Electric energy metering error evaluation method, system, equipment and medium
By combining high-resolution spectrum estimation and structure-preserving algorithms with the OU-Cox process model, multi-mode decomposition and error assessment of power grid harmonic signals are achieved, solving the problem of inaccurate metering error assessment in existing technologies and improving the accuracy and fairness of power metering.
Patent Information
- Application Number
- CN202511841408.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-09
- Publication Date
- 2026-01-23
AI Technical Summary
Existing harmonic power metering methods based on frequency domain analysis lack high-precision mode separation capabilities when dealing with complex power grid signals containing superimposed harmonics, interharmonics, and noise. This results in the inability to accurately extract and measure each harmonic component, causing confusion between fundamental and harmonic power measurement, inconsistent metering standards across regions, and inaccurate metering error assessment.
High-resolution spectrum estimation and multi-node synchronous measurement techniques are used to extract harmonic parameters. Multimodal decomposition is performed by combining structure preservation algorithm. The harmonic propagation attenuation law is quantified by spatiotemporal correlation analysis and probabilistic statistical methods. An error response function is constructed to quantitatively evaluate the power metering error. The OU-Cox process model is introduced to accurately quantify the random intensity of harmonics.
It achieves accurate harmonic mode separation and metering error assessment in complex harmonic environments, solving the problems of model inaccuracy and insufficient assessment accuracy in traditional methods, and ensuring the fairness and accuracy of cross-regional power metering.
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Figure CN121385774A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present application relates to the technical field of power system harmonic measurement, and in particular to an electric energy metering error evaluation method, system, device and medium. BACKGROUND
[0002] With the development and utilization of large-scale renewable energy and the rapid development of smart grid, a large number of new power electronic equipment are introduced into the power system, and the power grid presents obvious characteristics of high penetration rate and high power electronicization, accompanied by a significant increase in harmonic content of the power grid, which makes the problem of harmonic influence on electric energy metering increasingly serious.
[0003] The power grid harmonic can cause power metering deviation, resulting in insufficient sampling accuracy of electric energy metering equipment, error in data processing and communication interference, and seriously affecting the electric energy metering result, so it is of great significance to evaluate the electric energy metering error. However, there are technical differences in harmonic metering reference among different regional power grids, resulting in a lack of unified comparison reference for metering results between cross-regional power grids. This reference difference makes the metering profit and loss accounting of power grid enterprises in the harmonic environment lack of fairness protection. Therefore, it is urgent to develop a modal separation algorithm more suitable for power grid frequency domain analysis to assist in more accurate metering error evaluation. SUMMARY
[0004] In view of the above problems, the present application is proposed.
[0005] Therefore, the technical problem solved by the present application is that the existing harmonic electric energy metering method based on frequency domain analysis cannot accurately extract and measure the independent energy of each harmonic component when facing complex harmonic, interharmonic and noise superimposed power grid signals due to the lack of high-precision modal separation capability, resulting in confusion of fundamental and harmonic power metering, and finally causing the problem of non-uniformity of cross-regional metering reference and inaccurate metering error evaluation.
[0006] To solve the above technical problems, the present application provides the following technical solutions: an electric energy metering error evaluation method, which comprises, collecting full-wave data of power transmission and distribution lines in the power grid, and performing harmonic distribution analysis on the full-wave data to obtain time-space characteristic parameters; according to the time-space characteristic parameters, using a structure preserving algorithm to perform multi-modal decomposition on the electric signal of the harmonic pollution area of the power grid; estimating the random intensity of the Cox process of each mode, combining the metering error parameters of the electric energy metering equipment, and constructing an error response function; and using the error response function to quantitatively evaluate the error of the measurement result of the electric energy metering device.
[0007] As a preferred scheme of the electric energy metering error evaluation method, the harmonic distribution analysis on the full-wave data comprises, High-resolution spectrum estimation and multi-node synchronous measurement technology are used to extract harmonic parameters.
[0008] Based on the space-time correlation analysis and probability statistics method, the propagation attenuation law and time-varying distribution characteristics of harmonics in the power grid are quantified.
