A method for measuring power frequency phasors in urban substations

Abstract

The present invention discloses a method for measuring power frequency phasors in urban substations, comprising the following steps: S1, establishing a mathematical model of components to be analyzed in the power supply system of the urban substation, and inputting the signal components to be measured into the mathematical model; S2, fitting the power frequency phasor parameters using linear Bessel interpolation to obtain the fundamental frequency, amplitude, and phase; S3, using the fitting result in S2, compensating for a negative spectral image through an iterative process to reduce spectral leakage of the negative image; S4, constructing an energy threshold inequality, and judging whether the ratio of the energy of the interharmonic component to the signal component to be measured meets the threshold based on the result of S3, and directly outputting the result if it meets the threshold; if not, repeating S2-S4 until the threshold is met.

Description

Technical Field

[0001] The present invention relates to the technical field of signal and data analysis, and in particular to a method for measuring power frequency phasors of urban substations. Background Art

[0002] Currently, the integration of high proportions of renewable energy and power electronic equipment generates not only large amounts of harmonic currents within the power system, but also significant interharmonic interference, seriously impacting the measurement of power frequency phasors. Phasor measurement units (PMUs) are essential tools for estimating synchronized phasors in modern power systems. The estimation results obtained by PMUs are defined in IEEE / IEC 60255-118-1.

[0003] In recent years, methods for accurately measuring power-frequency phasors in urban substations with multiple disturbance components have attracted considerable attention. This work proposes a power-frequency phasor measurement technique with low response time and high accuracy, while meeting Class M PMU compliance.

[0004] Traditional power-frequency phasor measurement methods can be broadly categorized as Fourier transform-based and non-Fourier transform-based. Fourier transform-based methods, such as the interpolated discrete Fourier transform (IpDFT) and Taylor-Fourier transform (TFT), can estimate power-frequency phasors to a certain extent. However, these methods fail to achieve satisfactory results when applied to the multi-disturbance environments of urban substations. Non-Fourier transform-based methods, such as the Prony method and Kalman filtering, typically impose strict requirements on the model order and are unable to dynamically track power-frequency phasors in the presence of multiple disturbances. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for measuring power frequency phasors in urban substations. Based on linear Bessel interpolation, the method can measure power frequency phasors with a frequency resolution of 12.5 Hz in accordance with Class M PMU compliance, taking into account multi-component interference such as harmonics and interharmonics.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for measuring power frequency phasors in urban substations can measure power frequency phasors with a frequency resolution of 12.5 Hz in accordance with Class M PMU compliance, while taking into account multi-component interference such as harmonics and interharmonics. The method includes the following steps:

[0008] S1. Establish a mathematical model of the components to be analyzed in the power supply system of the urban substation, and input the signal components to be measured into the mathematical model;

[0009] S2. Use linear Bessel interpolation to fit the power frequency phasor parameters to obtain the fundamental frequency, amplitude and phase;

[0010] S3, using the fitting results in S2, compensating the spectrum negative image through an iterative process to reduce the spectrum leakage of the negative image;

[0011] S4. Construct an energy threshold inequality. Based on the result of S3, determine whether the ratio of the energy of the interharmonic component to the signal component to be measured meets the threshold. If so, directly output the result; if not, repeat S2-S4 until the threshold is met, and finally measure and obtain the power frequency vector.

[0012] Preferably, in S1, the mathematical model is:

[0013]

[0014] Where h=1,2,...,H is the highest order of harmonics; a h 、 are the amplitude and phase of the harmonics respectively; N is the data window length and is set to an even number; n is the sequence number of the discrete sampling point.

[0015] Preferably, in S1, usually, the signal component to be measured needs to be windowed to limit the calculation time, and the main lobe and side lobe characteristics of the window function are used to limit spectrum leakage. The signal component to be measured is windowed using the window function and subjected to discrete Fourier transform to obtain a representation of the signal component to be measured in the frequency domain:

[0016]

[0017] Where m = [0, N-1]; w(n) is the Kaiser window function; W(n) is the discrete Fourier transform form of the Kaiser window function; x(n) is the windowed signal.

