameter estimation of non-stationary signals. And it can be achieved with the assistance of existing load equipment without adding additional costs.

[0083] In step S101 of the above embodiment, it is mentioned that the fundamental signal is cyclically estimated based on the received signal by using the least mean square algorithm with a variable step size. The specific implementation method of this step is described in detail below.

[0084] Figure 3 is a flow chart of an electrical signal parameter estimation method according to another exemplary embodiment. Figure 3 As shown, the electrical signal parameter estimation method may include the following steps.

[0085] In step S201, parameters related to loop estimation are initialized.

[0086] As an example, set a(0)=0, φ(0)=0, p=0.99, β=0.001, λ=0.95, μmin=0.0001, μmax=0.005, μmin is the lower threshold of the step factor, μmax is the upper threshold of the step factor, and y(0)=0.

[0087] Among them, α(0) is the adaptive parameter at the 0th time, i.e. the initial time, φ(0) is the time average value of the autocorrelation function at the 0th time, i.e. the initial time, p and β are both step factor coefficients, λ is the time average value coefficient of the autocorrelation function, and y(0) is the error signal at the 0th time, i.e. the initial time.

[0088] In step S202, based on the error signal at the K-1th moment and the error signal at the K-2th moment, the time average of the autocorrelation function at the K-th moment is obtained; wherein K is a positive integer greater than or equal to 3, and the K-1th moment and the K-2th moment are adjacent moments.

[0089] As an implementation method, the time average value of the autocorrelation function at the Kth moment is obtained based on the error signal at the K-1th moment and the error signal at the K-2th moment through the first formula; wherein the first formula is expressed as follows:

[0090] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0091] Among them, φ(K) is the time average value of the autocorrelation function at the Kth moment, λ is the time average value coefficient of the autocorrelation function, y(K-1) is the error signal at the K-1th moment, and y(K-2) is the error signal at the K-2th moment.

[0092] The time average value of the autocorrelation function at the Kth moment is obtained so that the step size factor can be dynamically adjusted in the subsequent steps by the square of the time average value of the autocorrelation function.

[0093] In step S203, a step size factor at the Kth moment is obtained based on the square of the time average value of the autocorrelation function at the Kth moment.

[0094] As an implementation method, the step size factor at the Kth moment is obtained based on the square of the time average value of the autocorrelation function at the Kth moment through the second formula; wherein the second formula is expressed as follows:

[0095] μ(K)=pμ(K-1)+βφ^2(K), (μmin<μ(K)<μmax);

[0096] Among them, μ(K) is the step size factor at the Kth moment, μ(K-1) is the step size factor at the K-1th moment, p and β are both step size factor coefficients, and φ(K) is the time average of the autocorrelation function at the Kth moment.

[0097] In this embodiment, the step size factor is dynamically adjusted by the square of the time average of the autocorrelation function of the error signal at the K-1th moment and the error signal at the K-2th moment to better adapt to changes in the non-stationary environment.

[0098] In step S204, the adaptive parameter at the Kth moment is obtained based on the step size factor at the Kth moment.

[0099] As an implementation method, the adaptive parameter at the Kth moment is obtained based on the step size factor at the Kth moment through the third formula; wherein the third formula is expressed as follows:

[0100] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0101] Among them, α(k) is the adaptive parameter at the Kth moment, μ(K) is the step factor at the Kth moment, y(K-1) is the error signal at the K-1th moment, and x(k-2) is the received signal at the K-2th moment.

[0102] After the step size factor is obtained, the adaptive parameter can be obtained according to the step size factor, that is, the coefficient of the notch filter can be obtained.

[0103] In step S205, the error signal at the Kth moment is calculated based on the received signal and the adaptive parameter at the Kth moment.

[0104] As an implementation method, the error signal at the Kth moment is calculated based on the received signal and the adaptive parameter at the Kth moment through the fourth formula; wherein the fourth formula is expressed as follows:

[0105] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0106] Wherein, y(K) is the error signal at the Kth moment, x(K) is the received signal at the Kth moment, and α(k) is the adaptive parameter at the Kth moment.

[0107] In step S206, K is adjusted to K+1, and the process returns to step S202 until the fundamental wave signal at the Kth moment meets the preset conditions, and then the process goes to step S207; wherein the fundamental wave signal at the Kth moment is the difference between the error signal at the Kth moment and the received signal at the Kth moment.

