New energy grid-connected electric energy metering method and device

Abstract

According to the new energy grid-connected electric energy metering method and device, broadband harmonic characteristic information and inter-harmonic characteristic information are respectively obtained according to preprocessed power grid voltage and power grid current; after the broadband harmonic characteristic information and the inter-harmonic characteristic information are input into the influence analysis topology network, a first error compensation parameter is calculated by using an electric energy metering error output by the influence analysis topology network; based on a non-sinusoidal signal simulation model, combining the preprocessed power grid voltage and power grid current with the step wave signal to obtain a non-sinusoidal wave signal; performing error compensation calculation on the non-sine wave signal to obtain a second error compensation parameter; obtaining time-varying error data of the electric energy meter by using historical operation data of the electric energy meter; a target error compensation parameter is calculated based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter, the electric energy meter performs electric energy metering based on the target error compensation parameter, an electric energy metering result is obtained, and the accuracy of new energy grid-connected electric energy metering is improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of electric energy metering, and in particular, relates to an electric energy metering method and device for new energy grid-connected power. Background Art

[0002] With the rapid development of new energy, a large number of new energy power generation system equipment, mainly photovoltaic, are connected to the grid and operated. The accuracy of grid metering after the new energy is connected to the grid directly affects the fairness of transactions between power generation, transmission and distribution and users. Therefore, accurate measurement of electric energy for new energy grid connection has received more and more attention.

[0003] In the current energy metering of renewable energy grid-connected, due to the presence of harmonics in renewable energy grid-connected, the energy meter may overestimate the actual amount of energy used, resulting in a large measurement error. Therefore, it is necessary to determine the error compensation for measurement based on harmonic information. In the prior art, relevant personnel usually perform curve fitting based on voltage data and current data to determine the harmonic characteristic information, so as to calculate the error compensation coefficient. However, this method is too dependent on the professional quality of relevant personnel, resulting in the inability to accurately determine the harmonic characteristic information and the error compensation coefficient, and the human cost invested is also too high. In addition, in the current measurement error compensation calculation, most of them lack consideration of the measurement error caused by interharmonics and non-sinusoidal signals. The lack of consideration of interharmonics and non-sinusoidal signals will have a greater impact on the energy metering accuracy of the energy meter, resulting in the low reliability of the error compensation coefficient obtained, and the lack of consideration of the measurement accuracy caused by interharmonics and non-sinusoidal signals will also cause the measurement error of the energy meter to accumulate continuously. In the error compensation calculation, the time-varying error analysis of the electricity meter is also a very important part. At present, the time-varying error analysis is mainly carried out through the state space equation, but the accuracy of this method for time-varying error analysis is still insufficient, which can easily lead to a large deviation between the obtained time-varying error data and the actual situation, making the accuracy of electricity metering of new energy grid-connected fail to reach the target. Summary of the invention

[0004] In order to solve the deficiencies in the prior art, the present invention provides a method and device for measuring electric energy for renewable energy grid-connected power, which accurately compensates for errors caused by harmonics and non-sinusoidal wave signals, improves the accuracy of electric energy metering, and achieves a more ideal effect of electric energy metering for renewable energy grid-connected power.

[0005] The present invention adopts the following technical solution.

[0006] The present invention proposes a method for measuring electric energy for renewable energy grid connection, comprising:

[0007] Obtain the grid topology, grid voltage and grid current under the renewable energy grid-connected working condition and perform preprocessing; wherein the renewable energy grid-connected working condition includes power flow conversion working condition, low power factor working condition, bidirectional wide range working condition and wide frequency band working condition;

[0008] According to the pre-processed grid voltage and grid current, the time-frequency domain analysis method is used to obtain the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data; according to the pre-processed grid topology structure, a causal directed acyclic graph is constructed; according to the wave time-frequency domain analysis data and the harmonic current frequency distribution data, the harmonic influence intensity is determined based on the causal directed acyclic graph; the harmonic time-frequency domain analysis data, the harmonic current frequency distribution data and the harmonic influence intensity are integrated to obtain the broadband harmonic characteristic information;

[0009] Performing phase rotation on the frequency spectrum coefficients corresponding to the pre-processed grid voltage and grid current, and calculating the first frequency, first amplitude and first phase of the interharmonic based on the frequency spectrum coefficients after phase rotation; using the wavelet analysis method to calculate the second frequency, second amplitude and second phase of the interharmonic for the pre-processed grid voltage and grid current; taking the average value of the first frequency and the second frequency, the average value of the first amplitude and the second amplitude, and the average value of the first phase and the second phase of the interharmonic as the interharmonic characteristic information;

