A method and device for measuring power system frequency based on iterative optimization

Abstract

The present application provides a method and device for measuring the frequency of an electric power system based on iterative optimization, and the measuring device includes: obtaining a sampling signal of the electric power system signal to be measured, and calculating a discrete spectrum sequence after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence; calculating the spectrum position corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence; determining the three spectrum line parameters required for iteration according to the spectrum position; constructing a loss function of the frequency estimation position based on the sampling signal based on the three spectrum lines according to the three spectrum line parameters; iterating with the minimum constructed loss function as the optimization goal until the iteration termination condition is met to obtain the optimal frequency estimation position output; calculating the frequency measurement result of the electric power system signal to be measured according to the found optimal frequency estimation position. The present application can improve the efficiency, accuracy and robustness of the frequency measurement of the electric power system.

Description

Technical Field

[0001] The present application relates to the technical field of power systems, and in particular to a method and device for measuring power system frequency based on iterative optimization. Background Art

[0002] The power system frequency refers to the frequency of the AC power supply of the power system, and is one of the important indicators to ensure the stable operation of the power grid. In order to achieve stable load control of the power system, it is necessary to obtain the power system frequency in real time. Therefore, real-time and accurate power system frequency measurement is crucial to ensure the stable and reliable operation of the power system.

[0003] At present, the frequency measurement of power system is mainly realized by discrete Fourier transform (DFT) of power system sampling signal. However, the discrete Fourier transform method has the problems of large amount of calculation, limited accuracy, long required period, weak anti-harmonic ability, etc. At the same time, since the discrete Fourier transform method usually needs to ignore the negative frequency part of the fundamental wave, it will cause the problem of long-range spectrum leakage. In addition to the power frequency component, the power system signal will contain a large number of harmonics, which will also cause the problem of short-range spectrum leakage. Under the condition of asynchronous sampling, the traditional discrete Fourier transform method is difficult to take into account the problems of fundamental negative frequency and harmonics. Therefore, the actual measurement accuracy and reliability are not high, which will cause long-range spectrum leakage and short-range spectrum leakage problems, which will affect its application in the actual measurement of power system. If the fundamental negative frequency part is considered on the basis of the discrete Fourier transform method to solve the above problems, the complexity of frequency estimation will be greatly increased, the efficiency of estimation will be reduced, and it will be easily interfered by noise, resulting in poor robustness. Summary of the invention

[0004] The purpose of this application is to provide a method and device for measuring the frequency of an electric power system based on iterative optimization, which can take into account the problems of fundamental negative frequency and harmonics, improve the efficiency, accuracy, reliability and robustness of the frequency measurement of the electric power system, and realize fast and accurate measurement of the frequency of the electric power system.

[0005] To achieve the above object, an embodiment of the present application provides a method for measuring power system frequency based on iterative optimization, the steps comprising:

[0006] Step 1: obtain a sampling signal of the power system signal to be tested, and calculate a discrete spectrum sequence X(k) after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence, where k is a spectrum position value;

[0007] Step 2: Calculate the spectrum position k corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence X(k) m ;

[0008] Step 3: According to the spectrum position k m Determine the three-line parameter q required for iteration 1 ,q 2 and q 3 , where k m When q is less than or equal to 1, 1 =1,q 2 =2,q 3 =3; when k m When q is greater than 1, 1 =k m ,q 2 =k s ,q 3 =k t ;

[0009] Step 4: According to the three-line parameter q 1 ,q 2 and q 3 Constructing the frequency estimation position v based on the sampled signal based on three spectral lines e The loss function J(v e ), the loss function J(v e ) is expressed as:

[0010]

[0011] In the formula, Im[P 1 (v e )] and Re[P 1 (v e )] respectively correspond to the imaginary and real parts of the positive frequency part of the fundamental wave, Im[P 2 (v e )] and Re[P 2 (v e )] respectively correspond to the imaginary part and real part of the negative frequency part of the fundamental wave; P 1 and P 2 To use the three-line parameters q 1 ,q 2 and q 3 and the frequency estimated position v e The variables formed;

[0012] Step 5: construct the loss function J(v e ) is the minimum optimization target and iterates until the iteration termination condition is met to obtain the optimal frequency estimation position v e Output;

[0013] Step 6: Estimate the position v based on the optimal frequency found e Calculate the frequency measurement result of the power system signal to be measured.