[0018] Preferably, S2 comprises the following steps:

[0019] S21, establishing a relationship between the correction coefficient ξ, phasor parameters (amplitude and phase) and the linear polynomial;

[0020] S22, solving the linear polynomial to obtain the correction coefficient ξ, amplitude and phase;

[0021] S23. Using the correction coefficient ξ, the fundamental frequency is obtained by solving the relationship between the correction coefficient ξ and the fundamental frequency.

[0022] Preferably, in S21, the relationship between the correction coefficient ξ, the phasor parameter and the linear polynomial is:

[0023]

[0024] a1≈P x (ξ)(|X w (m-σ)|+|Xw (m)|+|X w (m+σ)|)|m=m m ;

[0025]

[0026] The selection of “±” and the value of σ in the above relationship are determined by the position of the second largest spectral line. If the index number corresponding to the second largest spectral line position is m m +1, take "+", σ=1; if the index number is m m -1, take “-”, σ=-1.

[0027] Where, P d 、P x and P p are linear polynomials for calculating fundamental frequency, amplitude and phase respectively; m m M is the index number corresponding to the maximum spectrum line obtained by DFT; f is the frequency polynomial order; p mf is the polynomial coefficient; σ is a constant, which takes the value of -1 or 1; m = [0, N-1], N is the data window length, and is set to an even number; a1 is the amplitude corresponding to the power frequency phasor; is the phase corresponding to the power frequency phasor.

[0028] Preferably, in S22, the linear polynomial needs to rely on the fundamental frequency dependency F fr , amplitude dependence A f , phase dependence P f and fitting factor F f The fitting solution is obtained;

[0029] Specifically, the frequency depends on the fundamental frequency F fr and fitting factor F f Conduct M f The polynomial P is obtained by fitting the polynomial d , by solving the polynomial P d Get the correction coefficient ξ;

[0030] The amplitude a1 corresponding to the power frequency phasor is determined by the amplitude dependence A f and fitting factor F f Conduct M a The polynomial P is obtained by fitting the polynomial x , substitute the correction coefficient ξ into the polynomial P x The measured value of the amplitude a1 corresponding to the power frequency phasor is obtained;

[0031] Phase corresponding to the power frequency phasor Through the phase dependence P f and fitting factor F f Conduct Mp The polynomial P is obtained by fitting the polynomial p , substitute the correction coefficient ξ into the polynomial P p The phase corresponding to the power frequency phasor is obtained The measured value.

[0032] in,

[0033]

[0034] F f (n) = w(n)·N / 2π;

[0035]

[0036] p f (m) = arg{W(m)};

[0037]

[0038] Where n bin is the required number of frequency bands; W(m) is the spectrum of the Kaiser window; w(n) is the Kaiser window; I0(β) is the first kind of Bessel function; M a is the order of the amplitude polynomial; M p is the order of the phase polynomial.

[0039] Preferably, in S23, the relationship between the correction coefficient ξ and the fundamental frequency is:

[0040] f1=(m m ±ξ)Δf;

[0041] The selection of “±” and the value of σ in the relationship are determined by the position of the second largest spectral line. If the index number corresponding to the second largest spectral line position is m m +1, take "+", σ=1; if the index number is m m -1, take "-", σ = -1;

[0042] Where f1 is the fundamental frequency; Δf is the frequency resolution, Δf = 1 / N.

[0043] Preferably, S3 comprises the following steps:

[0044] S31. The discrete Fourier transform (DFT) of the signal fundamental frequency can be represented based on the positive and negative images of the spectrum:

[0045]

[0046] S32. Compensate the spectrum negative image by iteration:

[0047]

[0048] Where, is the spectrum positive image; is the negative image of the spectrum; j is the number of iterations, j = 1, 2, ..., J; PID is the correction coefficient ξ, the amplitude a1 corresponding to the power frequency phasor, and the phase corresponding to the power frequency phasor A simplified representation of the calculation process.

[0049] Preferably, in S4, the result is made to satisfy the energy threshold inequality through an iterative process. The iterative process ends until the threshold is satisfied, and the calculation result of the power frequency phasor is returned. The energy threshold inequality is:

[0050]

[0051] in,

[0052] After eliminating the interference of the negative image, the interference of the intermediate harmonic of the component to be measured on the power frequency phasor measurement result is reduced through L iterations. The initial value of the iteration is:

[0053]

[0054] Where, is the interharmonic component; is the threshold; J is the maximum number of iterations of spectral negative image compensation.

