Method, device, equipment and medium for measuring short-time window frequency with suppression of interharmonic interference

Abstract

The present invention relates to the technical field of wide-area measurement of electric power systems, and in particular to a short-time window frequency measurement method, device, equipment and medium for suppressing interharmonic interference. The method comprises: designing two bandpass filters with different passband centers, calculating the passband gain ratio of the two bandpass filters, and establishing a mapping relationship between frequency and gain ratio; using two bandpass filters to filter signal sampling values ​​to obtain two groups of signals, and constructing Hankel matrices for the two groups of signals respectively; solving the generalized inverse matrix of a certain Hankel matrix, and multiplying it with another Hankel matrix to obtain a square matrix, calculating the characteristic root of the square matrix to obtain the amplitude ratio of the two groups of signals; solving the actual fundamental frequency value according to the mapping relationship between frequency and gain ratio, and performing median filtering on the measured fundamental frequency to eliminate random interference. In the present invention, the influence of sub / supersynchronous interharmonics can be suppressed in a relatively short time window, and accurate measurement of the fundamental frequency can be achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of wide-area measurement of electric power systems, and in particular to a short-time window frequency measurement method, device, equipment and medium for suppressing interharmonic interference. Background Art

[0002] With the construction and development of new power systems, the penetration rate of new energy sources continues to increase, the inertia constant of the system gradually decreases, and the inertia response support capacity is insufficient, affecting the frequency stability of the system. In order to measure the frequency support capacity of the system after the access of new energy sources, it is necessary to carry out inertia evaluation research. There is a close coupling relationship between the frequency and inertia of the power system. In order to accurately evaluate the inertia level of the system, the system frequency must be accurately measured first. However, after the large-scale access of new energy sources, broadband oscillations occur frequently, especially the sub / super-synchronous interharmonics that induce synchronous oscillations are near the fundamental wave, causing the measured fundamental frequency to fluctuate and deviate from the actual value, affecting the accurate evaluation of inertia.

[0003] In addition, after the system is disturbed, the voltage signal waveform is severely distorted. If the calculation window contains a distortion process, the measured fundamental frequency will change suddenly, and the voltage signal with inconsistent parameters before and after the disturbance cannot be simultaneously characterized. In order to reduce the influence of distortion on fundamental frequency measurement, the calculation window of frequency measurement should be shorter. Therefore, how to get rid of the influence of sub / supersynchronous interharmonics in a shorter time window and achieve accurate frequency measurement is the key and difficulty of inertia evaluation.

[0004] Most existing frequency measurement algorithms use the DFT (Discrete Fourier Transform) machine improvement algorithm, which measures the phasor and uses the phasor phase angle difference to obtain the fundamental frequency. In order to eliminate the influence of sub- / super-synchronous interharmonics, the calculation window of the DFT method needs to be increased, which leads to a longer frequency measurement response time when the signal is distorted, affecting the inertia evaluation of the disturbance process. In addition, modern spectrum estimation methods such as matrix beam are also used for frequency measurement. However, these methods are difficult to accurately obtain the number of frequency components in the signal when there is noise interference, which leads to inaccurate construction of the signal subspace in the spectrum estimation, causing the fundamental frequency to deviate seriously from the actual value.

[0005] The information disclosed in this background technology section is only intended to deepen the understanding of the overall background technology of the present invention, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a short-time window frequency measurement method, device, equipment and medium for suppressing interharmonic interference, thereby effectively solving the problems in the background technology.

[0007] In order to achieve the above object, the technical solution adopted by the present invention is: a short-time window frequency measurement method for suppressing interharmonic interference, comprising the following steps:

[0008] Design two bandpass filters with different passband centers, calculate the passband gain ratio of the two bandpass filters, and establish a mapping relationship between frequency and gain ratio;

[0009] The signal sampling values ​​are filtered using the two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and Hankel matrices of the same dimension are constructed for the two groups of filtered signals respectively;

[0010] Solving the generalized inverse matrix of a certain Hankel matrix and multiplying it with another Hankel matrix to obtain a square matrix, and calculating the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals;

[0011] The amplitude ratio is used as the gain ratio and substituted into the mapping relationship between the frequency and the gain ratio to solve the actual fundamental frequency value, and the measured fundamental frequency is subjected to median filtering to eliminate random interference.