[0014] In one possible implementation,

[0015] P 1 (v e ) and P 2 (v e ) are:

[0016]

[0017] Among them, X(q 1 ), X(q 2 ) and X(q 3 ) are the q 1 ,q 2 and q 3 Spectral value at the line position.

[0018] In a possible implementation, the steps of step 5 include:

[0019] Step 51: Set the initial iteration interval [α, β] and iteration parameters And the iteration termination condition parameter η;

[0020] Step 52: In the first iteration, set Will Substitute into the loss function J(v e ) and Get C1, C2, that is

[0021] Step 53, determine the size relationship between C1 and C2. If C1>C2, Assign the value of to α, otherwise The value of is assigned to α;

[0022] Step 54: Determine whether If yes, go to step 55, otherwise stop the iteration and And the loss function J(v e ) is used to calculate the gradient value to obtain the optimal frequency estimation position;

[0023]

[0024] In the formula, the symbol Represents the gradient.

[0025] In a possible implementation, the steps of step 55 include:

[0026] Calculate the scale factor in, They are respectively the discrete spectrum sequence X(k) Spectrum value at position;

[0027] According to the current C1 and C2 configuration If C1>C2, assign the value of C2 to C1. Assign the value of And follow Reset and the value of C2; if C1≤C2, the value of C1 is assigned to C2. Assign the value of And follow Reset and the value of C2, where δ 1 , δ 2 They respectively represent the offset correction coefficient for approaching the optimal solution from the left side and the correction coefficient for approaching the optimal solution from the right side calculated using the proportional coefficient χ, and the expressions are:

[0028]

[0029] In a possible implementation, α, β are configured according to the following rule: m = 0, configure α and β according to the formula α = 0.1ε, β = 0.7 + 0.1ε. If k m =1, α=0.4-0.2e, β=1.6+0.2ε, if k m >1, α=u-0.2-0.2ε, β=u+0.2+0.1ε, where ε is the first preset value and its value range is [0, 10η], and u is the second preset value determined according to the ratio of the second largest spectrum amplitude to the maximum spectrum amplitude.

[0030] In a possible implementation manner, the second preset value u is calculated according to the following formula:

[0031]

[0032] Among them, k s is the spectrum value corresponding to the second largest value in the modulus value sequence |X(k)|, |X(k s )| is the second largest value in the modulus value sequence |X(k)|.

[0033] In a possible implementation manner, in step 1, a Hanning window with a length of N is used to truncate the sampled signal.

[0034] In a possible implementation, in step 6, according to formula f 0 =v e ·f s / N Calculate the frequency measurement result f of the power system signal to be measured 0 , fs is the sampling frequency of the sampled signal.

[0035] A power system frequency measurement device based on iterative optimization, comprising:

[0036] The spectrum calculation module is used to obtain a sampling signal of the power system signal to be tested, and calculate a discrete spectrum sequence X(k) after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence, where k is a spectrum position value;

[0037] The maximum spectrum position calculation module is used to calculate the spectrum position k corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence X(k) m ;

[0038] The three spectral line parameter determination modules are used to determine the spectrum position k according to the spectrum position k. m Determine the three-line parameter q required for iteration 1 ,q 2 and q 3 , where k m When q is less than or equal to 1, 1 =1,q 2 =2,q 3 =3; when k m When q is greater than 1, 1 =k m ,q 2 =k s ,q 3 =k t ;

[0039] The loss function building module is used to calculate the loss function according to the three-line parameter q 1 ,q 2 and q 3 Constructing the frequency estimation position v based on the sampled signal based on three spectral lines e The loss function J(v e ), the loss function J(v e ) is expressed as:

[0040] In the formula, Im[P 1 (v e )] and Re[P 1 (v e )] respectively correspond to the imaginary and real parts of the positive frequency part of the fundamental wave, Im[P 2 (v e )] and Re[P 2 (v e )] respectively correspond to the imaginary part and real part of the negative frequency part of the fundamental wave; P 1 and P 2 To use the three-line parameters g 1 ,q2 and g 3 and the frequency estimated position v e The variables formed;

[0041] Iterative optimization module, used to construct the loss function J(v e ) is the minimum optimization target and iterates until the iteration termination condition is met to obtain the optimal frequency estimation position v e Output;

[0042] Frequency calculation module, used to estimate the position v according to the found optimal frequency e Calculate the frequency measurement result of the power system signal to be measured.

[0043] A power system frequency measurement device based on iterative optimization, the device comprising:

[0044] processor;

[0045] a memory for storing processor-executable instructions;

[0046] Wherein, the processor is configured to implement the above method when executing the instructions.

[0047] Compared with the prior art, the advantages of the present application are as follows: the present application first performs windowing and truncating on the power system sampling signal to filter out the interference of the harmonic part on the power system signal spectrum, introduces the fundamental positive frequency and negative frequency parts and adopts a three-spectral line method to jointly construct a loss function of the frequency estimation position, so that not only can the long-range spectrum leakage and short-range spectrum leakage problems caused by the fundamental negative frequency and harmonic components be solved, but also the problems of interval selection errors caused by measurement noise can be avoided, thereby improving the robustness of measurement estimation. At the same time, the loss function of the frequency estimation position under different spectral line parameters is iterated in combination with the iterative optimization process to find the optimal frequency estimation position that minimizes the loss function, which can reduce the complexity of the estimation process, and can quickly and accurately estimate the power system frequency under a small sampling period, thereby taking into account the efficiency, accuracy and robustness of the power system signal frequency estimation. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] The present invention will be described in more detail below based on embodiments and with reference to the accompanying drawings, wherein:

[0049] Figure 1 It is a schematic diagram of the implementation flow of a method for measuring power system frequency based on iterative optimization according to an embodiment of the present application.

[0050] Figure 2 It is a schematic diagram of the implementation flow of a method for measuring power system frequency based on iterative optimization according to another embodiment of the present application.

[0051] Figure 3 It is a schematic diagram of the principle of implementing iterative optimization in this embodiment.

[0052] Figure 4 It is a structural schematic diagram of a power system frequency measurement device based on iterative optimization according to an embodiment of the present application. DETAILED DESCRIPTION

[0053] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments, but the protection scope of the present invention is not limited thereby.

[0054] The iterative optimization algorithm gradually approaches or finds the optimal solution through an iterative process, and can quickly and accurately estimate the target optimal solution. Considering the long-range spectrum leakage problem caused by negative frequency, and considering the simple dual-spectral line method to construct the loss function of the frequency estimation position, there will be problems such as interval selection errors caused by measurement noise at some measurement points. This application introduces the fundamental positive frequency and negative frequency parts, and uses a three-spectral line method to jointly construct the loss function of the frequency estimation position. First, three spectral line parameters are determined according to the spectrum position corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence. The three spectral line parameters are used to construct a loss function of the frequency estimation position that contains both positive and negative frequency parts. Not only can the long-range spectrum leakage and short-range spectrum leakage problems caused by the fundamental negative frequency and harmonic components be solved, but also the interval selection errors caused by measurement noise can be avoided, and the robustness of the measurement estimation can be improved. At the same time, the loss function of the frequency estimation position under different spectral line parameters is iterated in combination with the iterative optimization process to find the optimal frequency estimation position that minimizes the loss function, which can quickly and accurately estimate the power system frequency under a small sampling period and ensure the robustness of the measurement estimation.

[0055] See also Figure 1 , 2 The present application embodiment provides a method for measuring the frequency of a power system based on iterative optimization, the steps comprising:

[0056] Step 1: obtain a sampling signal of the power system signal to be tested, and calculate a discrete spectrum sequence X(k) after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence, where k is a spectrum position value.

