Harmonic parameter measuring method, system and equipment for electric energy metering under power distribution network

Abstract

The invention discloses a harmonic parameter measurement method, system and device for electric energy metering under a power distribution network, and the method comprises the steps: carrying out the discrete sampling, obtaining a DC signal, and obtaining harmonic component data; by taking the side lobe peak level and the side lobe asymptotic attenuation rate of the cosine window as optimization targets, optimizing the parameters of the combined cosine window by adopting an immune algorithm to obtain a six-item five-order combined cosine window; combining the advantages of a multiplication window and a convolution window to construct a self-multiplication convolution optimization window; the optimization window is applied to harmonic analysis of discrete Fourier transform iterative interpolation, and harmonic parameter estimation and correction are completed through double-spectral-line iterative interpolation. According to the method, the combined cosine window parameters are optimized by adopting an immune algorithm, then the advantages of a multiplication window and a convolution window are combined to construct a self-multiplication convolution optimization window, the self-multiplication convolution optimization window is applied to harmonic analysis of FFT iterative interpolation, and estimation and correction of harmonic parameters are completed through double-spectral-line iterative interpolation. Therefore, the harmonic parameters under the direct-current power distribution network can be quickly and accurately obtained.

Description

Technical Field

[0001] The present application relates to the technical field of electric power signal parameter measurement, and in particular to a harmonic parameter measurement method, system and device for electric energy metering in a distribution network. Background Art

[0002] With the rapid development of power electronics technology, DC distribution network has gradually become an important form of distribution network development. Distributed power sources such as photovoltaic and wind power and DC loads are connected to the DC distribution network through power electronics devices, generating modulated harmonics on the DC bus generated by the switching operation of components such as IGBT, which leads to a decrease in the power quality of the DC distribution network and harms the electrical equipment in the DC distribution network. Therefore, fast and accurate measurement of harmonics in the system is of great significance to harmonic suppression, signal monitoring and harmonic governance of the power grid. Summary of the invention

[0003] In order to quickly and accurately obtain various harmonic parameters in a DC distribution network, the present application provides a harmonic parameter measurement method, system and equipment for electric energy metering in a distribution network. The present application adopts an immune algorithm to optimize the combined cosine window parameters, and then combines the advantages of the multiplication window and the convolution window to construct a self-multiplication convolution optimization window. The self-multiplication convolution optimization window is applied to harmonic analysis of FFT iterative interpolation, and the estimation and correction of harmonic parameters are completed through dual-spectrum iterative interpolation, so as to quickly and accurately obtain the harmonic parameters in the DC distribution network.

[0004] This application is implemented through the following technical solutions:

[0005] A harmonic parameter measurement method for electric energy metering in a distribution network, the harmonic parameter measurement method comprising:

[0006] Obtaining a DC signal by discrete sampling, and acquiring harmonic component data in the DC signal;

[0007] Taking the sidelobe peak level and the asymptotic attenuation rate of the cosine window as the optimization targets, the immune algorithm is used to optimize the combined cosine window parameters and a six-term fifth-order combined cosine window is obtained.

[0008] Based on the optimized six fifth-order combined cosine windows, a self-multiplication convolution optimization window is constructed by combining the advantages of the multiplication window and the convolution window;

[0009] The self-convolution optimization window is applied to the harmonic analysis of discrete Fourier transform iterative interpolation, and the harmonic parameter estimation and correction are completed through double-spectrum iterative interpolation.

[0010] In some implementations, the discrete sampling to obtain a DC signal and obtaining harmonic component data in the DC signal specifically includes:

[0011] Discretely sample the voltage or current signal in the power grid to obtain the DC signal under different DC charging piles;

[0012] The sampled DC signal is subjected to variational mode decomposition to obtain a DC component and a harmonic component corresponding to the DC signal, thereby obtaining harmonic component data in the DC signal.