[0010] The broadband harmonics affect the electric energy metering error by generating a broadband harmonics characteristic vector and a first distorted characteristic vector of the voltage and current to construct a broadband harmonics influence factor; the interharmonics affect the electric energy metering error by generating an interharmonics characteristic vector and a second distorted characteristic vector of the voltage and current to construct an interharmonics influence factor; the broadband harmonics influence factor, the interharmonics influence factor, the connection strength between the broadband harmonics influence factors and the connection strength between the interharmonics influence factors are used to form an impact analysis topological network; after the broadband harmonics characteristic information and the interharmonics characteristic information are input into the impact analysis topological network, the electric energy metering error output by the impact analysis topological network is used to calculate and obtain a first error compensation parameter;

[0011] A non-sinusoidal signal simulation model is established using the preprocessed grid topology structure; based on the non-sinusoidal signal simulation model, a non-sinusoidal wave signal is obtained by combining the preprocessed grid voltage and grid current with a step wave signal; an error compensation calculation is performed on the non-sinusoidal wave signal to obtain a second error compensation parameter;

[0012] The operation trajectory matrix is ​​constructed using the historical operation data of the electric energy meter. Based on the operation trajectory matrix, the time-varying error analysis of the electric energy meter under the condition of new energy grid connection is carried out to obtain the time-varying error data of the electric energy meter.

[0013] A target error compensation parameter is calculated based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter. The electric energy meter performs electric energy measurement based on the target error compensation parameter to obtain an electric energy measurement result.

[0014] Preferably, according to the preprocessed grid voltage and grid current, a time-frequency domain analysis method is used to obtain harmonic time-frequency domain analysis data and harmonic current frequency distribution data, including:

[0015] Building a power grid network topology model based on the preprocessed power grid topology structure;

[0016] Determine a frequency domain analysis window; based on the preprocessed grid voltage and grid current, use the frequency domain analysis window to perform fast Fourier transform to obtain a target spectrum, and calculate the frequency, amplitude and phase of the harmonics in the target spectrum;

[0017] Based on the frequency, amplitude and phase of the harmonics, harmonic time-frequency domain analysis is performed to obtain harmonic time-frequency domain analysis data; based on the harmonic time-frequency domain analysis data, harmonic current frequency distribution analysis is performed to obtain harmonic current frequency distribution data.

[0018] Preferably, the power grid network topology model includes nodes and directed edges. The nodes include new energy grid connection points, load nodes and other nodes. The directed edges are transmission lines connecting the nodes, and the flow direction on the transmission lines is used as the direction of each directed edge.

[0019] Preferably, a stable frequency reference point and a frequency domain range are set to determine a frequency domain analysis window, wherein the stable frequency reference point is the rated frequency of the power grid, and the frequency domain range is symmetrical and meets the requirements of power industry standards.

[0020] Preferably, time delay embedding is performed on the harmonic time series according to the frequency, amplitude and phase of the harmonic to obtain the phase space trajectory, geometric analysis is performed on the phase space trajectory to obtain the Lyapunov characteristic index, time-frequency domain analysis is performed according to the Lyapunov characteristic index combined with empirical mode decomposition to obtain the time-frequency trajectory and energy time-frequency distribution of the harmonic signal as the harmonic time-frequency domain analysis data;

[0021] The harmonic current frequency distribution analysis is performed based on the harmonic time-frequency domain analysis data. The frequency separation of the harmonic current time series is performed based on the harmonic time-frequency domain analysis data to obtain the harmonic current frequency, harmonic current frequency content rate and frequency duration as the harmonic current frequency distribution data.

[0022] Preferably, a causal directed acyclic graph is constructed based on the power grid network topology model; and harmonic impact intensity analysis is performed based on the causal directed acyclic graph according to the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data to obtain the harmonic impact intensity, including:

[0023] The nodes and directed edges in the power grid network topology model are learned according to the causal relationship discovery algorithm to obtain the causal strength between the nodes;

[0024] According to the Granger causality test, the causal strengths between nodes are combined to construct a causal strength matrix, and the causal strength matrix is ​​normalized to obtain a normalized causal strength matrix;

[0025] Connecting the nodes and directed edges of the directed acyclic graph according to the normalized causal strength matrix to obtain a causal directed acyclic graph;

[0026] According to the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data, the harmonic amplitude time series and the harmonic energy of each node in the causal directed acyclic graph are calculated; the harmonic contribution value is calculated according to the harmonic amplitude time series and the harmonic energy of each node, and the difference between the harmonic contribution value and the harmonic limit value is used as the harmonic influence intensity, wherein the harmonic limit value is set according to the national standard.