[0009] As a preferred scheme of the power metering error evaluation method, the multi-modal decomposition of the electric signal in the harmonic pollution area of the power grid by using the structure preserving algorithm comprises, The sampling frequency and the number of sampling points are set, the time series is constructed, and the original electric signal is read.
[0010] The original signal is subjected to a discrete Fourier transform algorithm, and the frequency axis and the one-sided amplitude spectrum are calculated.
[0011] The decomposition function is called, and the window length is dynamically determined according to the time-varying characteristics of the signal.
[0012] The discrimination function is called, and the effectiveness index of each decomposition component is calculated.
[0013] Recursive decomposition is performed, and each component is operated once.
[0014] Component screening is performed, and each component is operated twice.
[0015] The components are merged, and the retained components are operated three times.
[0016] The merged components are operated four times.
[0017] As a preferred scheme of the power metering error evaluation method, the window length is dynamically determined according to the dominant frequency of the signal. The power spectral density peak position is calculated, and it is judged whether the dominant frequency meets the corresponding condition.
[0018] A set of time-shifted sequences is generated, and the initial decomposition components are obtained through eigenvalue decomposition and diagonal line average reconstruction.
[0019] As a preferred scheme of the power metering error evaluation method, the discrimination function comprises, FFT transform is calculated , normalized amplitude spectrum , one-sided spectrum .
[0020] The spectrum peak value is detected, and the number of peak values whose amplitude exceeds the threshold value op is counted wherein, represents the decomposition variable, N represents the number of sampling points, and E is a peak detection function, Let M be the step function, M be the total number of peaks identified by the peak detection algorithm in the single-sided spectrum of the k-th decomposed component, and j be the index of the spectral peak.
[0021] The beneficial effect of this preferred technical solution is that by constructing an adaptive discrimination mechanism based on spectral peak statistics, the objectivity and accuracy of component screening in the multimodal decomposition process are improved, and the subjective bias and overfitting risk caused by manually setting thresholds are effectively avoided.
[0022] In a preferred embodiment of the power metering error assessment method described in this invention, the random intensity of the Cox process is used to statistically analyze the recurrence of each harmonic module in the power grid harmonic pollution area, and the relevant formula is as follows: in, To accumulate strength, Intensity of the Cox process Obtained by following the Ornstein-Uhlenbeck process, i.e. Obtained via the OU-Cox process, n is represented as The specific observed value, i.e., the number of times a certain target event occurs. Represented as time interval The total number of target events that affect metering error in the internal power grid harmonic module.
[0023] The beneficial effect of this preferred technical solution is that by introducing the OU-Cox process to model the random intensity of harmonics, the spatiotemporal uncertainty of power grid harmonic pollution can be accurately quantified, effectively solving the problem that traditional methods are difficult to characterize the random recurrence characteristics of harmonics.
[0024] As a preferred embodiment of the power metering error assessment method of the present invention, the construction of the error response function includes, Based on the number of recurrences of each mode in each time interval, corresponding to the error data of the energy metering equipment, and combining the Cox process random intensity of each mode with the metering error data of the energy metering equipment, an error response function is constructed as follows: in, The intensity of the Cox process under a certain mode, To correspond to the response coefficients under this mode, This refers to the measurement error of the electricity metering equipment.
[0025] The beneficial effect of this preferred technical solution is that by integrating the random intensity of each harmonic mode with the measured error data to construct an error response function, a quantitative mapping relationship between the dynamic characteristics of harmonics and the measurement error is established, which effectively solves the problem of model inaccuracy caused by ignoring the time-varying randomness of harmonics in traditional evaluation methods.
[0026] This invention provides an electrical energy metering error assessment system.