[0055] Compared with the prior art, the present invention has the following beneficial effects:

[0056] This invention targets multi-disturbance scenarios in urban substations and aims to accurately measure power-frequency phasor parameters in these substations. It constructs a linear Bessel interpolation method based on first-kind Bessel functions. This proposed interpolation method can be used for power-frequency phasor measurement in multi-disturbance scenarios in urban substations. Compared to existing technologies, the main calculation steps of this invention involve fitting linearized Bessel functions, which is less susceptible to frequency-domain fluctuations. Therefore, this method exhibits superior stability and is less susceptible to harmonics and interharmonics.

[0057] In summary, compared with the existing technology, the present invention can significantly reduce the spectrum leakage effect, the interference of interharmonics on the measurement results and the mutual coupling between the two disturbances, and the measurement accuracy of the power frequency phasor amplitude and frequency is greatly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1 It is a schematic diagram of the process of the present invention;

[0059] Figure 2 Schematic diagram of the relationship between positive and negative images of the spectrum in an embodiment of the present invention;

[0060] Figure 3Schematic diagram of the results of the out-of-band interference test under static conditions of an embodiment of the present invention; (a) is the total vector error TVE test result, (b) is the absolute frequency error FE test result, and (c) is the absolute frequency change rate error RFE test result. DETAILED DESCRIPTION

[0061] The technical solutions in the embodiments of the present invention will be clearly and completely described 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; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0062] See also Figure 1-Figure 3 , a method for measuring power frequency phasors of urban substations, comprising the following steps:

[0063] S1. Establish a mathematical model of the components to be analyzed in the power supply system of the urban substation, and input the signal components to be measured into the mathematical model, wherein the signal components to be measured include power frequency components and harmonics.

[0064]

[0065] Where h=1,2,...,H is the highest order of harmonics; a h 、 are the amplitude and phase of the harmonics respectively; N is the data window length and is set to an even number; n is the sequence number of the discrete sampling point.

[0066] Typically, the signal components to be measured need to be windowed to limit the calculation time of the algorithm, and the characteristics of the main lobe and side lobes of the window function are used to limit spectrum leakage.

[0067] The windowed signal can be expressed as:

[0068] x w (n) = x(n)w(n);

[0069] The windowed signal is transformed into the discrete Fourier transform (DFT) process to obtain the representation of the signal component to be measured in the frequency domain:

[0070]

[0071] Where m = [0, N-1]; w(n) is the Kaiser window function; W(n) is the discrete Fourier transform form of the Kaiser window function; x(n) is the windowed signal.

[0072] S2. Use linear Bessel interpolation to fit the power frequency phasor parameters to obtain the fundamental frequency, amplitude and phase.

[0073] Because the choice of window function affects algorithm performance under multi-component aliasing conditions, the Kaiser window can adjust the mainlobe and sidelobe ratios by adjusting the window parameters. This helps extract features during power-frequency phasor measurements and, to a certain extent, suppresses spectral leakage effects.

[0074] The calculation expression of the Kaiser window is:

[0075]

[0076]

[0077] Where I0(·) is the Bessel function of the first kind; β is the Kaiser window parameter; b is the sequence number of the series in the Bessel function of the first kind; and G is the gamma function.

[0078] Due to incoherent sampling in real-world situations, the fundamental frequency of the signal component to be measured lies between two consecutive highest spectral lines. The urgent task is to determine a correction coefficient ξ to accurately locate the fundamental frequency. Existing methods for calculating correction coefficients typically use multi-spectral line interpolation based on a cosine family window, but this is not applicable to first-kind Bessel functions. The present invention employs a numerical method to process first-kind Bessel functions and obtain the corresponding phasor parameters.

[0079] First, establish the correction coefficient ξ, the amplitude a1 corresponding to the power frequency phasor, and the phase corresponding to the power frequency phasor The relationship between the linear polynomial, the correction coefficient ξ, the amplitude a1 corresponding to the power frequency phasor, and the phase corresponding to the power frequency phasor The value of can be calculated by the number of the largest spectral line:

[0080]

[0081] a1≈P x (ξ)(|X w (m-σ)|+|X w (m)|+|X w (m+σ)|)|m=m m ;

[0082]

[0083] The selection of “±” and the value of σ in the above relationship are determined by the position of the second largest spectral line. If the index number corresponding to the second largest spectral line position is m m +1, take "+", σ=1; if the index number is m m -1, take “-”, σ=-1.