[0027] Preferably, a discrete Fourier transform is performed on the preprocessed grid voltage and grid current to obtain spectrum coefficients; the spectrum coefficients are phase rotated, and a first frequency, a first amplitude and a first phase of the interharmonics are calculated based on the spectrum coefficients after the phase rotation;

[0028] The pre-processed grid voltage and grid current are converted into analog voltage signals and analog current signals, and the analog voltage signals and analog current signals are decomposed by wavelet packets using wavelet basis functions to obtain frequency band wavelet coefficients; a reconstructed signal is obtained based on the frequency band wavelet coefficients, and a target wavelet basis function is determined using the reconstructed signal, the analog voltage signal and the analog current signal; and the inter-harmonic component detection is performed on the pre-processed grid voltage and grid current using the target wavelet basis function to obtain the second frequency, second phase and second amplitude of the corresponding inter-harmonic;

[0029] The average value of the first frequency and the second frequency of the interharmonic is calculated as the target frequency of the interharmonic, the average value of the first amplitude and the second amplitude of the interharmonic is calculated as the target amplitude of the interharmonic, and the average value of the first phase and the second phase of the interharmonic is calculated as the target phase of the interharmonic; the target frequency, target amplitude and target phase of the interharmonic are used as the interharmonic characteristic information.

[0030] Preferably, the number of wavelet packet decomposition layers is determined according to the sampling frequency and fundamental frequency of the analog voltage signal and the analog current signal, satisfying the following relationship:

[0031]

[0032] Where n is the number of wavelet packet decomposition layers, p is the sampling frequency, j is the base frequency, and σ is the correction coefficient.

[0033] Preferably, the covariance matrix of the error data set of the wideband harmonics affecting the electric energy metering is obtained as the first characteristic data matrix, and the covariance matrix of the error data set of the interharmonics affecting the electric energy metering is obtained as the second characteristic matrix; the matrix element composition analysis is performed on the first characteristic data matrix to generate the wideband harmonics characteristic vector and the first distortion characteristic vector of the voltage and current; the matrix element composition analysis is performed on the second characteristic matrix to generate the interharmonics characteristic vector and the second distortion characteristic vector of the voltage and current;

[0034] Performing feature interaction on the broadband harmonic eigenvector and the first distortion eigenvector of the voltage and current to determine the influence intensity of the broadband harmonics and the influence intensity of the broadband harmonics on the electric energy metering error as the broadband harmonic influence factor; performing interharmonic influence factor analysis on the interharmonic eigenvector and the second distortion eigenvector of the voltage and current to determine the influence intensity of the harmonics and the influence intensity of the interharmonics on the electric energy metering error as the interharmonic influence factor;

[0035] Determine a first connection strength between each broadband harmonic impact factor and a second connection strength between each interharmonic impact factor, construct a first analysis network according to the first connection strength and the broadband harmonic impact factor, construct a second analysis network according to the second connection strength and the interharmonic impact factor, and the first analysis network and the second analysis network constitute an impact analysis topology network;

[0036] After the broadband harmonic characteristic information and the interharmonic characteristic information are input into the impact analysis topology network, the first error compensation function is used to calculate the first error compensation parameter for the electric energy metering error output by the impact analysis topology network; wherein the first error compensation function is a preset compensation function.

[0037] Preferably, a non-sinusoidal signal simulation model is established using the preprocessed power grid topology structure; based on the non-sinusoidal signal simulation model, the preprocessed power grid voltage and power grid current are combined with the step wave signal to perform Fourier transform calculation to obtain Fourier transform coefficients; based on the Fourier transform coefficients, the non-sinusoidal wave signal data is calculated using the active power measurement algorithm and the reactive power measurement algorithm to obtain the non-sinusoidal wave signal data;

[0038] Based on the non-sinusoidal wave signal data, an error compensation calculation is performed using a second error compensation function to obtain a second error compensation parameter.