[0027] To solve the above-mentioned technical problems, the present invention provides the following technical solution: a system for evaluating electricity metering errors, comprising: The module includes data acquisition and preprocessing, multimodal decomposition and filtering, error function construction, and error evaluation and output. The data acquisition and preprocessing module is used to acquire full-wave data of transmission and distribution lines in the power grid, and to perform harmonic distribution analysis on the full-wave data to obtain spatiotemporal characteristic parameters. The multimodal decomposition and screening module is used to perform multimodal decomposition of electrical signals in the power grid harmonic pollution area based on the spatiotemporal characteristic parameters and using a structure-preserving algorithm. The error function construction module is used to estimate the random intensity of the Cox process in each mode, and construct the error response function by combining the metering error parameters of the power metering equipment. The error assessment and output module is used to quantitatively assess the error of the measurement results of the power metering device using the error response function.
[0028] The present invention provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the method for evaluating electrical energy metering errors.
[0029] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the steps of the power metering error assessment method.
[0030] The beneficial effects of the present invention are as follows: First, considering that actual data is coupled with multiple components and that empirical mode decomposition algorithms have defects in accuracy and stability during the complex data decomposition process, the present invention uses a symplectic geometric mode decomposition algorithm based on an iterative optimization module to decompose the initial data, thereby improving the accuracy and efficiency of mode decomposition and improving the accuracy of power metering error estimation.
[0031] Secondly, this invention introduces the stochastic intensity of the Cox process and combines it with the metering error of the power metering equipment to construct a power metering error response function. This is beneficial for estimating the metering error of the power metering equipment during operation, improving the metering accuracy of the power metering equipment, and providing necessary reference for the research and development design of power metering equipment. Attached Figure Description
[0032] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0033] Figure 1 The above is a flowchart of an electrical energy metering error assessment method provided in one embodiment of the present invention.
[0034] Figure 2 This is an overall framework diagram of an energy metering error assessment system provided in one embodiment of the present invention. Detailed Implementation
[0035] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the protection scope of the present invention.
[0036] Example 1, referring to Figure 1 This is one embodiment of the present invention, which provides a method for evaluating electricity metering errors, including: S1. Collect full-wave data of power transmission and distribution lines in the power grid, and perform harmonic distribution analysis on the full-wave data to obtain spatiotemporal characteristic parameters; S2. Based on the spatiotemporal characteristic parameters, the structure-preserving algorithm is used to perform multimodal decomposition of the electrical signal in the power grid harmonic pollution area; S3. Estimate the stochastic intensity of the Cox process for each mode, and construct the error response function by combining the metering error parameters of the power metering equipment; S4. Quantitatively evaluate the error of the measurement results of the power metering device using the error response function.
[0037] By integrating wide-area synchronous measurement with high-resolution spectrum analysis, the spatiotemporal characteristics of harmonics are accurately extracted; the structure-preserving algorithm is used to achieve accurate separation of harmonic modes, and the OU-Cox process is introduced to quantify the random intensity of each mode; finally, the constructed error response function establishes a quantitative mapping relationship between harmonic dynamic characteristics and metering errors, effectively solving the problems of model inaccuracy and insufficient evaluation accuracy of traditional methods in complex harmonic environments, and realizing accurate source tracing and fair evaluation of cross-regional power metering errors.
[0038] Example 2, an embodiment of the present invention, provides a method for evaluating electricity metering errors based on the previous embodiment, including: In this embodiment of the application, the harmonic distribution analysis in S1, namely the analysis of the frequency domain characteristics and spatial distribution law of power grid harmonics, is carried out by high-precision synchronous measurement units (PMU / merging units) deployed at key nodes of the power grid (such as substations, feeder heads and ends) to synchronously collect the full waveform data of three-phase voltage and current of transmission and distribution lines at a sampling rate of not less than 10kHz, so as to ensure the temporal and spatial synchronization of the data. Secondly, a high-resolution spectrum analysis method based on autoregressive (AR) spectrum estimation or short-time Fourier transform (STFT) is adopted to accurately extract frequency domain characteristic parameters such as amplitude, phase, frequency and harmonic distortion rate (THD) of harmonics and interharmonics at each monitoring point, and typical harmonic modes are identified by clustering algorithms (such as K-means). Furthermore, based on multi-node synchronous harmonic data, the spatial correlation coefficient of harmonic amplitude / phase is calculated using a spatiotemporal correlation analysis algorithm. Combined with the power grid topology impedance parameters, a spatial distribution heat map of harmonic pollution is constructed using a backpropagation tracing algorithm or a Kriging spatial interpolation method. This quantifies the attenuation law and phase shift characteristics of harmonics along the line, and finally generates spatiotemporal characteristic parameters that cover frequency domain characteristics and spatial distribution laws.