[0084] Where, P d 、P x and Pp are linear polynomials for calculating fundamental frequency, amplitude and phase respectively, obtained by spectrum fitting; m m M is the index number corresponding to the maximum spectrum line obtained by DFT; f is the frequency polynomial order; p mf are polynomial coefficients; σ is a constant, taking the value of -1 or 1; m = [0, N-1], where N is the data window length and is set to an even number.

[0085] Secondly, the linear polynomial is solved to obtain the correction coefficient ξ, amplitude and phase.

[0086] As shown in Table 1, the linear polynomial needs to rely on the fundamental frequency dependence F fr , amplitude dependence A f , phase dependence P f and fitting factor F f The fitting solution is obtained.

[0087] Table 1 Correspondence between dependency, fitting factors and polynomials

[0088]

[0089] According to Table 1, the frequency depends on the fundamental frequency F fr and fitting factor F f Conduct M f (frequency polynomial order) polynomial fitting to obtain polynomial P d , by solving the polynomial P d Get the correction coefficient ξ.

[0090] The amplitude a1 corresponding to the power frequency phasor is determined by the amplitude dependence A f and fitting factor F f Conduct M a The polynomial P is obtained by fitting the polynomial of order (amplitude polynomial order) x , substitute the correction coefficient ξ into the polynomial P x The measured value of the amplitude a1 corresponding to the power frequency phasor is obtained.

[0091] Phase corresponding to the power frequency phasor Through the phase dependence P f and fitting factor F f Conduct M p The polynomial P is obtained by fitting the polynomial of order (Phase polynomial order) p , substitute the correction coefficient ξ into the polynomial P p The phase corresponding to the power frequency phasor is obtained The measured value.

[0092] in,

[0093]

[0094] F f (n) = w(n)·N / 2π;

[0095]

[0096] p f (m) = arg{W(m)};

[0097]

[0098] Where n bin is the required number of frequency bands; W(m) is the spectrum of the Kaiser window; w(n) is the Kaiser window; I0(β) is the Bessel function of the first kind.

[0099] Third, using the correction coefficient ξ, the fundamental frequency is obtained by solving the relationship between the correction coefficient ξ and the fundamental frequency;

[0100] The relationship between the correction coefficient ξ and the fundamental frequency is:

[0101] f1=(m m ±ξ)Δf;

[0102] Where f1 is the fundamental frequency; Δf is the frequency resolution, Δf = 1 / N.

[0103] The correction coefficient ξ calculated by the above steps is substituted into the relationship to solve the fundamental frequency f1. The selection of "±" and the value of σ in the relationship are determined by the position of the second largest spectral line. If the index number corresponding to the second largest spectral line position is m m +1, take "+", σ=1; if the index number is m m -1, take “-”, σ=-1.

[0104] S3. Using the fitting results in S2, the spectrum negative image is compensated through an iterative process to reduce the spectrum leakage of the negative image.

[0105] like Figure 2 As shown in Figure 2, according to the convolution theorem, the characteristic of the windowed signal in the frequency domain is the transformation of the window function spectrum in the spatial position.

[0106] Therefore, the discrete Fourier transform (DFT) of the signal fundamental frequency can be represented based on the positive and negative images of the spectrum:

[0107]

[0108] Assuming that the maximum number of iterations used to compensate the negative image is J, the spectrum negative image can be expressed as follows:

[0109]

[0110] in,

[0111]

[0112] The iterative process of compensating the negative spectrum image can be described as follows:

[0113]

[0114] Where, is the spectrum positive image; is the negative image of the spectrum; j is the number of iterations, j = 1, 2, ..., J; PID is the correction coefficient ξ, the amplitude a1 corresponding to the power frequency phasor, and the phase corresponding to the power frequency phasor A simplified representation of the calculation process.

[0115] The specific iterative process can be represented by the following table:

[0116] Table 2 Pseudo code table of spectrum negative image compensation based on PID

[0117]

[0118]

[0119] S4. Construct an energy threshold inequality. Based on the result of S3, determine whether the ratio of the energy of the interharmonic component to the measured signal component meets the threshold. If so, there is no need to consider the interharmonic component and the result is output directly. If not, return to S2 to construct the interharmonic component and filter it out, and then perform S3-S4 until the threshold is met.