[0039] Preferably, historical operation data of the electric energy meter is obtained to perform time series chaos analysis to obtain time series chaos data;

[0040] Establish a Markov chain, and construct the operation trajectory matrix based on the Markov chain using time series chaotic data combined with a bidirectional Bayesian network;

[0041] Based on the operation trajectory matrix combined with the preset environmental change relationship table and the preset load change relationship table, the time-varying error analysis of the electric energy meter under the new energy grid-connected working condition is carried out to obtain the time-varying error data of the electric energy meter.

[0042] Preferably, a weighted sum of the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter is used as the target error compensation parameter;

[0043] The electric energy meter calibration parameters are determined based on the target error compensation parameters, the electric energy meter is calibrated based on the electric energy meter calibration parameters to obtain a calibrated electric energy meter, and the calibrated electric energy meter measures electric energy based on the target error compensation parameters to obtain an electric energy measurement result.

[0044] The present invention also proposes an electric energy metering device for new energy grid connection, comprising:

[0045] A data preprocessing module is used to obtain the grid topology, grid voltage and grid current under the renewable energy grid-connected working condition and perform preprocessing; wherein, the renewable energy grid-connected working condition includes power flow conversion working condition, low power factor working condition, bidirectional wide range working condition and wide frequency band working condition;

[0046] The broadband harmonic feature extraction module is used to obtain harmonic time-frequency domain analysis data and harmonic current frequency distribution data based on the pre-processed grid voltage and grid current by using the time-frequency domain analysis method; construct a causal directed acyclic graph based on the pre-processed grid topology; determine the harmonic influence intensity based on the causal directed acyclic graph based on the wave time-frequency domain analysis data and the harmonic current frequency distribution data; integrate the harmonic time-frequency domain analysis data, the harmonic current frequency distribution data and the harmonic influence intensity to obtain broadband harmonic feature information;

[0047] An interharmonic feature extraction module is used to perform phase rotation on the frequency spectrum coefficients corresponding to the preprocessed grid voltage and grid current, and calculate the first frequency, first amplitude and first phase of the interharmonic based on the frequency spectrum coefficients after phase rotation; use the wavelet analysis method to calculate the second frequency, second amplitude and second phase of the interharmonic for the preprocessed grid voltage and grid current; and use the average value of the first frequency and the second frequency, the average value of the first amplitude and the second amplitude, and the average value of the first phase and the second phase of the interharmonic as the interharmonic feature information;

[0048] A first error compensation parameter generating module is used to generate a broadband harmonic characteristic vector and a first distortion characteristic vector of voltage and current by using the error of broadband harmonic affecting electric energy metering, so as to construct a broadband harmonic influence factor; generate an interharmonic characteristic vector and a second distortion characteristic vector of voltage and current by using the error of interharmonic affecting electric energy metering, so as to construct an interharmonic influence factor; construct an influence analysis topological network by using the broadband harmonic influence factor, the interharmonic influence factor, the connection strength between each broadband harmonic influence factor and the connection strength between each interharmonic influence factor; after the broadband harmonic characteristic information and the interharmonic characteristic information are input into the influence analysis topological network, the first error compensation parameter is obtained by using the electric energy metering error calculation output by the influence analysis topological network;

[0049] The second error compensation parameter generating module is used to establish a non-sinusoidal signal simulation model by using the preprocessed power grid topology structure; based on the non-sinusoidal signal simulation model, the preprocessed power grid voltage and power grid current are combined with the step wave signal to obtain a non-sinusoidal wave signal; and the error compensation calculation of the non-sinusoidal wave signal is performed to obtain a second error compensation parameter;

[0050] The electric energy meter time-varying error analysis module is used to construct an operation trajectory matrix using the historical operation data of the electric energy meter, and to perform the electric energy meter time-varying error analysis under the new energy grid-connected working condition based on the operation trajectory matrix to obtain the electric energy meter time-varying error data;

[0051] The electric energy metering module is used to calculate the target error compensation parameter based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter. The electric energy meter performs electric energy metering based on the target error compensation parameter to obtain an electric energy metering result.

[0052] A terminal comprises a processor and a storage medium; the storage medium is used to store instructions; the processor is used to operate according to the instructions to execute the steps of the method.

[0053] A computer-readable storage medium stores a computer program, which implements the steps of the method when executed by a processor.