[0039] In one alternative implementation, harmonic distribution analysis can be performed by combining Fast Fourier Transform (FFT) with spectral kurtosis detection to extract harmonic amplitude, phase, and distortion rate parameters within a characteristic frequency band and generate a harmonic spectrum distribution map.
[0040] In another alternative implementation, harmonic distribution analysis can also be based on multi-node harmonic data from a wide-area synchronous measurement system (WAMS), using spatiotemporal correlation analysis and Kriging interpolation algorithms to construct a spatial heat map and propagation path model of harmonic pollution, and quantify the attenuation gradient and phase shift characteristics of harmonics along the transmission line.
[0041] Furthermore, the harmonic distribution analysis of the full-wave data in S1 includes the following steps A1-A2: A1. Harmonic parameters are extracted using high-resolution spectrum estimation and multi-node synchronous measurement techniques. A2. Based on spatiotemporal correlation analysis and probabilistic statistical methods, the propagation attenuation law and time-varying distribution characteristics of harmonics in the power grid are quantified.
[0042] In this embodiment, the structure-preserving algorithm in S2, namely the symplectic geometry algorithm, employs an iterative symplectic geometry-based mode decomposition algorithm. By introducing an iterative optimization mechanism based on the symplectic geometric characteristics, the algorithm parameters and calculation results are adjusted through multiple iterations to optimize and improve the decomposition results of each mode. Specifically, the algorithm preserves the symplectic structure of the Hamiltonian system by constructing a symplectic orthogonal transformation matrix. In each iteration, the conjugate gradient method is used to optimize the regularization parameters in the symplectic eigenvalue decomposition. An adaptive step-size adjustment strategy balances the convergence speed and decomposition accuracy, thereby achieving high-precision separation and feature extraction of coupled modes in power grid harmonic signals.
[0043] In one alternative implementation, the structure-preserving algorithm can preserve the symmetric structure characteristics of the Hamiltonian system by constructing a symmetric orthogonal matrix and symmetric eigenvalue decomposition, decomposing the original signal into a series of symmetric mode components, each corresponding to a specific frequency feature, and simultaneously ensuring the convergence and stability of the decomposition by adjusting the regularization parameter in the iteration process through adaptive threshold adjustment.
[0044] In another alternative implementation, the structure preservation algorithm can also be a hybrid method based on variational mode decomposition (VMD) and symplectic geometry theory. By introducing symplectic geometric constraints into the variational framework, the center frequency and bandwidth of the mode are simultaneously optimized in the frequency domain, and the penalty factor and Lagrange multiplier are dynamically adjusted using an iterative update mechanism, thereby achieving high-precision separation and extraction of harmonic modes under strong noise background.
[0045] Furthermore, the multimode decomposition of electrical signals in the harmonic pollution region of the power grid using the structure-preserving algorithm in S2 includes the following steps B1-B8: B1. Set the sampling frequency and number of sampling points, construct the time series, and read the original signal; Furthermore, set the sampling frequency and the number of sampling points. , Constructing time series and read the original signal , where n represents the discrete-time index, i.e. the nth sampling point.
[0046] B2. Perform a Discrete Fourier Transform algorithm on the original signal to calculate the frequency axis and the single-sided amplitude spectrum; Furthermore, a Fast Fourier Transform is performed on the original signal to calculate the frequency axis. and single-sided amplitude spectrum , where k represents the frequency index, that is, the kth discrete frequency point in the single-sided spectrum.
[0047] B3. Call the decomposition function to dynamically determine the window length based on the time-varying characteristics of the signal; Furthermore, the window length is d.