[0108] That is to say, after obtaining the error signal at the Kth moment, K is adjusted to K+1, and the process returns to step S202 to execute the next cycle, and the error signal at the next moment is estimated until the fundamental signal s(K)=y(k)-x(k) reaches a stable value, and the cycle is stopped.

[0109] In step S207, the signal parameters of the fundamental wave signal are obtained.

[0110] It should be noted that, in the embodiment of the present application, the implementation process of the above step S207 can refer to the description of the implementation process of the above step S102, which will not be repeated here.

[0111] The electrical signal parameter estimation method of the embodiment of the present application dynamically adjusts the step factor based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments, and obtains the adaptive parameters based on the dynamically adjusted step factor, and then estimates the fundamental signal based on the adaptive parameters. When the frequency of the electrical signal changes rapidly, good response speed and estimation accuracy can be obtained at the same time.

[0112] Figure 4is a block diagram of an electrical signal parameter estimation device according to an exemplary embodiment. Figure 4 The device includes a signal estimation module 301 and a parameter acquisition module 302.

[0113] Among them, the signal estimation module 301 is used to use a variable step-size minimum mean square algorithm to cyclically estimate the fundamental signal based on the received signal until the fundamental signal meets the preset conditions; wherein the fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through the notch filter; the step-size factor of the variable step-size minimum mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments;

[0114] The parameter acquisition module 302 is used to acquire the signal parameters of the fundamental wave signal.

[0115] In some implementations, the signal estimation module 301 includes:

[0116] An autocorrelation parameter acquisition unit 3011 is used to obtain a time average of an autocorrelation function at a Kth moment based on an error signal at a K-1th moment and an error signal at a K-2th moment; wherein K is a positive integer greater than or equal to 3, and the Kth moment and the K-1th moment are adjacent moments;

[0117] A step factor acquisition unit 3012 is used to obtain a step factor at the Kth moment based on the square of the time average value of the autocorrelation function at the Kth moment;

[0118] The adaptive parameter acquisition unit 3013 is used to obtain the adaptive parameter at the Kth moment based on the step size factor at the Kth moment;

[0119] The error signal acquisition unit 3014 is used to calculate the error signal at the Kth moment based on the received signal and the adaptive parameter at the Kth moment;

[0120] The parameter adjustment unit 3015 is used to adjust K to K+1.

[0121] In some implementations, the autocorrelation parameter acquisition unit 3011 is specifically configured to:

[0122] Through the first formula, based on the error signal at the K-1th moment and the error signal at the K-2th moment, the time average value of the autocorrelation function at the Kth moment is obtained; wherein the first formula is expressed as follows:

[0123] φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2)

[0124] Among them, φ(K) is the time average value of the autocorrelation function at the Kth moment, λ is the time average value coefficient of the autocorrelation function, y(K-1) is the error signal at the K-1th moment, and y(K-2) is the error signal at the K-2th moment.

[0125] In some implementations, the step factor acquisition unit 3012 is specifically configured to:

[0126] The step size factor at the Kth moment is obtained by the second formula based on the square of the time average value of the autocorrelation function at the Kth moment; wherein the second formula is expressed as follows:

[0127] μ(K)=pμ(K-1)+βφ^2(K)

[0128] Among them, μ(K) is the step size factor at the Kth moment, p and β are the step size factor coefficients, and φ(K) is the time average of the autocorrelation function at the Kth moment.

[0129] In some implementations, the step factor μ(K) at the Kth moment is greater than a step factor lower limit threshold, and the step factor μ(K) at the Kth moment is less than a step factor upper limit threshold.

[0130] In some implementations, the adaptive parameter acquisition unit 3013 is specifically configured to:

[0131] Through the third formula, based on the step size factor at the Kth moment, the adaptive parameter at the Kth moment is obtained; wherein the third formula is expressed as follows:

[0132] α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2))

[0133] Among them, α(k) is the adaptive parameter at the Kth moment, μ(K) is the step factor at the Kth moment, y(K-1) is the error signal at the K-1th moment, and x(k-2) is the received signal at the K-2th moment.

[0134] In some implementations, the error signal acquisition unit 3014 is specifically configured to:

[0135] The error signal at the Kth moment is calculated based on the received signal and the adaptive parameter at the Kth moment by the fourth formula; wherein the fourth formula is expressed as follows:

[0136] y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2)

[0137] Wherein, y(K) is the error signal at the Kth moment, x(K) is the received signal at the Kth moment, and α(k) is the adaptive parameter at the Kth moment.