[0012] Furthermore, the passband gain ratio of the two bandpass filters is:

[0013]

[0014] In the formula, H bp1 (f) represents the bandpass filter h bp1 Gain, H bp2 (f) represents the bandpass filter h bp2 The gain of the two bandpass filters is represented by R(f).

[0015] Furthermore, in establishing the mapping relationship between the frequency and the gain ratio, a one-to-one mapping relationship between the frequency and the gain ratio is established based on cubic spline interpolation, including:

[0016] f=G i (R) = a i +b i (RR i )+c i (RR i ) 2 +d i (RR i ) 3 ;

[0017] Where f is the frequency, R represents the gain ratio of the two bandpass filters; i 、b i 、c i d iis the fitting coefficient of the i-th subspace; G i (R) is the fitting function of the i-th subspace, R is the independent variable, and f is the dependent variable.

[0018] Furthermore, the use of the two bandpass filters to filter the signal sampling values ​​to obtain two groups of signals with consistent frequency components but inconsistent amplitudes includes the following steps:

[0019] The power system signal model is characterized as:

[0020]

[0021] In the formula, X0, f0 and are the amplitude, frequency and initial phase angle of the fundamental component respectively; X i 、f i and are the amplitude, frequency and initial phase angle of the i-th interference component respectively; H represents the number of harmonic and interharmonic interference components in the signal;

[0022] Use the two bandpass filters to filter the above signals respectively:

[0023]

[0024]

[0025] Wherein, x(n) represents the sampled value of the signal x(t), N is the order of the filter, y1(n) and y2(n) are the nth sampled values ​​after filtering by the two bandpass filters;

[0026] According to the filtering principle, the corresponding time domain signal is:

[0027]

[0028]

[0029] In the formula, H bp1 (f0) and H bp2 (f0) represent the bandpass filter h bp1 and h bp2 The gain at frequency f0, H bp1 (f i ) and H bp2 (f i ) represent the bandpass filter h bp1 and h bp2 At frequency f i The gain at .

[0030] Furthermore, the Hankel matrices of the same dimension are constructed for the two groups of filtered signals respectively as follows:

[0031]

[0032]

[0033] Where y1(·) and y2(·) are two groups of signal sampling values ​​after bandpass filtering; Y1 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y1(0), y1(1), …, y1(M-1); Y2 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y2(0), y2(1), …, y2(M-1); M and L are positive integers.

[0034] Furthermore, the calculating of the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals comprises the following steps:

[0035] The sinusoidal signal in the time domain signal is expressed using the Euler formula:

[0036]

[0037]

[0038] Its sampling value is expressed as:

[0039]

[0040]

[0041] In the formula, p i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y1, respectively, containing amplitude and phase information; i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y2, including amplitude and phase information; i and are the changes of the phasors of the positive and negative frequency components of the i-th frequency signal in a sampling interval; H is the number of interference components in the signal;

[0042]

[0043]

[0044]

[0045] Where, T s is the sampling interval;

[0046] Y1 and Y2 are expressed as:

[0047] Y1=Z1PZ2;

[0048] Y2=Z1QZ2;

[0049] in:

[0050]

[0051]

[0052]

[0053]

[0054] Multiply the pseudo-inverse of Y2 by Y1, and we get:

[0055]

[0056] Where Z1 is the left multiplication matrix after the decomposition of matrices Y1 and Y2, Z2 is the right multiplication matrix after the decomposition of matrices Y1 and Y2, P is the diagonal matrix composed of the phasors of the components in signal y1; Q is the diagonal matrix composed of the phasors of the components in signal y2; the superscript + indicates the pseudo-inverse of the matrix, and the superscript - indicates the inverse matrix;

[0057] therefore:

[0058]

[0059] Solution The characteristic root of y1 and y2 can be obtained as follows:

[0060]

[0061] Further, solving the actual fundamental frequency value according to the mapping relationship between the frequency and the gain ratio includes:

[0062] Searching for the amplitude ratio of the fundamental wave signal among the amplitude ratios of the frequency components of the two groups of the signals according to the gain ratio range;

[0063] The amplitude ratio is used as the gain ratio and substituted into the mapping relationship between the frequency and the gain ratio to solve the actual fundamental frequency value.