[0048] B4. Call the discriminant function to calculate the effectiveness index for each decomposition component; Furthermore, the components are decomposed into effectiveness indicators B5. Perform recursive decomposition, processing each component once. Furthermore, a recursive decomposition is performed, for each component... ,like Then for Repeat step A3 to obtain a new component. ,Will Add to the original component set until the validity index of all components is less than the threshold. Let i be the i-th signal component that needs to be determined whether it needs further decomposition.
[0049] B6. Perform component filtering and perform secondary operations on each component; Further, component screening is performed on the components. ,like and Calculate its FFT spectrum and detect peaks, retaining the largest peak. The components, record the peak position , The component represented as the j-th signal The maximum amplitude in the time domain B7. Merge components. Perform three operations on the retained components. When merging components with similar frequencies, for the retained components, if the peak positions differ... Then the corresponding time-domain sequences will be superimposed. And mark the merged components; B8. Perform four operations on the merged components.
[0050] For the merged components, calculate the FFT spectrum and detect peak values. If the maximum peak value is... If so, retain that component, and you will eventually obtain the set of valid components. .
[0051] In the embodiments of this application, the Discrete Fourier Transform algorithm in B2 is the Fast Fourier Transform, specifically the Fast Fourier Transform based on the Radix-2 Decimation-in-Time (DIT-FFT). After windowing the power grid signal using the Blackman-Harris window function and zero-filling to integer powers of 2 for preprocessing, a bit reversal and multi-level butterfly iterative operation structure is adopted. On the FPGA hardware platform, a parallel pipeline architecture is used to complete one radix-2 butterfly operation per clock cycle, optimizing the computational complexity from O(N²) to O(N log N). Furthermore, phase gradient frequency offset correction and amplitude compensation algorithms are used to improve the spectral accuracy, providing high-resolution frequency domain features for subsequent mode decomposition.
[0052] In one alternative implementation, the Discrete Fourier Transform algorithm can be the classic Cooley-Tukey algorithm using radix-2 decimation-in-time (DIT-FFT) or radix-2 decimation-in-frequency (DIF-FFT), which reduces the computational complexity to O(N log N) through recursive decomposition, enabling rapid analysis of the power grid signal spectrum.
[0053] In another alternative implementation, the Discrete Fourier Transform algorithm can also be based on the Prime Factorization (PFA) algorithm or the Winograd Fourier Transform (WFTA) algorithm, which further optimizes the computational efficiency by reducing the number of multiplication operations. This is especially suitable for embedded power metering scenarios where the number of sampling points has specific factorization or where real-time requirements are extremely high.
[0054] In this embodiment, the time-varying characteristic of the signal in B3, namely the dominant frequency, is dynamically characterized by tracking the peak frequency of the concentrated energy region of the spectrum in real time to represent the non-stationary characteristics of the signal. The specific implementation process is as follows: First, the instantaneous frequency of the signal is initially estimated by combining the autocorrelation function and the YIN algorithm to eliminate the frequency doubling error caused by harmonic interference; then, a frequency tracking loop is constructed by using a Kalman filter, with the estimated dominant frequency of the previous moment as the state variable, and the state is updated and predicted based on the spectrum peak detection results to achieve smooth tracking of the frequency changes of the non-stationary signal. In one alternative implementation, the time-varying characteristics of the signal in B3 can be the instantaneous bandwidth of the signal, the envelope fluctuation characteristics extracted by Hilbert transform, and the degree of diffusion of spectral energy quantified.
[0055] In another alternative implementation, the time-varying characteristics of the signal in B3 can also be time-frequency ridge trajectories. Based on the time-frequency matrix of short-time Fourier transform or wavelet transform, the continuity characteristics of the frequency components evolving over time are extracted through path optimization algorithms, thereby providing a more comprehensive description of the dynamic behavior of harmonic modes.
[0056] Furthermore, dynamically determining the window length based on the time-varying characteristics of the signal in B3 includes the following steps B31-B32: B31. Calculate the peak position of the power spectral density and determine whether the dominant frequency meets the corresponding conditions. Furthermore, the peak position of the power spectral density is calculated. If the dominant frequency satisfy ,but ,otherwise , denoted as sampling frequency, and d represents the decomposition window length.