[0054] The beneficial effect of the present invention is that, compared with the prior art, it at least includes, based on the pre-processed power grid topology data, voltage waveform data and current waveform data, using causal inference analysis to extract broadband harmonic features, which can achieve more refined broadband harmonic feature extraction, making the obtained broadband harmonic feature information more comprehensive and specific. Based on the impact analysis topological network, the first error compensation parameter is generated using broadband harmonic feature information and interharmonic feature information, and the second error compensation parameter is generated based on the non-sinusoidal signal data. The interharmonic feature information and non-sinusoidal signal data are added to the generation of the error compensation parameter, so that the generated error compensation parameter is more accurate. The time-varying error analysis of the electric energy meter under complex operating conditions based on the operation trajectory matrix can improve the accuracy of the time-varying error analysis of the electric energy meter and avoid excessive deviation between the obtained electric energy meter time-varying error data and the actual situation. By calculating the target error compensation parameter through the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter to measure the electric energy of the electric energy meter, it is possible to avoid the continuous accumulation of measurement errors of the electric energy meter, greatly improve the accuracy of electric energy measurement, and achieve a more ideal effect of electric energy measurement for new energy grid connection. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 It is a flow chart of the electric energy metering method for new energy grid connection proposed by the present invention;

[0056] Figure 2 It is a schematic diagram of the structural composition of the electric energy metering device for renewable energy grid connection in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to make the purpose, technical scheme and advantages of the present invention clearer, the technical scheme of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. The embodiments described in this application are only embodiments of a part of the present invention, rather than all embodiments. Based on the spirit of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work belong to the protection scope of the present invention.

[0058] The present invention provides a method for measuring electric energy of a new energy grid-connected system. Figure 1 As shown, including:

[0059] Step 1, obtaining the grid topology, grid voltage and grid current under the renewable energy grid-connected working condition and performing preprocessing; wherein the renewable energy grid-connected working condition includes power flow conversion working condition, low power factor working condition, bidirectional wide range working condition and wide frequency band working condition.

[0060] Specifically, the topological structure data, the grid voltage waveform data and the grid current waveform data of the grid under the new energy grid-connected working condition are obtained, and the grid topological structure data, the grid voltage waveform data and the grid current waveform data are preprocessed to obtain the preprocessed grid topological structure data, the grid voltage waveform data and the grid current waveform data;

[0061] In the specific implementation process of the present invention, after the new energy is connected to the grid, the corresponding data acquisition unit is deployed to obtain the grid topology data, voltage waveform data and current waveform data under the complex operating conditions in the new energy grid connection. The grid topology data includes the connection relationship between the line, switch and transformer equipment, etc. The voltage waveform data is the data of the voltage changing over time, and the current waveform data is the data of the current changing over time. The complex operating conditions include power flow conversion, low power factor, bidirectional wide range and wide frequency band. Acquiring data under complex operating conditions in the new energy grid connection is conducive to analyzing the error compensation for electric energy metering under complex conditions. The grid topology data, voltage waveform data and current waveform data are preprocessed, and the preprocessing includes data cleaning, data repair, abnormal data elimination and data missing value filling to obtain the preprocessed grid topology data, voltage waveform data and current waveform data.

[0062] Step 2: Based on the preprocessed grid voltage and grid current, the time-frequency domain analysis method is used to obtain the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data; based on the preprocessed grid topology, a causal directed acyclic graph is constructed; based on the wave time-frequency domain analysis data and the harmonic current frequency distribution data, the harmonic influence intensity is determined based on the causal directed acyclic graph; the harmonic time-frequency domain analysis data, the harmonic current frequency distribution data and the harmonic influence intensity are integrated to obtain the broadband harmonic characteristic information.

[0063] Specifically, step 2 includes:

[0064] Step 2.1, constructing a power grid network topology model based on the preprocessed power grid topology structure;

[0065] In the embodiment, the power grid network topology model includes nodes and directed edges. The nodes include new energy grid connection points, load nodes and other nodes. The directed edges are transmission lines connecting the nodes, and the flow direction on the transmission lines is used as the direction of each directed edge.

[0066] Step 2.2, determining a frequency domain analysis window; based on the preprocessed grid voltage and grid current, using the frequency domain analysis window to perform fast Fourier transform to obtain a target spectrum, and calculating the frequency, amplitude and phase of the harmonics in the target spectrum;

[0067] In an embodiment, a stable frequency reference point and a frequency domain range are set to determine a frequency domain analysis window, wherein the stable frequency reference point is the rated frequency of the power grid, and the frequency domain range is symmetrical and meets the requirements of power industry standards.