[0013] In some implementations, the sidelobe peak level and the sidelobe asymptotic attenuation rate of the cosine window are taken as optimization targets, and the immune algorithm is used to optimize the combined cosine window parameters to obtain six fifth-order combined cosine windows, specifically including:

[0014] The side lobe performance index of the window is taken as the optimization object, and the number of cosine window items is determined to be 6;

[0015] Taking the sidelobe asymptotic attenuation rate and the sidelobe peak level as optimization targets, and the window coefficient as a feasible solution to the optimization target, and establishing an affinity function based on the relationship between the optimization target and the feasible solution;

[0016] Obtaining various parameters and their weights in the affinity function;

[0017] Determining constraints of feasible solutions to the optimization objective;

[0018] Determine the initial values ​​of various parameters of the immune algorithm;

[0019] The immune algorithm is used to optimize the coefficients of the six-term fifth-order combined cosine window function to obtain the six-term fifth-order combined cosine window function with the best sidelobe performance.

[0020] In some embodiments, the affinity function is expressed as:

[0021] Function min = -(θ·|G|+(1-θ)·|E|)

[0022] Among them, G is the maximum sidelobe peak level, E is the sidelobe attenuation rate, and θ is the weight;

[0023] The feasible solution of the optimization objective, i.e. the constraint condition of the window coefficient, is:

[0024]

[0025] Among them, 0<a h <1, H represents the number of terms in the window function, a h Represents the coefficients of each term of the window function;

[0026] The time domain expression of the optimized six-term fifth-order combined cosine window function is:

[0027]

[0028] Where m = 0, 1, 2, ..., N-1, M represents the length of the window, w OSTC (m) represents the function value at the mth sample point, and N is the length of the signal.

[0029] In some implementations, the six fifth-order combined cosine windows obtained based on the optimization are combined with the advantages of the multiplication window and the convolution window to construct a self-multiplication convolution optimization window, specifically including:

[0030] Set the multiplication window order to 2 to obtain a 2nd-order, 6-term, 5th-order combined cosine self-squared window;

[0031] The spectrum function of the second-order six-term fifth-order combined cosine self-squared window is obtained by discrete Fourier transformation;

[0032] The spectrum function of the second-order six-term fifth-order combined cosine squared window is subjected to a self-product operation, that is, a self-convolution operation of the second-order six-term fifth-order combined cosine squared window is realized to obtain a self-convolution optimization window.

[0033] In some embodiments, the self-convolution optimization window is applied to the harmonic analysis of discrete Fourier transform iterative interpolation, and harmonic parameter estimation and correction are completed by double-spectrum iterative interpolation, specifically including:

[0034] Performing FFT frequency sweep on the harmonic components in the DC signal to obtain the rough estimated initial frequency of each harmonic, and setting the initial frequency interval to obtain the corresponding frequencies of the two spectral lines after interpolation, and respectively calculating the phasor values ​​corresponding to the initial frequency and the frequencies of the two spectral lines after interpolation, and taking the absolute values ​​thereof to obtain the corresponding amplitudes; respectively comparing the amplitudes of the three, selecting the frequency corresponding to the maximum spectral line as the initial value of iteration, and performing iterative optimization. During the iterative optimization process, the frequency obtained in the previous iteration is used as the initial frequency for the next iteration, and the frequency interval of the next iteration is half of the frequency interval of the previous iteration, until the number of iterations meets the preset value, and the final harmonic frequency and frequency interval are obtained;

[0035] The final obtained harmonic frequency, harmonic phase and harmonic amplitude are corrected to obtain harmonic parameters.

[0036] In some embodiments, the harmonic frequency correction formula is:

[0037] f re_i =k1·|Δf re +Δε

[0038] Among them, k1 is the spectrum line on the left side of the center frequency, Δf is the frequency interval, Δf re represents the interval between two spectral lines after the double-line interpolation iteration, Δε represents the estimated error of the offset, and Δε=(γ+0.5)·Δf re;

[0039] The harmonic phase correction formula is:

[0040]

[0041] The harmonic amplitude correction formula is:

[0042]

[0043] In a second aspect, the present application proposes a harmonic parameter measurement system for electric energy metering in a distribution network, the harmonic parameter measurement system comprising:

[0044] A sampling and analysis unit, the sampling and analysis unit is used to obtain a DC signal by discrete sampling and obtain harmonic component data in the DC signal;

[0045] A window parameter optimization unit, wherein the window parameter optimization unit takes the sidelobe peak level and the sidelobe asymptotic attenuation rate of the cosine window as optimization targets, and uses an immune algorithm to optimize the combined cosine window parameters to obtain a six-term fifth-order combined cosine window;

[0046] A self-multiplication convolution unit, wherein the self-multiplication convolution unit constructs a self-multiplication convolution optimization window based on the optimized six-term fifth-order combined cosine window and combining the advantages of the multiplication window and the convolution window;

[0047] And, a harmonic analysis unit, which applies the self-convolution optimization window to the harmonic analysis of discrete Fourier transform iterative interpolation, and completes the estimation and correction of harmonic parameters through double-spectrum iterative interpolation.

[0048] In a third aspect, the present application proposes a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0049] In a fourth aspect, the present application proposes a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the steps of the above method when executed by a processor.

[0050] The present application proposes a harmonic parameter measurement method, system and device for electric energy metering in a distribution network. The method adopts a window function coefficient optimization method based on an immune algorithm to optimize the window function coefficients and obtain a window with excellent sidelobe characteristics. The window has the characteristics of a small sidelobe peak level and a large sidelobe asymptotic attenuation rate, and can effectively reduce spectrum leakage. Then, based on the optimized window function, dual-spectrum iterative interpolation parameter estimation is performed to quickly and accurately obtain various harmonic parameters, thereby solving the problem of harmonic measurement in DC signals in a DC distribution network. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] The drawings described herein are used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation on the embodiments of the present application. In the drawings:

[0052] Figure 1 A flow chart of the harmonic parameter measurement method proposed in the embodiment of the present application;

[0053] Figure 2 This is a principle block diagram of the harmonic parameter measurement system proposed in an embodiment of the present application. DETAILED DESCRIPTION

[0054] In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with examples and drawings. The illustrative implementation scheme of the present application and its description are only used to explain the present application and are not intended to limit the present application.

[0055] Example:

[0056] Harmonics (ripples) are important indicator parameters for measuring the power quality of a DC distribution network. In order to quickly and accurately measure harmonics in a system, this embodiment proposes a harmonic parameter measurement method for power metering in a distribution network.

[0057] like Figure 1 As shown, the harmonic parameter measurement method proposed in this embodiment specifically includes the following steps:

[0058] Step 100, sequentially obtain the voltage and current signal waveform data corresponding to different DC charging piles, and perform VMD decomposition on the waveform data under the above different DC charging piles to obtain the DC component and harmonic component corresponding to the DC power data, and obtain the harmonic component data in the DC signal.

[0059] Furthermore, the specific implementation process of step 100 is as follows:

[0060] Step 101, based on the fixed sampling technology, discrete sampling is performed on the voltage / current signal in the power grid to obtain the DC voltage / current signal under different DC charging piles; wherein the sampling frequency is f s , the voltage / current signals of different DC charging piles are expressed as x(n).

[0061] Step 102, using the variational mode decomposition method, the sampled voltage / current signal is decomposed into a number of intrinsic mode functions (IMF) with different frequency characteristics. k (t) is expressed as:

[0062] u k (t) = A k (t)cos(φ k (t))

[0063] Where k is the number of decomposed modes; A k (t) represents the envelope amplitude function, and A k (t)≥0;φ k (t) represents the instantaneous phase.

[0064] Step 103: sort out the above-decomposed modes and extract harmonic component data in the DC signal.

[0065] Step 200, taking the optimization of the sidelobe performance of the cosine window as the starting point, taking the sidelobe peak level and the sidelobe progressive attenuation rate as the optimization targets, and optimizing the combined cosine window parameters using the immune algorithm, a six-term fifth-order combined cosine window is obtained.