[0064] The present invention also includes a short-time window frequency measurement device for suppressing interharmonic interference, using the above method, the measurement device includes:

[0065] A mapping relationship unit, used to design two bandpass filters with different passband centers, calculate the passband gain ratio of the two bandpass filters, and establish a mapping relationship between frequency and gain ratio;

[0066] A Hankel matrix unit is used to filter the signal sampling values ​​using the two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and to construct Hankel matrices of the same dimension for the two groups of filtered signals respectively;

[0067] An amplitude ratio unit, used for solving a generalized inverse matrix of a certain Hankel matrix, and multiplying it with another Hankel matrix to obtain a square matrix, and calculating the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals;

[0068] The filtering unit is used to substitute the amplitude ratio as the gain ratio into the mapping relationship between the frequency and the gain ratio, solve the actual fundamental frequency value, and perform median filtering on the measured fundamental frequency to eliminate random interference.

[0069] The present invention also includes a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method described above is implemented.

[0070] The present invention also includes a storage medium on which a computer program is stored. When the computer program is executed by a processor, the method described above is implemented.

[0071] The beneficial effects of the present invention are as follows: the present invention searches for the amplitude ratio of the fundamental signal in the characteristic root according to the gain ratio range of the bandpass filter, substitutes the amplitude ratio into the relationship between the frequency and the gain ratio to solve the actual fundamental frequency value, and performs median filtering on the measured fundamental frequency to eliminate the influence of random interference. This method can suppress the influence of sub / supersynchronous interharmonics in a shorter time window, realize accurate measurement of the fundamental frequency, thereby improving the accuracy of inertia evaluation and providing support for the frequency stability of the new power system. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0073] Figure 1 is a flow chart of the method in Example 1;

[0074] Figure 2 It is a structural schematic diagram of the device in Example 1;

[0075] Figure 3 is a flow chart of the method in Example 2;

[0076] Figure 4 Schematic diagram of filtering characteristics of a low-pass filter in Example 2;

[0077] Figure 5 Schematic diagram of the gain ratio of the bandpass filter in Example 2;

[0078] Figure 6 This is a schematic diagram of the test results of the field recording data in Example 2;

[0079] Figure 7 A schematic diagram of the structure of a computer device. DETAILED DESCRIPTION

[0080] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0081] Embodiment 1:

[0082] like Figure 1 As shown: A short-time window frequency measurement method for suppressing interharmonic interference includes the following steps:

[0083] Design two bandpass filters with different passband centers, calculate the passband gain ratio of the two bandpass filters, and establish a mapping relationship between frequency and gain ratio;

[0084] The signal sampling values ​​are filtered using two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and the Hankel matrices of the same dimension are constructed for the two groups of filtered signals respectively;

[0085] Solve the generalized inverse matrix of a Hankel matrix and multiply it with another Hankel matrix to get a square matrix. Calculate the characteristic root of the square matrix to get the amplitude ratio of each frequency component of the two groups of signals.

[0086] The amplitude ratio is used as the gain ratio and substituted into the mapping relationship between frequency and gain ratio to solve the actual fundamental frequency value. The measured fundamental frequency is median filtered to eliminate random interference.

[0087] According to the gain ratio range of the bandpass filter, the amplitude ratio of the fundamental signal is searched in the characteristic root, and the amplitude ratio is substituted into the relationship between frequency and gain ratio to solve the actual fundamental frequency value, and the measured fundamental frequency is median filtered to eliminate the influence of random interference. This method can suppress the influence of sub / supersynchronous interharmonics in a shorter time window, achieve accurate measurement of fundamental frequency, thereby improving the accuracy of inertia evaluation and providing support for frequency stability of new power systems.

[0088] In this embodiment, the passband gain ratio of the two bandpass filters is:

[0089]

[0090] In the formula, H bp1 (f) represents the bandpass filter h bp1 Gain, H bp2 (f) represents the bandpass filter h bp2 The gain of the two bandpass filters is represented by R(f).