[0068] The voltage signal and the current signal are converted from the time domain to the frequency domain by fast Fourier transform to reveal the frequency spectrum of the voltage signal and the current signal, and the target spectrum is obtained, so as to calculate the frequency, amplitude and phase of the harmonics in the target spectrum.

[0069] Step 2.3, performing harmonic time-frequency domain analysis based on the frequency, amplitude and phase of the harmonics to obtain harmonic time-frequency domain analysis data; performing harmonic current frequency distribution analysis based on the harmonic time-frequency domain analysis data to obtain harmonic current frequency distribution data;

[0070] In the embodiment, the harmonic time series is time-delayed and embedded according to the frequency, amplitude and phase of the harmonic to obtain the phase space trajectory, the phase space trajectory is geometrically analyzed to obtain the Lyapunov characteristic index, and the time-frequency domain analysis is performed according to the Lyapunov characteristic index combined with the empirical mode decomposition to obtain the time-frequency trajectory and energy time-frequency distribution of the harmonic signal as the harmonic time-frequency domain analysis data. The harmonic current frequency distribution analysis is performed based on the harmonic time-frequency domain analysis data, and the frequency separation of the harmonic current time series is performed according to the harmonic time-frequency domain analysis data to obtain the harmonic current frequency, the harmonic current frequency content and the frequency duration as the harmonic current frequency distribution data.

[0071] Step 2.4, based on the power grid network topology model, a causal directed acyclic graph is constructed; based on the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data, a harmonic impact intensity analysis is performed based on the causal directed acyclic graph to obtain the harmonic impact intensity;

[0072] include:

[0073] Step 2.4.1, learning the nodes and directed edges in the power grid network topology model according to the causal relationship discovery algorithm to obtain the causal strength between the nodes;

[0074] Step 2.4.2, combining the causal strengths between nodes according to the Granger causality test to construct a causal strength matrix, and normalizing the causal strength matrix to obtain a normalized causal strength matrix;

[0075] Step 2.4.3, connect the nodes and directed edges of the directed acyclic graph according to the normalized causal strength matrix to obtain a causal directed acyclic graph.

[0076] In the present invention, the power grid network topology model constructed based on the preprocessed power grid topology structure is a directed acyclic graph. There is an association relationship between two nodes on the same edge, not a causal relationship. Therefore, it is necessary to construct a causal directed acyclic graph based on the power grid network topology model to exclude interference from non-causal nodes. According to the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data, based on the causal directed acyclic graph, a more accurate harmonic impact intensity is excavated, which can exclude the influence of other harmonic interference sources on the grid-connected metering of new energy.

[0077] Step 2.4.4, calculate the harmonic amplitude time series and harmonic energy of each node in the causal directed acyclic graph based on the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data; calculate the harmonic contribution value based on the harmonic amplitude time series and harmonic energy of each node, and use the difference between the harmonic contribution value and the harmonic limit value as the harmonic impact intensity, where the harmonic limit value is set according to national standards.

[0078] In step 2.5, a broadband harmonic feature integration model based on a deep neural network is used to integrate the harmonic time-frequency domain analysis data, harmonic current frequency distribution data, and harmonic influence intensity to obtain broadband harmonic feature information.

[0079] Broadband harmonics refer to harmonics with broadband frequency domain characteristics, and the harmonic frequency range is wide. In the power system, broadband harmonics are mainly derived from factors such as renewable energy power generation and multi-type loads. These factors lead to the continuous increase of harmonic content in the power grid, the widening of the harmonic frequency domain range, and the presentation of broadband frequency domain characteristics. The harmonic time-frequency domain analysis data, harmonic current frequency distribution data, and harmonic influence intensity data are input into the broadband harmonic feature integration model for feature integration. The broadband harmonic feature integration model is a convergence model obtained by inputting the sample data set into a deep neural network for training to obtain broadband harmonic feature information, which can achieve more refined broadband harmonic feature extraction, making the obtained broadband harmonic feature information more comprehensive and specific, and greatly reducing the complexity of artificial feature engineering.

[0080] Step 3, phase-rotate the spectral coefficients corresponding to the preprocessed grid voltage and grid current, and calculate the first frequency, first amplitude and first phase of the interharmonics based on the spectral coefficients after phase rotation; use the wavelet analysis method to calculate the second frequency, second amplitude and second phase of the interharmonics for the preprocessed grid voltage and grid current; and use the average value of the first frequency and second frequency, the average value of the first amplitude and the second amplitude, and the average value of the first phase and the second phase of the interharmonics as the interharmonic feature information.