[0066] Furthermore, the specific implementation process of step 200 is as follows:

[0067] Step 201, firstly, it is necessary to determine the object of window function optimization and related constraints. In general, the main lobe performance of the window is determined by the number of window items, while the side lobe performance of the window is mainly determined by the window coefficient. In addition, the side lobe performance index is taken as the object of optimization, and the number of items that determine the main lobe performance is determined before optimization. Compare the combined cosine window function with the best side lobe performance under the current number of items, and determine the upper limit of the optimization of the side lobe performance under a certain number of items. At the same time, considering that an overly wide window function cannot be used during measurement and correction to ensure a more accurate acquisition of the main lobe frequency, the number of cosine window items is determined to be 6.

[0068] Step 202: Since the sidelobe performance of the window is mainly determined by the window coefficient, the sidelobe performance index is regarded as the antigen; the window coefficient of the optimized window is regarded as the feasible solution of the optimization target, that is, the antibody. Based on the relationship between the optimization target and the feasible solution, and considering that the sidelobe performance is mainly determined by the sidelobe attenuation rate and the peak level, an affinity function is established, which is expressed as:

[0069] Function min = -(θ·|G|+(1-θ)·|E|)

[0070] Among them, G is the maximum sidelobe peak level, E is the sidelobe attenuation rate, and θ is the weight.

[0071] Step 203, weights and various parameters are obtained: G can obtain the sidelobe peak level by comparing the sidelobe maximum near the main lobe; E can be obtained by fitting the first-order curve of the side lobe peak point. When θ = 0, only the sidelobe attenuation rate is considered as the sidelobe performance evaluation index; when θ = 1, only the maximum sidelobe peak level is considered as the sidelobe performance evaluation index; when θ = 0.5, the sidelobe attenuation rate and the maximum sidelobe peak level are considered as the sidelobe performance evaluation index.

[0072] Step 204, the feasible solution of the optimization objective should also satisfy the constraints:

[0073]

[0074] Among them, 0<a h <1, H represents the number of terms in the window function, a h Represents the coefficients of each term of the window function.

[0075] Step 205, determining the initial values ​​of various parameters of the immune algorithm.

[0076] Through multiple parameter experiments, the population size was set to 100, and the optimal immune parameters suitable for the cosine window function optimization model were obtained. The specific settings of each immune operator are as follows: Take the immune selection ratio N P 30%; antibody clone expansion multiple: 8 times; population refresh ratio: take N P 30%; Maximum evolutionary number G max :200.

[0077] Step 206, using the immune algorithm to optimize the coefficients of the six fifth-order combined cosine window functions, when the sidelobe peak level and the sidelobe asymptotic attenuation rate weights are both 0.5, the correlation coefficient of the window function with the best sidelobe performance is obtained through multiple optimization iterations, and the time domain expression of the combined cosine window function is obtained as follows:

[0078]

[0079] Where m = 0, 1, 2, ..., N-1, M represents the length of the window, w OSTC (m) represents the function value at the mth sample point, and N is the length of the signal.

[0080] Step 300, based on the optimized six-term fifth-order combined cosine window, a self-multiplication convolution optimization window is constructed by combining the advantages of the multiplication window and the convolution window.

[0081] Furthermore, the specific implementation process of step 300 is as follows:

[0082] Step 301, according to the general formula of the self-squared window, and taking into account the factor of the amount of calculation, the order of the multiplication window is selected as 2, and the optimal six-term cosine self-squared window expression shown in the following formula is obtained:

[0083]

[0084] Among them, a h Represents the coefficient of the h-th combination window.

[0085] Step 302, the 2nd order six-term optimal cosine self-squared window is subjected to discrete Fourier transform to obtain its spectrum function, as shown in the following formula:

[0086]

[0087] Where ω represents the angular frequency; W R (ω) represents the spectrum function of the window, which is expressed as:

[0088]

[0089] By performing a self-convolution operation on the self-squared window, we can obtain:

[0090] w SMMC (n) = w OSTCM (m)*w OSTCM (m)

[0091] Among them, w SMMC (n) is a sequence of length 2M-1. A sequence of length N=2M can be obtained by padding the end of the sequence with zeros.