[0091] In establishing a mapping relationship between frequency and gain ratio, a one-to-one mapping relationship between frequency and gain ratio is established based on cubic spline interpolation, including:

[0092] f=G i (R) = a i +b i (RR i )+c i (RR i ) 2 +d i (RR i ) 3 ;

[0093] Where f is the frequency, R represents the gain ratio of the two bandpass filters; i 、b i 、c i d i is the fitting coefficient of the i-th subspace; G i (R) is the fitting function of the i-th subspace, R is the independent variable, and f is the dependent variable.

[0094] As a preferred embodiment of the above, two bandpass filters are used to filter the signal sampling values ​​to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, including the following steps:

[0095] The power system signal model is characterized as:

[0096]

[0097] In the formula, X0, f0 and are the amplitude, frequency and initial phase angle of the fundamental component respectively; X i 、f i and are the amplitude, frequency and initial phase angle of the i-th interference component respectively; H represents the number of harmonic and interharmonic interference components in the signal;

[0098] Use two bandpass filters to filter the above signals separately:

[0099]

[0100]

[0101] Wherein, x(n) represents the sampled value of the signal x(t), N is the order of the filter, y1(n) and y2(n) are the nth sampled values ​​after filtering by the two bandpass filters;

[0102] According to the filtering principle, the corresponding time domain signal is:

[0103]

[0104]

[0105] In the formula, H bp1 (f0) and H bp2 (f0) represent the bandpass filter h bp1 and h bp2 The gain at frequency f0, H bp1 (f i ) and H bp2 (f i ) represent the bandpass filter h bp1 and h bp2 At frequency f i The gain at .

[0106] In this embodiment, Hankel matrices of the same dimension are constructed for the two groups of filtered signals respectively:

[0107]

[0108]

[0109] Where y1(·) and y2(·) are two groups of signal sampling values ​​after bandpass filtering; Y1 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y1(0), y1(1), …, y1(M-1); Y2 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y2(0), y2(1), …, y2(M-1); M and L are positive integers.

[0110] The calculation of the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals includes the following steps:

[0111] The sinusoidal signal in the time domain is expressed using the Euler formula:

[0112]

[0113]

[0114] Its sampling value is expressed as:

[0115]

[0116]

[0117] In the formula, p i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y1, respectively, containing amplitude and phase information; i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y2, including amplitude and phase information; i and are the changes of the phasors of the positive and negative frequency components of the i-th frequency signal in a sampling interval; H is the number of interference components in the signal;

[0118]

[0119]

[0120]

[0121] Where, T s is the sampling interval;

[0122] Y1 and Y2 are expressed as:

[0123] Y1=Z1PZ2;

[0124] Y2=Z1QZ2;

[0125] in:

[0126]

[0127]

[0128]

[0129]

[0130] Multiply the pseudo-inverse of Y2 by Y1, and we get:

[0131]

[0132] Where Z1 is the left multiplication matrix after the decomposition of matrices Y1 and Y2, Z2 is the right multiplication matrix after the decomposition of matrices Y1 and Y2, P is the diagonal matrix composed of the phasors of the components in signal y1; Q is the diagonal matrix composed of the phasors of the components in signal y2; the superscript + indicates the pseudo-inverse of the matrix, and the superscript - indicates the inverse matrix;

[0133] therefore:

[0134]

[0135] Solution The characteristic root of y1 and y2 can be obtained as follows:

[0136]

[0137] As a preferred embodiment of the above embodiment, according to the mapping relationship between frequency and gain ratio, solving the actual fundamental frequency value includes:

[0138] Searching for the amplitude ratio of the fundamental wave signal among the amplitude ratios of the frequency components of the two groups of signals according to the gain ratio range;

[0139] The amplitude ratio is used as the gain ratio and substituted into the mapping relationship between frequency and gain ratio to solve for the actual fundamental frequency value.

[0140] like Figure 2 As shown, this embodiment also includes a short-time window frequency measurement device for suppressing interharmonic interference. Using the above method, the measurement device includes:

[0141] A mapping relationship unit is used to design two bandpass filters with different passband centers, calculate the passband gain ratio of the two bandpass filters, and establish a mapping relationship between frequency and gain ratio;

[0142] The Hankel matrix unit is used to filter the signal sampling values ​​using two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and to construct Hankel matrices of the same dimension for the two groups of filtered signals respectively;

[0143] Amplitude ratio unit, used to solve the generalized inverse matrix of a Hankel matrix, and multiply it with another Hankel matrix to obtain a square matrix, and calculate the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals;

[0144] The filtering unit is used to substitute the amplitude ratio as the gain ratio into the mapping relationship between the frequency and the gain ratio to solve the actual fundamental frequency value, and perform median filtering on the measured fundamental frequency to eliminate random interference.