[0057] In a possible implementation, a Hanning window is used to truncate the sampling signal. The Hanning window has excellent sidelobe level and sidelobe attenuation rate, and good anti-harmonic characteristics, which can effectively reduce the interference of the harmonic components of the power system sampling signal on the power system signal spectrum.

[0058] In one possible implementation, the power system signal can be represented as a continuous real sinusoidal signal in the time domain: A represents the amplitude, f 0 Indicates frequency, Represents the initial phase, with sampling frequency f s Sample x(t) and truncate the sampled signal using a Hanning window of length N to obtain an N-point discrete sinusoidal signal sequence x(n), n = 0, 1, 2, ..., N-1. After performing a discrete Fourier transform, a discrete spectrum sequence X(k), k = 0, 1, 2, ..., N-1 can be obtained.

[0059] Step 2: Calculate the spectral position k corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence X(k) m .

[0060] In a possible implementation, the modulus sequence |X(k)| of the discrete spectrum sequence X(k) is calculated, and then the k value corresponding to the maximum value in the modulus sequence |X(k)| is obtained to obtain the desired spectrum position k. m .

[0061] Step 3: According to the spectrum position k m Determine the spectral line parameter q required for iteration 1 ,q 2 and q 3 , where k m When q is less than or equal to 1, 1 =1,q 2 =2,q 3 =3; when k m When q is greater than 1, 1 =k m ,q 2 =k s ,q 3 =k t .

[0062] In a possible implementation, the loss function J(v e ) includes the positive frequency part and the negative frequency part of the fundamental wave. The real frequency of the signal will be near the frequency corresponding to the maximum value of the spectrum. When the spectral line position is 0, it represents the DC component. There is no imaginary part, which will cause the loss function to be unable to be calculated, that is, the spectral line with k = 0 cannot participate in the iterative calculation. In the embodiment of the present application, when k = 0, the first, second and third spectral lines are directly selected as the spectral line parameters q 1 ,q 2 and q 3 , when k m If it is greater than 1, select k m , k s , k t Select as the line parameter q 1 ,q 2 and q 3 Participate in subsequent iterative calculations.

[0063] Step 4: According to the three-line parameter q 1 , g 2 and q 3 Constructing the frequency estimation position v based on the sampled signal based on three spectral lines e The loss function J(v e ), loss function J(v e ) is:

[0064]

[0065] In the formula, Im[P 1 (v e )] and Re[P 1 (v e )] respectively correspond to the imaginary and real parts of the positive frequency part of the fundamental wave, Im[P 2 (v e )] and Re[P 2 (v e )] respectively correspond to the imaginary part and real part of the negative frequency part of the fundamental wave; P 1 and P 2 To use the three-line parameters g 1 , g 2 and q 3 and the frequency estimated position v e The variables formed.

[0066] In one possible implementation, P 1 (v e ) and P 2 (v e ) are:

[0067]

[0068] Among them, X(q 1 ), X(q 2 ) and X(q 3 ) are the q 1 ,q 2 and q 3 Spectral value at the line position.

[0069] The embodiment of the present application fully considers the influence of the positive and negative frequency components of the fundamental wave and the problem of robustness of estimation. By constructing the loss function according to the above formulas (1) and (2) for the windowed signal, the positive and negative frequency parts of the fundamental wave are introduced and the frequency estimation position v is constructed based on the three spectral line parameters. e The loss function J(v e ), and then based on the loss function J(v e) can not only effectively solve the long-range spectral leakage and short-range spectral leakage problems, but also avoid the interval selection errors caused by measurement noise, thereby significantly improving the accuracy of power system frequency measurement under harmonic interference.

[0070] Step 5: Use the constructed loss function J(v e ) minimum (i.e. min[J(v e )]) Iterate for the optimization target until the iteration termination condition is met and the optimal frequency estimation position v is obtained e Output.