[0092] According to the convolution characteristics of discrete Fourier transform, the discrete Fourier transform of two sequences after convolution operation in the time domain is equal to the product operation of the discrete Fourier transform of two discrete sequences in the frequency domain, which can be obtained:

[0093]

[0094] Let ω=2kπ / N, then the above formula can be converted to:

[0095]

[0096] Step 400, applying the proposed self-convolution optimization window to the harmonic analysis of FFT iterative interpolation, and completing the estimation and correction of harmonic parameters through double-spectrum iterative interpolation.

[0097] Furthermore, the specific implementation process of step 400 is as follows:

[0098] Step 401, perform FFT frequency sweep on the harmonic components in the DC signal obtained by discrete sampling to obtain the rough estimated initial frequency f0 of each harmonic, and set the initial frequency interval to The corresponding frequencies of the two spectral lines after interpolation are f0+Δf and f0-Δf. The phasor values ​​corresponding to f0, f0+Δf and f0-Δf are calculated using the following formulas, and their absolute values ​​are taken to obtain the corresponding amplitudes.

[0099]

[0100] Compare the amplitudes of the three respectively, and select the frequency corresponding to the maximum amplitude spectrum line as the initial value of the iteration. After one iteration, the frequency obtained by the iteration is closer to the true value of the frequency to be measured than the initial value of the iteration, but the frequency measurement accuracy is still low at this time; so it is used as the initial frequency for the next iteration. Continue to perform double spectrum interpolation on the maximum amplitude spectrum line, and the frequency interval becomes Repeat until the number of iterations y reaches the set value 6, and the final output frequency is f re and frequency spacing Δf.

[0101] Step 402, further accurately estimate the harmonic frequency, that is, correct the harmonic frequency obtained by double spectrum interpolation. Suppose the real frequency point corresponding to the peak of the signal during asynchronous sampling is k, and the spectrum line corresponding to the rough estimated frequency obtained after FFT frequency sweep is k0, iteratively determine the two spectrum lines k1 and k2 with the second largest and largest amplitudes, and k2=k1+1.

[0102] Correspondingly, the amplitudes of the spectral lines k1 and k2 can be obtained by discrete Fourier transform of the signal x(n), namely A=|X(k1)| and B=|X(k2)|. a ≤1, then introduce the variable γ, the value range is -0.5<γ<0.5, so that there is a relationship γ=kk a -0.5, where k is the index of the real spectral line; k a is the index number corresponding to the center frequency, and according to the appropriate weight allocation, the equation for the parameter ζ can be obtained:

[0103]

[0104] By converting the above formula, we can get:

[0105]

[0106] Where W[E] represents the spectral components of E with the window function W applied to them. N is the length of the signal. The parameters γ and ζ can be obtained by polynomial fitting. Specifically, a set of γ values ​​in the interval [-0.5, 0.5] is taken and substituted into the above formula to obtain a corresponding set of ζ values. The function polyfit(ζ,γ,c) is called for fitting, where c is the order of the fitting approximation polynomial.

[0107] The correction formula for the frequency of the i-th harmonic is obtained as follows:

[0108] f re_i =k1·Δf+Δε

[0109] Among them, k1 is the spectrum line on the left side of the center frequency, Δf rerepresents the interval between two spectral lines after the double-line interpolation iteration, Δε represents the estimated error of the offset, and Δε=(γ+0.5)·Δf re .

[0110] The phase correction formula of the i-th harmonic is:

[0111]

[0112] The amplitude correction formula of the i-th harmonic is:

[0113]

[0114] Where h(γ) is the same as above to solve γ=f -1 The polynomial coefficients can be obtained by the Matlab function polyfit(γ,h(γ),c), where angle[A] represents the phase of A.

[0115] Finally, the corresponding parameters can be obtained.

[0116] The harmonic parameter measurement method proposed in this embodiment firstly adopts a window function coefficient optimization method based on an immune algorithm to optimize the window function coefficients, thereby obtaining a window with excellent sidelobe characteristics. The window has the characteristics of a small sidelobe peak level and a large sidelobe asymptotic attenuation rate, and can effectively reduce spectrum leakage. Then, based on the optimized window function, dual-spectrum iterative interpolation parameter estimation is performed to quickly and accurately obtain various harmonic parameters, thereby solving the problem of harmonic measurement in DC signals under a DC distribution network.