[0145] Embodiment 2:

[0146] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. Figure 3 The figure is a schematic flow chart of a short-time window frequency measurement method for suppressing sub- / super-synchronous interharmonic interference provided by an embodiment of the present invention, the method comprising:

[0147] Step 1: Design two bandpass filters with different passband centers, calculate the passband gain ratio of the two filters, and establish a one-to-one mapping relationship between frequency and gain ratio based on cubic spline interpolation.

[0148] In order to avoid the influence of the long filter window length on the frequency measurement of the disturbance process, the calculation window of the bandpass filter in the embodiment of the present invention should not exceed 2 cycles. Under this window length, the existing filter design method is difficult to obtain a bandpass filter that meets the filtering requirements. Therefore, the present invention adopts a method of first designing a low-pass filter and then performing frequency shifting to obtain a bandpass filter.

[0149] There are many methods for designing low-pass filters, including window function method, equiripple design method, etc. Figure 4 The schematic diagram of the low-pass filter designed by the window function method is given. After obtaining the low-pass filter, the design of the band-pass filter can be realized using the following formula:

[0150]

[0151] In the formula, h lp represents a low-pass filter, h bp represents a bandpass filter, f p Indicates the passband center of the bandpass filter.

[0152] In the present invention, two bandpass filters need to be designed, so as long as f p Take two different values, denoted as f p1 and f p2 , the two bandpass filters are denoted as h bp1 and h bp2 For the convenience of frequency measurement, f p1 and f p2 The frequency of the fundamental wave should be symmetrical. p1 =48, f p2 =52. Obviously, in the frequency domain, the fundamental component is bp1 On the right side of h bp2 On the left side of f p1 ~f p2 The gain ratio of the bandpass filter in the range can be expressed as:

[0153]

[0154] In the formula, H bp1 (f) represents the bandpass filter h bp1 Gain, H bp2 (f) represents the bandpass filter h bp2 The gain of the two bandpass filters is shown in Figure 2. Figure 5As shown, it is obvious that the frequency and gain ratio are one-to-one corresponding, so there is the following relationship:

[0155] f=G(R)=R -1 (f) (3)

[0156] In the formula, G(R) represents the functional relationship between gain ratio and frequency, R is the independent variable, and f is the dependent variable; R -1 (f) represents the inverse function of R(f). The actual functional relationship of G(R) is unknown, and the present invention uses cubic spline interpolation to establish the relationship between the two.

[0157] When the filter spectrum is analyzed according to formula (2) to solve the gain ratio, a series of discrete points (f i ,R i )(0≤i≤K), forming K subintervals, each subinterval is interpolated by a cubic spline function, and an interpolation function sequence is obtained to represent the one-to-one mapping relationship between gain ratio and frequency.

[0158] In order to avoid excessive jitter at the subspace breakpoints after interpolation, a polynomial of no more than degree 3 is used for interpolation in each subinterval:

[0159] f=G i (R) = a i +b i (RR i )+c i (RR i ) 2 +d i (RR i ) 3 (4)

[0160] In the formula, a i 、b i 、c i d i is the fitting coefficient of the i-th subspace; G i (R) is the fitting function of the ith subspace. The cubic spline interpolation method is relatively mature and will not be described in detail in this invention. i and R i+1 When , the fundamental frequency can be substituted into the above formula.

[0161] Step 2: Use the two bandpass filters in step 1 to filter the signal sampling values ​​to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and construct Hankel matrices of the same dimension for the two groups of filtered signals.

[0162] The power system signal model can be characterized as:

[0163]

[0164] In the formula, X0, f0 and are the amplitude, frequency and initial phase angle of the fundamental component respectively; X i 、f i and are the amplitude, frequency and initial phase angle of the i-th interference component respectively; H represents the number of interference components such as harmonics and interharmonics in the signal.