[0081] Specifically, step 3 includes:

[0082] Step 3.1, performing discrete Fourier transform on the preprocessed grid voltage and grid current to obtain spectrum coefficients; performing phase rotation on the spectrum coefficients, and calculating the first frequency, first amplitude and first phase of the interharmonic based on the spectrum coefficients after the phase rotation;

[0083] In the embodiment, the preprocessed grid voltage and grid current are subjected to discrete Fourier transform using a rectangular window with a preset fundamental wave cycle length, and the data is converted from the time domain to the frequency domain through discrete Fourier transform to obtain spectrum coefficients.

[0084] In an embodiment, since both the positive and negative frequency components of the interharmonic spectrum contain phase factors, and the phase factors will affect the phase of the spectrum coefficients as the spectral line position changes, performing phase rotation on the spectrum coefficients can eliminate the influence of interharmonic spectrum leakage, achieve accurate calculation of the interharmonic components, obtain the spectrum coefficients after phase rotation, perform spectrum analysis on the spectrum coefficients after phase rotation, and obtain the first frequency, first amplitude, and first phase of the interharmonic.

[0085] Step 3.2, converting the preprocessed grid voltage and grid current into analog voltage signals and analog current signals, performing wavelet packet decomposition on the analog voltage signals and analog current signals using wavelet basis functions to obtain frequency band wavelet coefficients; obtaining a reconstructed signal based on the frequency band wavelet coefficients, and determining a target wavelet basis function using the reconstructed signal, the analog voltage signal, and the analog current signal; performing interharmonic component detection on the preprocessed grid voltage and grid current using the target wavelet basis function to obtain a second frequency, a second phase, and a second amplitude of the corresponding interharmonics;

[0086] In the embodiment, the preprocessed grid voltage and grid current are converted into analog voltage signals and analog current signals, and the analog voltage signals and analog current signals are decomposed by wavelet packets according to the number of wavelet packet decomposition layers to obtain corresponding wavelet coefficients of several frequency bands; wherein the number of wavelet packet decomposition layers is determined according to the sampling frequency and base frequency of the analog voltage signal and the analog current signal, and the following relationship is satisfied:

[0087]

[0088] Where n is the number of wavelet packet decomposition layers, p is the sampling frequency, j is the base frequency, and σ is the correction coefficient;

[0089] In the embodiment, a single-branch reconstruction is performed based on the frequency band wavelet coefficients to obtain several reconstructed signals, a fast Fourier transform is performed based on the reconstructed signal, the analog voltage signal, and the analog current signal to obtain corresponding amplitude information and phase information, a phase error and an amplitude error are calculated based on the corresponding amplitude information and phase information, a target wavelet basis function is determined based on the phase error and the amplitude error, and an interharmonic component detection is performed on the preprocessed grid voltage and grid current using the target wavelet basis function to obtain a second frequency, a second phase, and a second amplitude of the corresponding interharmonic.

[0090] Step 3.3, calculate the average value of the first frequency and the second frequency of the interharmonic as the target frequency of the interharmonic, calculate the average value of the first amplitude and the second amplitude of the interharmonic as the target amplitude of the interharmonic, calculate the average value of the first phase and the second phase of the interharmonic as the target phase of the interharmonic; the target frequency, target amplitude and target phase of the interharmonic are used as the interharmonic characteristic information.

[0091] The present invention realizes accurate extraction of interharmonic characteristic information.

[0092] Step 4, using the error of broadband harmonics affecting electric energy metering to generate broadband harmonic characteristic vectors and the first distortion characteristic vectors of voltage and current to construct broadband harmonic influence factors; using the error of interharmonics affecting electric energy metering to generate interharmonic characteristic vectors and the second distortion characteristic vectors of voltage and current to construct interharmonic influence factors; using broadband harmonic influence factors, interharmonic influence factors, the connection strength between each broadband harmonic influence factor and the connection strength between each interharmonic influence factor to form an impact analysis topological network; after broadband harmonic characteristic information and interharmonic characteristic information are input into the impact analysis topological network, the first error compensation parameter is obtained by calculating the electric energy metering error output by the impact analysis topological network.