[0057] B32. Generate a set of time-shifted sequences, and obtain the initial decomposed components through eigenvalue decomposition and diagonal average reconstruction.
[0058] Furthermore, a set of time-shifted sequences is generated. The initial decomposition components are obtained through eigenvalue decomposition and diagonal average reconstruction. .
[0059] Furthermore, the call discrimination function in B4 includes the following steps B41-B42: B41. Calculate the FFT transform. Normalized amplitude spectrum , single-sided spectrum ; B42. Detect spectral peaks and count the number of peaks whose amplitude exceeds the threshold op. in, Let N represent the number of sampling points, and k represent the decomposition variables. Let M be denoted as , and j be denoted as .
[0060] Furthermore, the stochastic intensity of the Cox process in S3 is used to statistically analyze the recurrence of each harmonic module in the power grid harmonic pollution area. The relevant formula is as follows: in, To accumulate strength, Intensity of the Cox process Obtained by following the Ornstein-Uhlenbeck process (OU process), i.e. Obtained via the OU-Cox process, n is represented as The specific observed value, i.e., the number of times a certain target event occurs. Represented as time interval The total number of target events that affect metering error in the internal power grid harmonic module.
[0061] Furthermore, the construction of the error response function in S3 includes, Based on the number of recurrences of each mode in each time interval, corresponding to the error data of the energy metering equipment, and combining the Cox process random intensity of each mode with the metering error data of the energy metering equipment, an error response function is constructed as follows: in, The intensity of the Cox process under a certain mode, To correspond to the response coefficients under this mode, This refers to the measurement error of the electricity metering equipment.
[0062] Example 3, referring to Figure 2 This is one embodiment of the present invention, which provides a system for evaluating power metering errors, including: a data acquisition and preprocessing module, a multimodal decomposition and screening module, an error function construction module, and an error evaluation and output module; The data acquisition and preprocessing module is used to acquire full-wave data of transmission and distribution lines in the power grid, and to perform harmonic distribution analysis on the full-wave data to obtain spatiotemporal characteristic parameters. The multimodal decomposition and screening module is used to perform multimodal decomposition of electrical signals in the power grid harmonic pollution area based on the spatiotemporal characteristic parameters and using a structure-preserving algorithm. The error function construction module is used to estimate the random intensity of the Cox process in each mode, and construct the error response function by combining the metering error parameters of the power metering equipment. The error assessment and output module is used to quantitatively assess the error of the measurement results of the power metering device using the error response function.
[0063] This embodiment also provides an electronic device applicable to an energy metering error assessment method, comprising: a memory and a processor; the memory is used to store computer-executable instructions, and the processor is used to execute the computer-executable instructions to implement an energy metering error assessment method as proposed in the above embodiment.
[0064] This embodiment also provides a storage medium storing a computer program that, when executed by a processor, implements an energy metering error assessment method as proposed in the above embodiments.
[0065] The storage medium proposed in this embodiment belongs to the same inventive concept as the method for evaluating electrical energy metering errors proposed in the above embodiments. Technical details not described in detail in this embodiment can be found in the above embodiments, and this embodiment has the same beneficial effects as the above embodiments.
[0066] Based on the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented using software and necessary general-purpose hardware, and of course, it can also be implemented using hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of the various embodiments of the present invention.
[0067] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.
Claims
1. A method for evaluating electrical energy metering errors, characterized in that: include, Full-wave data of power transmission and distribution lines in the power grid are collected, and harmonic distribution analysis is performed on the full-wave data to obtain spatiotemporal characteristic parameters; Based on the aforementioned spatiotemporal characteristic parameters, a structure-preserving algorithm is used to perform multimodal decomposition of the electrical signals in the power grid harmonic pollution region; Estimate the stochastic intensity of the Cox process for each mode, and construct the error response function by combining the metering error parameters of the power metering equipment; The error response function is used to quantitatively evaluate the error of the measurement results of the electricity metering device.