[0071] In a possible implementation, the optimal frequency estimation position v is obtained by iterative calculation. e The steps include:

[0072] Step 51: Set the initial iteration interval [α, β] and iteration parameters And the iteration termination condition parameter η;

[0073] Step 52: In the first iteration, set Will Substitute into the loss function J(v e ) and Get C1, C2, that is

[0074] Step 53, determine the size relationship between C1 and C2. If C1>C2, The value of is assigned to α, otherwise The value of is assigned to α;

[0075] Step 54: Determine whether If yes, go to step 55, otherwise stop the iteration and And the loss function J(v e ) is used to calculate the optimal frequency estimation position:

[0076]

[0077] Step 55: Reconfigure C1 and C2 and value, and return to step 53.

[0078] The essence of optimization is to substitute different values ​​into the loss function, and then find a more suitable value that minimizes the loss function by comparing the values ​​of the loss function. This value is the optimal solution. This application uses an iterative process to find the optimal iteration parameter value, and then uses the gradient to form a weighted coefficient to perform weighted calculation on the optimal iteration parameter value, which can quickly and accurately obtain the optimal frequency estimation position ve In a possible implementation, the specific steps of step 55 include:

[0079] Calculate the scale factor in, They are respectively the discrete spectrum sequence X(k) Spectrum value at position;

[0080] According to the current C1 and C2 configuration If C1>C2, assign the value of C2 to C1. Assign the value of And follow Reset and the value of C2; if C1≤C2, the value of C1 is assigned to C2. Assign the value of And follow Reset and the value of C2, where δ 1 , δ 2 They represent the offset correction coefficient of the optimal solution from the left and the correction coefficient of the optimal solution from the right, respectively, which are calculated using the proportional coefficient χ. The calculation expressions are:

[0081]

[0082] The position corresponding to the real frequency in the spectrum is related to the ratio of the spectrum line. Through this ratio, the measured value can quickly approach the optimal solution and reduce the number of iterations. In the updating process of , the proportional coefficient χ between the two points is introduced, and the proportional coefficient χ is used to determine the offset correction coefficient δ that approaches the optimal solution from the left and right sides respectively. 1 ,δ 2 , and then use the offset correction factor δ 1 ,δ 2 For C1 and C2 and Updates can greatly improve the speed of iterative optimization and reduce the number of iterations.

[0083] like Figure 3 As shown, the ordinate represents the loss function J(θ), the abscissa represents the frequency estimation position θ, the center point at the bottom of the curve is the optimal frequency estimation position, and the iterative solution process is to quickly find the optimal frequency estimation position through iteration. In this embodiment, the frequency estimation position is continuously iterated by iteratively narrowing the optimization interval based on the proportional coefficient χ until it reaches The iteration ends, and the optimal frequency estimation position is found.

[0084] In a possible implementation, the iteration termination condition parameter η∈[1e-10, 1e-2], the higher the measurement accuracy requirement, the smaller η is, and the lower the measurement accuracy requirement, the larger η is. Keep approaching the true frequency. When the iteration termination condition parameter η is reached, exit the iteration calculation and use the gradient to calculate the optimal frequency estimation position v e

[0085]

[0086] In a possible implementation, α, β are configured according to the following rule: m = 0, configure α and β according to the formula α = 0.1ε, β = 0.7 + 0.1ε. If k m =1, α=0.4-0.2ε, β=1.6+0.2ε, if k m >1, α=u-0.2-0.2ε, β=u+0.2+0.1ε, where ε is the first preset value and its value range is [0, 10η], and u is the second preset value determined according to the ratio of the second largest spectrum amplitude to the maximum spectrum amplitude.

[0087] In one possible implementation, [α, β] and The specific setting rules are shown in the following table.

[0088] Table 1: Parameter setting rules

[0089]

[0090]

[0091] In a possible implementation manner, the value range of ε is [0, 10η], which is set according to the measurement requirements. When the measurement delay requirement is high, the value of ε is large, otherwise, the value of ε is small.

[0092] In a possible implementation manner, the second preset value u is calculated according to the following formula:

[0093]

[0094] Among them, k s is the spectrum value corresponding to the second largest value in the modulus value sequence |X(k)|, |X(k s )| is the second largest value in the modulus value sequence |X(k)|.

[0095] Step 6: Estimate the position v based on the optimal frequency found e Calculate the frequency measurement result of the power system signal to be measured.