[0120] After eliminating the interference of the negative image, the interference of the intermediate harmonics of the signal component to be measured on the power frequency phasor measurement result is reduced through L iterations. The initial value of the iteration is:

[0121]

[0122] Where, is the constructed interharmonic component; is the threshold; J is the maximum number of iterations for compensating the negative image.

[0123] During the iteration process, when the interharmonic components of the construction The energy of the signal component X w The ratio of (m) exceeds the threshold When , the accuracy of the result is unacceptable. L iterations are performed until the result satisfies the energy threshold inequality. When the result satisfies the energy threshold inequality, the iteration process ends, and the substation power frequency phasor is finally measured.

[0124] The energy threshold inequality is:

[0125]

[0126] The specific iterative calculation process is as follows:

[0127] Table 3 Pseudo code table of energy threshold iteration

[0128]

[0129]

[0130] Where, E e is the interharmonic component energy; f OOB 、a OOB 、 are the frequency, amplitude and phase of the approximate interharmonics respectively; iPID is the iterative process of PID and compensated negative image.

[0131] In a specific embodiment, to test the detection performance of the present invention, the basic estimation accuracy of the signal components by the evaluation algorithm is selected.

[0132] The single-frequency signal was set to 50 Hz, with interharmonic frequencies ranging from 10 to 25 Hz and 75 to 100 Hz. Gaussian white noise was added to the measured signal components, with a signal-to-noise ratio (SNR) of 60 dB. Each method was calculated 10 times according to its nominal period, with a reporting rate of 50 fps. The maximum values ​​of the total vector error (TVE), absolute frequency error (FE), and absolute rate of change error (RFE) of the estimated results are shown in Figure 1. Figure 3 As shown in the figure, the red horizontal line represents the measurement compliance of the Class M PMU in IEEE / IEC 60255-118-1. The method in this embodiment is denoted by A, and the advanced algorithms in the prior art: TFT, MW-FIR, CS-TFM, iIpDFT, and iIpDFT-dc are considered as comparison algorithms.

[0133] The maximum TVE, FE and RFE obtained by the present invention are 0.6667%, 0.0081 Hz and 0.0265 Hz / s respectively.

[0134] The maximum TVE, FE, and RFE obtained by iIpDFT are 1.5757%, 0.0139Hz, and 0.0544Hz / s, respectively; the maximum TVE, FE, and RFE obtained by TFT are 4.4162%, 0.0196Hz, and 0.1628Hz / s, respectively; the maximum TVE, FE, and RFE obtained by CS-TFM are 1.2724%, 0.0095Hz, and 0.0673Hz / s, respectively; the maximum TVE, FE, and RFE obtained by MW-FIR are 2.3331%, 0.0144Hz, and 0.1011Hz / s, respectively; and the maximum TVE, FE, and RFE obtained by iIpDFT-dc are 1.6267%, 0.0152Hz, and 0.1192Hz / s, respectively. Compared with existing technologies, the errors of the present invention are all smaller than those of existing algorithms, highlighting the superiority of the present invention in measurement accuracy.

[0135] The test signal is as follows:

[0136]

[0137] Where, f = 50HZ; f i is the frequency of the interharmonics; are the phases of the fundamental wave and interharmonics, respectively, and are set to random numbers between 0 and 2π.

[0138] The calculation expression of the total vector error is:

[0139]

[0140] The calculation expression of the absolute frequency error is:

[0141] FE(n)=f measured (n)-f ref (n);

[0142] The calculation expression of the absolute frequency change rate error is:

[0143]

[0144] Where, are the real and imaginary PMU estimates at reporting time n, respectively; X r (n), X i (n) are the reference values ​​of the real and imaginary parts of the phasor at the reporting time n; n is the number corresponding to the reporting time; f measured (n), f ref (n) are the measured frequency and frequency reference value respectively.