2. The method for evaluating electrical energy metering errors as described in claim 1, characterized in that: The harmonic distribution analysis of the full-wave data includes, High-resolution spectrum estimation and multi-node synchronous measurement techniques are used to extract harmonic parameters; Based on spatiotemporal correlation analysis and probabilistic statistical methods, the propagation attenuation law and time-varying distribution characteristics of harmonics in the power grid are quantified.
3. The method for evaluating electrical energy metering errors as described in claim 2, characterized in that: The method of using a structure-preserving algorithm to perform multimodal decomposition of electrical signals in harmonic pollution areas of the power grid includes... Set the sampling frequency and number of sampling points, construct the time series, and read the original electrical signal; Perform a Discrete Fourier Transform algorithm on the original signal to calculate the frequency axis and the single-sided amplitude spectrum; Call the decomposition function to dynamically determine the window length based on the time-varying characteristics of the signal; Call the discriminant function to calculate the effectiveness index for each decomposition component; Perform recursive decomposition, performing the operation once for each component; Perform component filtering, and perform secondary operations on each component; Merge the components, and perform three operations on the retained components; The merged components are processed in four steps.
4. The method for evaluating electrical energy metering errors as described in claim 3, characterized in that: The dynamic determination of the window length based on the dominant signal frequency includes, Calculate the peak position of the power spectral density and determine whether the dominant frequency meets the corresponding conditions; A set of time-shifted sequences is generated, and the initial decomposed components are obtained through eigenvalue decomposition and diagonal average reconstruction.
5. The method for evaluating electrical energy metering errors as described in claim 4, characterized in that: The call discrimination function includes, Calculate FFT transform Normalized amplitude spectrum , single-sided spectrum ; Detect spectral peaks and count the number of peaks whose amplitude exceeds the threshold op. in, Let N represent the decomposed variables, N represent the number of sampling points, and E represent the peak detection function. Let M be the step function, M be the total number of peaks identified by the peak detection algorithm in the single-sided spectrum of the k-th decomposed component, and j be the index of the spectral peak.
6. The method for evaluating electrical energy metering errors as described in claim 5, characterized in that: The random intensity of the Cox process is used to statistically analyze the recurrence of each harmonic module in the power grid harmonic pollution area. The relevant formula is as follows: in, To accumulate strength, Intensity of the Cox process Obtained by following the Ornstein-Uhlenbeck process, i.e. Obtained via the OU-Cox process, n is represented as The specific observed value, i.e., the number of times a certain target event occurs. Represented as time interval The total number of target events that affect metering error in the internal power grid harmonic module.
7. The method for evaluating electrical energy metering errors as described in claim 6, characterized in that: The constructed error response function includes, Based on the number of recurrences of each mode in each time interval, corresponding to the error data of the energy metering equipment, and combining the Cox process random intensity of each mode with the metering error data of the energy metering equipment, an error response function is constructed as follows: in, The intensity of the Cox process under a certain mode, To correspond to the response coefficients under this mode, This refers to the measurement error of the electricity metering equipment.
8. A system for evaluating electricity metering errors, employing the electricity metering error evaluation method as described in any one of claims 1 to 7, characterized in that, include: The module includes data acquisition and preprocessing, multimodal decomposition and filtering, error function construction, and error evaluation and output. The data acquisition and preprocessing module is used to acquire full-wave data of transmission and distribution lines in the power grid, and to perform harmonic distribution analysis on the full-wave data to obtain spatiotemporal characteristic parameters. The multimodal decomposition and screening module is used to perform multimodal decomposition of electrical signals in the power grid harmonic pollution area based on the spatiotemporal characteristic parameters and using a structure-preserving algorithm. The error function construction module is used to estimate the random intensity of the Cox process in each mode, and construct the error response function by combining the metering error parameters of the power metering equipment. The error assessment and output module is used to quantitatively assess the error of the measurement results of the power metering device using the error response function.
9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the power metering error assessment method according to any one of claims 1 to 7.
10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the power metering error assessment method according to any one of claims 1 to 7.