[0096] In a possible implementation, the frequency measurement result f of the power system signal to be measured is calculated. 0 The calculation expression is:

[0097] f 0 =v e ·f s / N (6)

[0098] Among them, f s is the sampling frequency of the sampling signal, and N is the number of points for windowing and truncating the sampling signal.

[0099] See also Figure 2 In a possible implementation, the power system signal (continuous signal) is first discretized and windowed to obtain an N-point discrete sequence x(n), and then the discrete sequence x(n) is Fourier transformed and the modulus is calculated to obtain the modulus value sequence |X(k)|, and the spectrum position k corresponding to the maximum modulus value is obtained. m ; According to the spectrum position k m Determine the spectral line parameter q 1 ,q 2 and q 3 , where k m When q is less than or equal to 1, 1 =1,q 2 =1,q 3 =3; when k m When q is greater than 1, 1 =k m ,q 2 =k s ,q 3 =k t ; Set the initial iteration interval [α, β], iteration parameters As well as the iteration termination condition η, the spectral line parameter q 1 ,q 2 and q 3 Substitute it into the loss function constructed by formula (1) and perform iterative optimization calculation until the iteration termination condition parameter η is met to obtain the optimal frequency estimation position:

[0100]

[0101] Finally, the final frequency measurement result of the power system signal to be measured, that is, the power system signal frequency, is calculated according to formula (4).

[0102] In the embodiment of the present application, the power system sampling signal is first windowed and truncated to filter out the interference of the harmonic part on the power system signal spectrum, and the long-range spectrum leakage problem caused by the negative frequency is considered. The fundamental positive frequency and negative frequency parts and three spectrum lines are introduced to jointly construct the loss function, and the loss function is iteratively optimized within a certain interval. The optimal frequency estimation position v is found by iterative calculation to continuously approach the true frequency. e , and then estimate the position v based on the optimal frequency e By calculating the power system frequency, the long-range spectrum leakage and short-range spectrum leakage problems caused by the fundamental negative frequency and harmonic components can be solved under small sampling periods, while avoiding problems such as interval selection errors caused by measurement noise, and minimizing the impact of the fundamental negative frequency on frequency measurement. This enables fast and accurate measurement of the power system signal frequency under small sampling periods, while taking into account calculation speed, accuracy and robustness.

[0103] like Figure 4 As shown, as an implementation of the above method of the embodiment of the present application, the power system frequency measurement device based on iterative optimization in the embodiment of the present application includes:

[0104] The spectrum calculation module is used to obtain a sampling signal of the power system signal to be tested, and calculate a discrete spectrum sequence X(k) after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence, where k is a spectrum position value;

[0105] The maximum spectrum position calculation module is used to calculate the spectrum position k corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence X(k) m ;

[0106] The three spectral line parameter determination modules are used to determine the spectrum position k according to the spectrum position k. m Determine the three-line parameter q required for iteration 1 ,q 2 and q 3 , where k m When q is less than or equal to 1, 1 =1,q 2 =2,q 3 =3; when k m When q is greater than 1, 1 =k m ,q 2 =k s ,q 3 =k t ;

[0107] The loss function building module is used to calculate the loss function according to the three-line parameter q 1 ,q 2 and q 3 Constructing the frequency estimation position v based on the sampled signal based on three spectral linese The loss function J(v e ), the loss function J(v e ) is expressed as,

[0108] In the formula, Im[P 1 (v e )] and Re[P 1 (v e )] respectively correspond to the imaginary and real parts of the positive frequency part of the fundamental wave, Im[P 2 (v e )] and Re[P 2 (v e )] respectively correspond to the imaginary part and real part of the negative frequency part of the fundamental wave; P 1 and P 2 To use the three-line parameters q 1 ,q 2 and q 3 and the frequency estimated position v e The variables formed;

[0109] Iterative optimization module, used to construct the loss function J(v e ) is the minimum optimization target and iterates until the iteration termination condition is met to obtain the optimal frequency estimation position v e Output;

[0110] Frequency calculation module, used to estimate the position v according to the found optimal frequency e Calculate the frequency measurement result of the power system signal to be measured.