[0138] Regarding the device in the above embodiment, the specific manner in which each module performs operations has been described in detail in the embodiment of the method, and will not be elaborated here.

[0139] The electrical signal parameter estimation device of the embodiment of the present application uses a variable step-size minimum mean square algorithm to cyclically estimate the fundamental signal based on the received signal, specifically dynamically adjusts the step factor based on the square of the time average of the autocorrelation function of the error signal at two adjacent moments, and obtains an adaptive parameter based on the dynamically adjusted step factor, and then estimates the fundamental signal based on the adaptive parameter. When the frequency of the electrical signal changes rapidly, it is possible to simultaneously obtain good response speed and estimation accuracy, solving the problem in the related art that it is difficult to simultaneously obtain a faster convergence speed and a smaller steady-state measurement error, and is more suitable for parameter estimation of non-stationary signals. And it can be achieved with the assistance of existing load equipment without adding additional costs.

[0140] Figure 5 1 is a block diagram of an apparatus 1800 for electrical signal parameter estimation according to an exemplary embodiment. For example, the apparatus 1800 may be a mobile phone, a computer, a digital broadcast terminal, a messaging device, a game console, a tablet device, a medical device, a fitness device, a personal digital assistant, etc.

[0141] Reference Figure 5 , the device 1800 may include one or more of the following components: a processing component 1802 , a memory 1804 , a power component 1806 , a multimedia component 1808 , an audio component 1810 , an input / output (I / O) interface 1812 , a sensor component 1814 , and a communication component 1816 .

[0142] The processing component 1802 generally controls the overall operation of the device 1800, such as operations associated with display, phone calls, data communications, camera operations, and recording operations. The processing component 1802 may include one or more processors 1820 to execute instructions to perform all or part of the steps of the above-described method. In addition, the processing component 1802 may include one or more modules to facilitate interaction between the processing component 1802 and other components. For example, the processing component 1802 may include a multimedia module to facilitate interaction between the multimedia component 1808 and the processing component 1802.

[0143] The memory 1804 is configured to store various types of data to support operations on the device 1800. Examples of such data include instructions for any application or method operating on the device 1800, contact data, phone book data, messages, pictures, videos, etc. The memory 1804 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0144] The power component 1806 provides power to the various components of the device 1800. The power component 1806 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device 1800.

[0145] The multimedia component 1808 includes a screen that provides an output interface between the device 1800 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 1808 includes a front camera and / or a rear camera. When the device 1800 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have a focal length and optical zoom capability.

[0146] The audio component 1810 is configured to output and / or input audio signals. For example, the audio component 1810 includes a microphone (MIC), and when the device 1800 is in an operating mode, such as a call mode, a recording mode, and a speech recognition mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 1804 or sent via the communication component 1816. In some embodiments, the audio component 1810 also includes a speaker for outputting audio signals.

[0147] I / O interface 1812 provides an interface between processing component 1802 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: home button, volume button, start button, and lock button.

[0148] The sensor assembly 1814 includes one or more sensors for providing various aspects of the status assessment of the device 1800. For example, the sensor assembly 1814 can detect the open / closed state of the device 1800, the relative positioning of components, such as the display and keypad of the device 1800, the sensor assembly 1814 can also detect the position change of the device 1800 or a component of the device 1800, the presence or absence of user contact with the device 1800, the orientation or acceleration / deceleration of the device 1800, and the temperature change of the device 1800. The sensor assembly 1814 can include a proximity sensor configured to detect the presence of a nearby object without any physical contact. The sensor assembly 1814 can also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 1814 can also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, or a temperature sensor.

[0149] The communication component 1816 is configured to facilitate wired or wireless communication between the device 1800 and other devices. The device 1800 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 1816 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 1816 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0150] In an exemplary embodiment, the device 1800 can be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors or other electronic components to perform the above-mentioned methods.

[0151] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 1804 including instructions, and the instructions can be executed by a processor 1820 of the device 1800 to perform the above method. For example, the non-transitory computer-readable storage medium can be a ROM, a random access memory (RAM), a CD-ROM, a magnetic tape, a floppy disk, an optical data storage device, etc.

[0152] Those skilled in the art will readily appreciate other embodiments of the present invention after considering the specification and practicing the invention disclosed herein. This application is intended to cover any variations, uses or adaptations of the present invention that follow the general principles of the present invention and include common knowledge or customary techniques in the art that are not disclosed in this application. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present invention are indicated by the following claims.

[0153] It should be understood that the present invention is not limited to the exact construction that has been described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the present invention is limited only by the appended claims.