Claims

1. A method for measuring power frequency phasors in urban substations, characterized in that: The following steps are involved: S1. Establish a mathematical model of the components to be analyzed in the power supply system of the urban substation, and input the signal components to be measured into the mathematical model; S2. Use linear Bessel interpolation to fit the power frequency phasor parameters to obtain the fundamental frequency, amplitude and phase: S21. Establish correction coefficient , the relationship between phasor parameters and linear polynomials; S22. Solve the linear polynomial to obtain the correction coefficient , amplitude and phase: ； ； ； In the above relationship, "The selection and The value of is determined by the position of the second largest spectral line. If the index number corresponding to the second largest spectral line position is ,Pick" ”、 ; If the index number is ,Pick" ”、 ; Where, 、 and are the linear polynomials for calculating fundamental frequency, amplitude and phase respectively; The index number corresponding to the maximum spectrum line obtained by DFT; is the frequency polynomial order; are the polynomial coefficients; is a constant, taking the value of -1 or 1; , is the data window length and is set to an even number; is the amplitude corresponding to the power frequency phasor; is the phase corresponding to the power frequency phasor; S23. Using the correction factor , through the correction factor The fundamental frequency is obtained by solving the relationship between . S3, using the fitting results in S2, compensating the spectrum negative image through an iterative process to reduce the spectrum leakage of the negative image; S4. Construct an energy threshold inequality. Based on the result of S3, determine whether the ratio of the energy of the interharmonic component to the signal component to be measured meets the threshold. If so, directly output the result; if not, repeat S2-S4 until the threshold is met, and finally measure and obtain the power frequency vector.

2. A method for measuring power frequency phasors of urban substations according to claim 1, characterized in that: In S1, the mathematical model is: ； Where, is the highest order of harmonics; 、 are the amplitude and phase of the harmonics respectively; is the data window length and is set to an even number; is the sequence number of the discrete sampling point.

3. A method for measuring power frequency phasors of urban substations according to claim 2, characterized in that: In S1, the window function is used to add a window to the signal component to be measured, and after discrete Fourier transform, the representation of the component to be measured in the frequency domain is obtained: ； Where, ; is the Kaiser window function; is the discrete Fourier transform form of the Kaiser window function; is the windowed signal.

4. The method for measuring power frequency phasors of urban substations according to claim 1, characterized in that: In S22, the linear polynomial needs to rely on the fundamental frequency dependency , amplitude dependence , phase dependence and fitting factors The fitting solution is obtained; Specifically, the frequency depends on the fundamental frequency and fitting factors conduct The polynomial is obtained by fitting the polynomial , by solving the polynomial Get the correction factor ; Amplitude corresponding to the power frequency phasor Through amplitude dependence and fitting factors conduct The polynomial is obtained by fitting the polynomial , the correction factor Substitute the polynomial The amplitude corresponding to the power frequency phasor is obtained The measured value of Phase corresponding to the power frequency phasor Through phase dependence and fitting factors conduct The polynomial is obtained by fitting the polynomial , the correction factor Substitute the polynomial The phase corresponding to the power frequency phasor is obtained The measured value of in, ; ; ; ; ; Where, is the required number of frequency bands; is the spectrum of the Kaiser window; For Caesar windows; is the Bessel function of the first kind; is the order of the amplitude polynomial; is the order of the phase polynomial.

5. The method for measuring power frequency phasors of urban substations according to claim 1, characterized in that: In S23, the correction coefficient The relationship with the fundamental frequency is: ； In the relational expression "The selection and The value of is determined by the position of the second largest spectral line. If the index number corresponding to the second largest spectral line position is ,Pick" ”、 ; If the index number is ,Pick" ”、 ; Where, is the fundamental frequency; is the frequency resolution, .

6. The method for measuring power frequency phasors of urban substations according to claim 1, characterized in that: S3 includes the following steps: S31. The discrete Fourier transform DFT of the signal fundamental frequency is represented by the positive and negative images based on the spectrum: ； S32. Compensate the spectrum negative image by iteration: ； Where, is the spectrum positive image; is the negative image of the spectrum; is the number of iterations, ; is the correction factor , the amplitude corresponding to the power frequency phasor , the phase corresponding to the power frequency phasor A simplified representation of the calculation process.

7. The method for measuring power frequency phasors of urban substations according to claim 1, characterized in that: In S4, the energy threshold inequality is: ； in, The initial value of the iteration is: Where, is the interharmonic component; is the threshold; The maximum number of iterations for spectral negative image compensation.

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