[0165] The two bandpass filters obtained in step 1 are used to filter the above signals respectively:

[0166]

[0167] Wherein, x(n) represents the sampling value of the signal x(t), N is the order of the filter, and y1(n) and y2(n) are the nth sampling values ​​after filtering by the two bandpass filters.

[0168] According to the filtering principle, the time domain signals corresponding to equations (6) and (7) are:

[0169]

[0170] If the ratio of the fundamental signal amplitudes in equation (8) and equation (9) can be obtained, it can be substituted into equation (4) to obtain the fundamental frequency.

[0171] To do this, we first need to construct the Hankel matrix using the filtered sample values:

[0172]

[0173] Wherein, Y1 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y1(0), y1(1), ..., y1(M-1); Y2 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y2(0), y2(1), ..., y2(M-1).

[0174] Step 3: Solve the generalized inverse matrix of a Hankel matrix in step 2, and multiply it with another Hankel matrix to obtain a square matrix. Calculate the characteristic roots of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals in step 2.

[0175] The sinusoidal signals in equations (8) and (9) can be expressed using the Euler formula as follows:

[0176]

[0177] The sampling values ​​of equations (12) and (13) can be expressed as:

[0178]

[0179] In the formula, p i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y1, respectively, containing amplitude and phase information; i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y2, including amplitude and phase information; i and They are the changes of the phasors of the positive and negative frequency components of the i-th frequency signal in a sampling interval:

[0180]

[0181]

[0182] Where, T s is the sampling interval.

[0183] Y1 and Y2 can be expressed as:

[0184] Y1=Z1PZ2 (19)

[0185] Y2=Z1QZ2 (20)

[0186] Where:

[0187]

[0188] Multiply the pseudo-inverse of Y2 by Y1, and we get:

[0189]

[0190] Where Z1 is the left multiplication matrix after the decomposition of matrices Y1 and Y2, Z2 is the right multiplication matrix after the decomposition of matrices Y1 and Y2, P is the diagonal matrix composed of the phasors of the components in signal y1; Q is the diagonal matrix composed of the phasors of the components in signal y2; the superscript + indicates the pseudo-inverse of the matrix, and the superscript - indicates the inverse matrix;

[0191] therefore,

[0192]

[0193] From the above formula, we can see that we only need to solve The characteristic root of y1 and y2 can be obtained as follows:

[0194]

[0195] Simply substitute the value of equation (27) into equation (4) to get the fundamental frequency value. Therefore, solve the generalized inverse matrix of matrix Y2 in step 2 and multiply it with matrix Y1 to get a square matrix. Calculate the characteristic roots of the square matrix to get the amplitude ratio of each frequency component in signals y1 and y2.

[0196] Step 4: Search the amplitude ratio of the fundamental signal in the characteristic root of step 3 according to the gain ratio range of step 1, substitute the amplitude ratio into the relationship between the frequency and the gain ratio of step 1 to solve the actual fundamental frequency value, and perform median filtering on the measured fundamental frequency to eliminate the influence of random interference.

[0197] Due to the presence of random noise interference in the actual signal, the position of the fundamental amplitude ratio in formula (26) is not fixed, and it needs to be searched. Considering that the range of the gain ratio is generally very small, between 0.98 and 1.02 in the present invention, it is sufficient to find the inner value in this range in the amplitude ratio matrix of formula (26). If there are multiple values ​​within this range, the average value is solved and the value is substituted into formula (4) to obtain the fundamental frequency.

[0198] In addition, due to the influence of random noise, the measured fundamental frequency often has burrs, so the present invention uses median filtering to eliminate the influence of the interference and smooth the fundamental frequency value.

[0199] In order to illustrate the effectiveness of the proposed short-time window phasor measurement method, the following simulation tests were carried out:

[0200] The interharmonic components of 40Hz and 70Hz with a content of 10% are superimposed on the fundamental signal, and the fundamental frequency is offset by 0.1Hz. The signal model is:

[0201]

[0202] The signal sampling rate is 1kHz, the calculation window length is 2 cycles 40ms, L = 20, and the frequency measurement is performed using the proposed method. The test shows that the frequency error is 10 -7 The order of magnitude is reduced, eliminating the influence of sub / supersynchronous interharmonics on the fundamental frequency. Considering the existence of random interference in the actual system, 70dB noise is superimposed on the above signal. The test shows that the frequency error fluctuates between ±0.01Hz, indicating that the proposed method has certain anti-noise performance.