Claims

1. A method for estimating electrical signal parameters, characterized in that: include: A variable step-size minimum mean square algorithm is used to cyclically estimate the fundamental signal based on the received signal until the fundamental signal meets a preset condition; wherein the fundamental signal is the difference between the error signal and the received signal, and the error signal is the output signal of the received signal after passing through a notch filter; the step-size factor of the variable step-size minimum mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments; Acquire signal parameters of the fundamental wave signal.

2. The method according to claim 1, characterized in that The method adopts a variable step-size minimum mean square algorithm to cyclically estimate the fundamental signal based on the received signal; comprising: Based on the error signal at the K-1th moment and the error signal at the K-2th moment, a time average of the autocorrelation function at the K-th moment is obtained; wherein K is a positive integer greater than or equal to 3, and the K-1th moment and the K-2th moment are adjacent moments; Obtaining a step size factor at the Kth moment based on the square of the time average value of the autocorrelation function at the Kth moment; Based on the step size factor at the Kth moment, obtaining an adaptive parameter at the Kth moment; Calculating an error signal at the Kth moment based on the received signal and the adaptive parameter at the Kth moment; The K is adjusted to K+1.

3. The method according to claim 2, characterized in that The method of obtaining the time average of the autocorrelation function at the Kth moment based on the error signal at the K-1th moment and the error signal at the K-2th moment comprises: Through the first formula, based on the error signal at the K-1th moment and the error signal at the K-2th moment, the time average value of the autocorrelation function at the Kth moment is obtained; wherein the first formula is expressed as follows: φ(K)=λφ(K-1)+(1-λ)y(K-1)y(K-2) Among them, φ(K) is the time average value of the autocorrelation function at the Kth moment, λ is the time average value coefficient of the autocorrelation function, y(K-1) is the error signal at the K-1th moment, and y(K-2) is the error signal at the K-2th moment.

4. The method according to claim 2, characterized in that: The step size factor at the Kth moment is obtained based on the square of the time average value of the autocorrelation function at the Kth moment, including: The step size factor at the Kth moment is obtained by the second formula based on the square of the time average value of the autocorrelation function at the Kth moment; wherein the second formula is expressed as follows: μ(K)=pμ(K-1)+βφ^2(K) Among them, μ(K) is the step size factor at the Kth moment, p and β are the step size factor coefficients, and φ(K) is the time average of the autocorrelation function at the Kth moment.

5. The method according to claim 4, characterized in that The step size factor μ(K) at the Kth moment is greater than the step size factor lower limit threshold, and the step size factor μ(K) at the Kth moment is less than the step size factor upper limit threshold.

6. The method according to claim 2, characterized in that The step size factor at the Kth moment is used to obtain the adaptive parameter at the Kth moment, including: Through the third formula, based on the step size factor at the Kth moment, the adaptive parameter at the Kth moment is obtained; wherein the third formula is expressed as follows: α(k)=α(k-1)-2μ(K)α(k-1)y(K-1)(x(k-2)-y(k-2)) Among them, α(k) is the adaptive parameter at the Kth moment, μ(K) is the step factor at the Kth moment, y(K-1) is the error signal at the K-1th moment, and x(k-2) is the received signal at the K-2th moment.

7. The method according to claim 2, characterized in that The calculating the error signal at the Kth moment based on the received signal and the adaptive parameter at the Kth moment includes: The error signal at the Kth moment is calculated based on the received signal and the adaptive parameter at the Kth moment by the fourth formula; wherein the fourth formula is expressed as follows: y(K)=x(K)+α(k)x(K-1)+x(K-2)-α(k)y(K-1)-y(K-2) Wherein, y(K) is the error signal at the Kth moment, x(K) is the received signal at the Kth moment, and α(k) is the adaptive parameter at the Kth moment.

8. An electrical signal parameter estimation device, characterized in that: include: A signal estimation module, configured to use a variable step-size minimum mean square algorithm to cyclically estimate a fundamental signal based on a received signal until the fundamental signal satisfies a preset condition; wherein the fundamental signal is a difference between an error signal and the received signal, and the error signal is an output signal of the received signal after passing through a notch filter; the step-size factor of the variable step-size minimum mean square algorithm is adjustable, and the step-size factor is adjusted based on the square of the time average value of the autocorrelation function of the error signal at two adjacent moments; The parameter acquisition module is used to acquire the signal parameters of the fundamental wave signal.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: include: The storage medium stores a computer program, and when the computer program is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

Images