[0111] The power system frequency measurement device based on iterative optimization in this embodiment corresponds one to one with the above-mentioned power system frequency measurement device based on iterative optimization, and will not be described one by one here.

[0112] In another embodiment of the present application, a power system frequency measurement device based on iterative optimization includes:

[0113] processor;

[0114] a memory for storing processor-executable instructions;

[0115] The processor is configured to implement the above method when executing instructions. The processor and the memory are directly or indirectly electrically connected to realize data transmission or interaction. For example, these elements can be electrically connected through one or more communication buses or signal buses. The above control methods each include at least one software function module that can be stored in the memory in the form of software or firmware.

[0116] The processor can be an integrated circuit chip with signal processing capabilities. The processor can be a general-purpose processor, including a CPU (Central Processing Unit), NP (Network Processor), etc.; it can also be a digital signal processor, a dedicated integrated circuit, a ready-made programmable gate array or other programmable logic device, a discrete gate or transistor logic device, a discrete hardware component. It can implement or execute the methods, steps and logic block diagrams disclosed in the embodiments of the present invention. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0117] The memory can store various software programs and modules, such as the program instructions / modules corresponding to the image processing method and device provided in the embodiments of the present invention. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, implementing the method in the embodiments of the present application. The memory may include but is not limited to RAM (Random Access Memory), ROM (Read Only Memory), PROM (Programmable Read-Only Memory), EPROM (Erasable Programmable Read-Only Memory), EEPROM (Electric Erasable Programmable Read-Only Memory), etc.

[0118] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0119] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0120] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to produce a computer-implemented process, so that the instructions executed on the computer or other programmable device provide for implementing the functions specified in the flowchart - one or more processes and / or blocks. Figure 1 A step that specifies a function in one or more boxes.

[0121] Although the present invention has been described with reference to preferred embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, the various technical features mentioned in the various embodiments may be combined in any manner as long as there are no structural conflicts. The present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. A method for measuring power system frequency based on iterative optimization, characterized in that the steps include: Step 1: obtain a sampling signal of the power system signal to be tested, and calculate a discrete spectrum sequence X(k) after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence, where k is a spectrum position value; Step 2: Calculate the spectrum position k corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence X(k) m ; Step 3: According to the spectrum position k m Determine the three-line parameters q1, q2 and q3 required for iteration, where k m When k is less than or equal to 1, q1=1, q2=2, q3=3; when k m When greater than 1, q1=k m , q2=k s , q3=k t , ks is the spectral position corresponding to the second largest value in the modulus value sequence |X(k)|; Step 4: construct a frequency estimation position v based on the sampling signal based on the three spectral lines according to the three spectral line parameters q1, q2 and q3 e The loss function J(v e ), the loss function J(v e ) is: In the formula, Im[P1(v e )] and Re[P1(v e )] respectively correspond to the imaginary and real parts of the positive frequency part of the fundamental wave, Im[P2(v e )] and Re[P2(v e )] correspond to the imaginary and real parts of the negative frequency part of the fundamental wave respectively; P1 and P2 are the three-line parameters q1, q2 and q3 and the frequency estimation position v respectively. e The intermediate variables formed; Step 5: construct the loss function J(v e ) is the minimum optimization target and iterates until the iteration termination condition is met to obtain the optimal frequency estimation position v e Output; Step 6: Estimate the position v based on the optimal frequency found e Calculate the frequency measurement result of the power system signal to be measured.

2. The method for measuring power system frequency based on iterative optimization according to claim 1, characterized in that: P1(v e ) and P2(v e ) are: Among them, X(q1), X(q2) and X(q3) are the spectrum values ​​at the positions of q1, q2 and q3 spectrum lines respectively.

3. The method for measuring power system frequency based on iterative optimization according to claim 1, characterized in that: The steps of step 5 include: Step 51: Set the initial iteration interval [α, β] and iteration parameters And the iteration termination condition parameter η; Step 52: In the first iteration, set Will Substitute into the loss function J(v e ) and Get C1, C2, that is Step 53, determine the size relationship between C1 and C2. If C1>C2, The value of is assigned to α, otherwise The value of is assigned to α; Step 54: Determine whether If yes, go to step 55, otherwise stop the iteration and And the loss function J(v e ) is used to calculate the optimal frequency estimation position: In the formula, the symbol represents the gradient; Step 55: Reconfigure C1 and C2 and value, and return to step 53.