[0203] To further verify the practicability of the proposed method, the field recording data was analyzed using the proposed method. The spectrum analysis of the recording data showed that there were sub- / super-synchronous interharmonic components of 25Hz and 75Hz in the signal. The frequency measurement results are shown in Figure 2. Figure 6As shown in the figure, it can be seen that the proposed method can track the dynamic changes of frequency, get rid of the influence of sub / super synchronous interharmonics, and the frequency fluctuation range does not exceed 0.005Hz in steady state. Therefore, the proposed method can be used to measure the fundamental frequency in actual systems.

[0204] See also Figure 7 A computer device 400 provided in an embodiment of the present application includes: a processor 410 and a memory 420, wherein the memory 420 stores a computer program executable by the processor 410, and when the computer program is executed by the processor 410, the above method is executed.

[0205] The embodiment of the present application further provides a storage medium 430 on which a computer program is stored. When the computer program is run by the processor 410, the above method is executed.

[0206] Among them, the storage medium 430 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 red-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, disk or optical disk.

[0207] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. "Multiple" means two or more, unless otherwise clearly and specifically defined.

[0208] In the present invention, unless otherwise clearly specified and limited, the terms "installed", "connected", "connected", "fixed" and the like should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0209] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner. In addition, those skilled in the art may combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples, unless they are contradictory.

[0210] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, segment or portion of code that includes one or more executable instructions for implementing the steps of a specific logical function or process, and the scope of the preferred embodiments of the present invention includes alternative implementations in which functions may not be performed in the order shown or discussed, including performing functions in a substantially simultaneous manner or in the reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present invention belong.

[0211] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in combination with these instruction execution systems, devices or apparatuses. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in combination with these instruction execution systems, devices or apparatuses. More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic devices), a portable computer disk box (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium and then editing, interpreting or processing in other suitable ways if necessary, and then stored in a computer memory.

[0212] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0213] A person skilled in the art may understand that all or part of the steps in the method for implementing the above-mentioned embodiment may be completed by instructing related hardware through a program, and the program may be stored in a computer-readable storage medium, which, when executed, includes one or a combination of the steps of the method embodiment.

[0214] The storage medium mentioned above may be a read-only memory, a magnetic disk or an optical disk, etc. Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and cannot be understood as limiting the present invention. A person of ordinary skill in the art may change, modify, replace and modify the above embodiments within the scope of the present invention.

Claims

1. A short-time window frequency measurement method for suppressing interharmonic interference, characterized in that: The steps include: Design two bandpass filters with different passband centers, calculate the passband gain ratio of the two bandpass filters, and establish a mapping relationship between frequency and gain ratio; The signal sampling values ​​are filtered using the two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and Hankel matrices of the same dimension are constructed for the two groups of filtered signals respectively; Solving the generalized inverse matrix of a certain Hankel matrix and multiplying it with another Hankel matrix to obtain a square matrix, and calculating the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals; The amplitude ratio is used as the gain ratio and substituted into the mapping relationship between the frequency and the gain ratio to solve the actual fundamental frequency value, and the measured fundamental frequency is subjected to median filtering to eliminate random interference; The method of filtering the signal sampling values ​​using the two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes comprises the following steps: The power system signal model is characterized as: In the formula, X0, f0 and are the amplitude, frequency and initial phase angle of the fundamental component respectively; X i 、f i and are the amplitude, frequency and initial phase angle of the i-th interference component respectively; H represents the number of harmonic and interharmonic interference components in the signal; The signal x(t) is filtered using the two bandpass filters: Wherein, x(n) represents the sampled value of the signal x(t), N is the order of the filter, y1(n) and y2(n) are the nth sampled values ​​after filtering by the two bandpass filters; According to the filtering principle, the corresponding time domain signal is: In the formula, H bp1 (f0) and H bp2 (f0) represent the bandpass filter h bp1 and h bp2 The gain at frequency f0, H bp1 (f i ) and H bp2 (f i ) represent the bandpass filter h bp1 and h bp2 At frequency f i Gain at The Hankel matrices of the same dimension are constructed for the two groups of filtered signals respectively: Where y1(·) and y2(·) are two groups of signal sampling values ​​after bandpass filtering; Y1 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y1(0), y1(1), ..., y1(M-1); Y2 is the (M-L+1)×L Hankel matrix composed of sampling values ​​y2(0), y2(1), ..., y2(M-1); M and L are positive integers; The calculating of the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals comprises the following steps: The sinusoidal signal in the time domain signal is expressed using the Euler formula: Its sampling value is expressed as: In the formula, p i and are the phasors of the positive and negative frequency components of the i-th frequency signal in signal y1, respectively, containing amplitude and phase information; q i and are the phasors of the positive and negative frequency components of the ith frequency signal in signal y2, including amplitude and phase information; i and are the changes of the phasors of the positive and negative frequency components of the i-th frequency signal in a sampling interval; H is the number of interference components in the signal; Where, T s is the sampling interval; Y1 and Y2 are expressed as: Y1=Z1PZ2; Y2=Z1QZ2; in: Multiply the pseudo-inverse of Y2 by Y1, and we get: Where Z1 is the left multiplication matrix after the decomposition of matrices Y1 and Y2, Z2 is the right multiplication matrix after the decomposition of matrices Y1 and Y2, P is the diagonal matrix composed of the phasors of the components in signal y1; Q is the diagonal matrix composed of the phasors of the components in signal y2; the superscript + indicates the pseudo-inverse of the matrix, and the superscript - indicates the inverse matrix; therefore: Solution The characteristic root of , we get the amplitude ratio of the fundamental components in y1 and y2:

2. The short-time window frequency measurement method for suppressing interharmonic interference according to claim 1 is characterized in that: The passband gain ratio of the two bandpass filters is: In the formula, H bp1 (f) represents the bandpass filter h bp1 Gain, H bp2 (f) represents the bandpass filter h bp2 The gain of the two bandpass filters is represented by R(f).

3. The short-time window frequency measurement method for suppressing interharmonic interference according to claim 2 is characterized in that: In the establishing of the mapping relationship between the frequency and the gain ratio, a one-to-one mapping relationship between the frequency and the gain ratio is established based on cubic spline interpolation, including: f=G i (R)=a i +b i (R-R i )+c i (R-R i ) 2 +d i (R-R i ) 3 ; Where f is the frequency, R represents the gain ratio of the two bandpass filters; a i 、b i 、c i d i is the fitting coefficient of the i-th subspace; G i (R) is the fitting function of the i-th subspace, R is the independent variable, and f is the dependent variable.

4. The short-time window frequency measurement method for suppressing interharmonic interference according to claim 1 is characterized in that: According to the mapping relationship between the frequency and the gain ratio, solving the actual fundamental frequency value includes: Searching for the amplitude ratio of the fundamental wave signal among the amplitude ratios of the frequency components of the two groups of the signals according to the gain ratio range; The amplitude ratio is used as the gain ratio and substituted into the mapping relationship between the frequency and the gain ratio to solve the actual fundamental frequency value.

5. A short-time window frequency measurement device for suppressing interharmonic interference, characterized in that: Using the method according to any one of claims 1 to 4, the measuring device comprises: A mapping relationship unit, used to design two bandpass filters with different passband centers, calculate the passband gain ratio of the two bandpass filters, and establish a mapping relationship between frequency and gain ratio; A Hankel matrix unit is used to filter the signal sampling values ​​using the two bandpass filters to obtain two groups of signals with consistent frequency components but inconsistent amplitudes, and to construct Hankel matrices of the same dimension for the two groups of filtered signals respectively; An amplitude ratio unit, used for solving a generalized inverse matrix of a certain Hankel matrix, and multiplying it with another Hankel matrix to obtain a square matrix, and calculating the characteristic root of the square matrix to obtain the amplitude ratio of each frequency component of the two groups of signals; The filtering unit is used to substitute the amplitude ratio as the gain ratio into the mapping relationship between the frequency and the gain ratio, solve the actual fundamental frequency value, and perform median filtering on the measured fundamental frequency to eliminate random interference.

6. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the method according to any one of claims 1 to 4 is implemented.

7. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the method according to any one of claims 1 to 4 is implemented.

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