[0117] Based on the same technical concept as above, this embodiment also proposes a harmonic parameter measurement system for electric energy metering in a distribution network, such as Figure 2 As shown, the harmonic parameter measurement system proposed in this embodiment includes:

[0118] A sampling and analysis unit, the sampling and analysis unit is used to obtain a DC signal by discrete sampling and obtain harmonic component data in the DC signal;

[0119] A window parameter optimization unit, which takes the sidelobe peak level and the sidelobe asymptotic attenuation rate of the cosine window as optimization targets, and uses an immune algorithm to optimize the combined cosine window parameters to obtain a six-term fifth-order combined cosine window;

[0120] A self-multiplied convolution unit, which is based on the optimized six-term fifth-order combined cosine window and combines the advantages of the multiplication window and the convolution window to construct a self-multiplied convolution optimization window;

[0121] And, a harmonic analysis unit, which applies a self-convolution optimization window to harmonic analysis of FFT iterative interpolation, and completes harmonic parameter estimation and correction through double-spectrum iterative interpolation.

[0122] It should be noted that the specific implementation process of each functional module unit of the system is as described in the above steps 100 to 400, and will not be repeated here.

[0123] 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.

[0124] 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.

[0125] 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 A function specified in one or more boxes.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0127] The specific implementation methods described above further illustrate the purpose, technical solutions and beneficial effects of the present application in detail. It should be understood that the above description is only the specific implementation method of the present application and is not intended to limit the scope of protection of the present application. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application should be included in the scope of protection of the present application.

Claims

1. A method for measuring harmonic parameters for electric energy metering in a distribution network, characterized in that: The harmonic parameter measurement method comprises: Obtaining a DC signal by discrete sampling, and acquiring harmonic component data in the DC signal; Taking the sidelobe peak level and the asymptotic attenuation rate of the cosine window as the optimization targets, the immune algorithm is used to optimize the combined cosine window parameters and a six-term fifth-order combined cosine window is obtained. Based on the optimized six fifth-order combined cosine windows, a self-multiplication convolution optimization window is constructed by combining the advantages of the multiplication window and the convolution window; The self-convolution optimization window is applied to the harmonic analysis of discrete Fourier transform iterative interpolation, and the harmonic parameter estimation and correction are completed through double-spectrum iterative interpolation.

2. The method for measuring harmonic parameters of electric energy metering in a distribution network according to claim 1 is characterized in that: The discrete sampling obtains a DC signal, and obtains harmonic component data in the DC signal, specifically including: Discretely sample the voltage or current signal in the power grid to obtain the DC signal under different DC charging piles; The sampled DC signal is subjected to variational mode decomposition to obtain a DC component and a harmonic component corresponding to the DC signal, thereby obtaining harmonic component data in the DC signal.

3. The method for measuring harmonic parameters of electric energy metering in a distribution network according to claim 1 is characterized in that: The sidelobe peak level and the sidelobe asymptotic attenuation rate of the cosine window are taken as optimization targets, and the immune algorithm is used to optimize the combined cosine window parameters to obtain six fifth-order combined cosine windows, specifically including: The side lobe performance index of the window is taken as the optimization object, and the number of cosine window items is determined to be 6; Taking the sidelobe asymptotic attenuation rate and the sidelobe peak level as optimization targets, and the window coefficient as a feasible solution to the optimization target, and establishing an affinity function based on the relationship between the optimization target and the feasible solution; Obtaining various parameters and their weights in the affinity function; Determining constraints of feasible solutions to the optimization objective; Determine the initial values ​​of various parameters of the immune algorithm; The immune algorithm is used to optimize the coefficients of the six-term fifth-order combined cosine window function to obtain the six-term fifth-order combined cosine window function with the best sidelobe performance.