4. The method for measuring power system frequency based on iterative optimization according to claim 3, characterized in that: The steps of step 55 include: Calculate the scale factor According to the current C1 and C2 configuration If C1>C2, assign the value of C2 to C1. Assign the value of And follow Reset and the value of C2; if C1≤C2, the value of C1 is assigned to C2. Assign the value of And follow Reset And the value of C2, where δ1 and δ2 respectively represent the offset correction coefficient of the optimal solution approximated from the left and the optimal solution correction coefficient approximated from the right calculated using the proportional coefficient χ, and the calculation expression is:

5. The method for measuring power system frequency based on iterative optimization according to claim 3, characterized in that: α, β are configured according to the following rules: if k m = 0, configure α and β according to the formula α = 0.1ε, β = 0.7 + 0.1ε. If k m =1, α=0.4-0.2ε, β=1.6+0.2ε, if k m >1, α=u-0.2-0.2ε, β=u+0.2+0.1ε, where ε is the first preset value and its value range is [0,10η], and u is the second preset value determined according to the ratio of the second largest spectrum amplitude to the maximum spectrum amplitude.

6. The method for measuring power system frequency based on iterative optimization according to claim 5, characterized in that: The second preset value u is calculated according to the following formula: Among them, k s is the spectrum value corresponding to the second largest value in the modulus value sequence |X(k)|.

7. The method for measuring power system frequency based on iterative optimization according to any one of claims 1 to 6, characterized in that: In step 1, a Hanning window with a length of N is used to truncate the sampled signal.

8. The method for measuring power system frequency based on iterative optimization according to any one of claims 1 to 6, characterized in that: In step 6, according to the formula f0 = v e ·f s / N calculates the frequency measurement result f0, f s is the sampling frequency of the sampling signal, and N is the number of points for windowing and truncating the sampling signal.

9. A power system frequency measurement device based on iterative optimization, characterized in that: include: The spectrum calculation module is used to obtain a sampling signal of the power system signal to be tested, and calculate a discrete spectrum sequence X(k) after windowing and truncating the sampling signal to form a discrete sinusoidal signal sequence, where k is a spectrum position value; The maximum spectrum position calculation module is used to calculate the spectrum position k corresponding to the maximum value of the modulus sequence in the discrete spectrum sequence X(k) m ; The three spectral line parameter determination modules are used to determine the spectrum position k according to the spectrum position k. m Determine the three-line parameters q1, q2 and q3 required for iteration, where k m When k is less than or equal to 1, q1=1, q2=2, q3=3; when k m When greater than 1, q1=k m , q2=k s , q3=k t ; A loss function construction module is used to construct a frequency estimation position v based on the sampled signal based on the three spectral lines according to the three spectral line parameters q1, q2 and q3. e The loss function J(v e ), the loss function J(v e ) is expressed as, In the formula, Im[P1(v e )] and Re[P1(v e )] respectively correspond to the imaginary and real parts of the positive frequency part of the fundamental wave, Im[P2(v e )] and Re[P2(v e )] correspond to the imaginary and real parts of the negative frequency part of the fundamental wave respectively; P1 and P2 are the three-line parameters q1, q2 and q3 and the frequency estimation position v respectively. e The intermediate variables formed; Iterative optimization module, used to construct the loss function J(v e ) is the minimum optimization target and iterates until the iteration termination condition is met to obtain the optimal frequency estimation position v e Output; Frequency calculation module, used to estimate the position v according to the found optimal frequency e Calculate the frequency measurement result of the power system signal to be measured.

10. A power system frequency measurement device based on iterative optimization, characterized in that: The device comprises: processor; a memory for storing processor-executable instructions; Wherein, the processor is configured to implement the method described in any one of claims 1 to 8 when executing the instructions.

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