4. The method for measuring harmonic parameters of electric energy metering in a distribution network according to claim 3 is characterized in that: The affinity function is expressed as: function min =-(θ·|G|+(1-θ)·|E|) Among them, G is the maximum sidelobe peak level, E is the sidelobe attenuation rate, and θ is the weight; The feasible solution of the optimization objective, i.e. the constraint condition of the window coefficient, is: Among them, 0<a h <1, H represents the number of terms in the window function, a h Represents the coefficients of each term of the window function; The time domain expression of the optimized six-term fifth-order combined cosine window function is: Where m = 0, 1, 2, ..., N-1, M represents the length of the window, w OSTC (m) represents the function value at the mth sample point, and N is the length of the signal.

5. The method for measuring harmonic parameters for electric energy metering in a distribution network according to claim 1, characterized in that: The six fifth-order combined cosine windows obtained based on the optimization combine the advantages of the multiplication window and the convolution window to construct the self-multiplication convolution optimization window, which specifically includes: Set the multiplication window order to 2 to obtain a 2nd-order, 6-term, 5th-order combined cosine self-squared window; The spectrum function of the second-order six-term fifth-order combined cosine self-squared window is obtained by discrete Fourier transformation; The spectrum function of the second-order six-term fifth-order combined cosine squared window is subjected to a self-product operation, that is, a self-convolution operation of the second-order six-term fifth-order combined cosine squared window is realized to obtain a self-convolution optimization window.

6. A method for measuring harmonic parameters for electric energy metering in a distribution network according to any one of claims 1 to 5, characterized in that: The method of applying the self-convolution optimization window to the harmonic analysis of discrete Fourier transform iterative interpolation and completing harmonic parameter estimation and correction through double-spectrum iterative interpolation specifically includes: Performing FFT frequency sweep on the harmonic components in the DC signal to obtain the rough estimated initial frequency of each harmonic, and setting the initial frequency interval to obtain the corresponding frequencies of the two spectral lines after interpolation, and respectively calculating the phasor values ​​corresponding to the initial frequency and the frequencies of the two spectral lines after interpolation, and taking the absolute values ​​thereof to obtain the corresponding amplitudes; respectively comparing the amplitudes of the three, selecting the frequency corresponding to the maximum spectral line as the initial value of iteration, and performing iterative optimization. During the iterative optimization process, the frequency obtained in the previous iteration is used as the initial frequency for the next iteration, and the frequency interval of the next iteration is half of the frequency interval of the previous iteration, until the number of iterations meets the preset value, and the final harmonic frequency and frequency interval are obtained; The final obtained harmonic frequency, harmonic phase and harmonic amplitude are corrected to obtain harmonic parameters.

7. The method for measuring harmonic parameters for electric energy metering in a distribution network according to claim 6, characterized in that: The harmonic frequency correction formula is: f re_i =k1·Δf re +No Among them, k1 is the spectrum line on the left side of the center frequency, Δf is the frequency interval, Δf re represents the interval between two spectral lines after the double-line interpolation iteration, Δε represents the estimated error of the offset, and Δε=(γ+0.5)·Δf re ; The harmonic phase correction formula is: The harmonic amplitude correction formula is:

8. A harmonic parameter measurement system for electric energy metering in a distribution network, characterized in that: The harmonic parameter measurement system comprises: A sampling and analysis unit, the sampling and analysis unit is used to obtain a DC signal by discrete sampling and obtain harmonic component data in the DC signal; A window parameter optimization unit, wherein the window parameter optimization unit takes the sidelobe peak level and the sidelobe asymptotic attenuation rate of the cosine window as optimization targets, and uses an immune algorithm to optimize the combined cosine window parameters to obtain a six-term fifth-order combined cosine window; A self-multiplication convolution unit, wherein the self-multiplication convolution unit constructs a self-multiplication convolution optimization window based on the optimized six-term fifth-order combined cosine window and combining the advantages of the multiplication window and the convolution window; And, a harmonic analysis unit, which applies the self-convolution optimization window to the harmonic analysis of discrete Fourier transform iterative interpolation, and completes the estimation and correction of harmonic parameters through double-spectrum iterative interpolation.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 7 are implemented.

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

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