[0093] Specifically, step 4 includes:

[0094] Step 4.1, obtaining the covariance matrix of the error data set of broadband harmonics affecting electric energy metering as the first characteristic data matrix, and obtaining the covariance matrix of the error data set of interharmonics affecting electric energy metering as the second characteristic matrix; performing matrix element composition analysis on the first characteristic data matrix to generate broadband harmonics characteristic vectors and first distortion characteristic vectors of voltage and current; performing matrix element composition analysis on the second characteristic matrix to generate interharmonics characteristic vectors and second distortion characteristic vectors of voltage and current;

[0095] In an embodiment, an error data set of the influence of broadband harmonics on electric energy metering and an error data set of the influence of interharmonics on electric energy metering are obtained in a database, wherein the error data of the influence of broadband harmonics on electric energy metering include: numerical values ​​corresponding to physical quantities such as the influence of broadband harmonics on the distortion of grid voltage and grid current, and the error data of the influence of interharmonics on electric energy metering include: numerical values ​​corresponding to physical quantities such as the influence of broadband harmonics on the distortion of grid voltage and grid current; a first characteristic data matrix is ​​constructed with the digitized error data set of the influence of broadband harmonics on electric energy metering, and a second characteristic matrix is ​​constructed with the digitized error data set of the influence of interharmonics on electric energy metering;

[0096] The first characteristic data matrix is ​​constructed by using the covariance matrix of the error data set of broadband harmonics affecting electric energy measurement, and the second characteristic matrix is ​​constructed by using the covariance matrix of the error data set of interharmonics affecting electric energy measurement.

[0097] Step 4.2, feature interaction is performed on the broadband harmonic feature vector and the first distorted feature vector of the voltage and current to determine the influence intensity of the broadband harmonics and the influence intensity of the broadband harmonics on the electric energy metering error as the broadband harmonic influence factor; interharmonic influence factor analysis is performed on the interharmonic feature vector and the second distorted feature vector of the voltage and current to determine the influence intensity of the harmonics and the influence intensity of the interharmonics on the electric energy metering error as the interharmonic influence factor;

[0098] Step 4.3, determining the first connection strength between each broadband harmonic impact factor and the second connection strength between each interharmonic impact factor, constructing a first analysis network according to the first connection strength and the broadband harmonic impact factor, constructing a second analysis network according to the second connection strength and the interharmonic impact factor, and the first analysis network and the second analysis network constitute an impact analysis topological network;

[0099] Step 4.4, after the broadband harmonic characteristic information and the interharmonic characteristic information are input into the impact analysis topology network, the first error compensation function is used to calculate the first error compensation parameter for the electric energy metering error output by the impact analysis topology network; wherein the first error compensation function is a preset compensation function.

[0100] The first error compensation parameter obtained by the present invention takes into account the influence of broadband harmonics and interharmonics on the electric energy metering error, so that the obtained error compensation parameter is more reliable.

[0101] Step 5, using the preprocessed grid topology to establish a non-sinusoidal signal simulation model; based on the non-sinusoidal signal simulation model, using the preprocessed grid voltage and grid current combined with the step wave signal to obtain a non-sinusoidal wave signal; performing error compensation calculation on the non-sinusoidal wave signal to obtain a second error compensation parameter.

[0102] Specifically, step 5 includes:

[0103] Step 5.1, using the preprocessed power grid topology to establish a non-sinusoidal signal simulation model; based on the non-sinusoidal signal simulation model, using the preprocessed power grid voltage and power grid current combined with the step wave signal to perform Fourier transform calculation to obtain Fourier transform coefficients; based on the Fourier transform coefficients, using the active power measurement algorithm and the reactive power measurement algorithm to perform non-sinusoidal wave signal data calculation to obtain non-sinusoidal wave signal data;

[0104] In an embodiment, a non-sinusoidal signal simulation model is constructed in the simulation platform software according to the topological structure data of the new energy grid-connected. Under the condition that the non-sinusoidal signal voltage and current harmonic models are the fundamental waves, the effective values ​​of the voltage and current of the harmonics below the preset order and the phase angles of each harmonic voltage and current are selected to construct the non-sinusoidal signal simulation model.

[0105] The step wave signal is a non-sinusoidal waveform signal, which is characterized by maintaining a constant amplitude within a specified time period and then jumping to another constant amplitude value in a short time. In the non-sinusoidal signal simulation model, the step wave signal and the preset non-sinusoidal wave signal are differentially sampled at equal intervals to obtain the differential value. According to the differential value combined with the preprocessed grid voltage and grid current, discrete Fourier transform calculation is performed at the preset sampling point to obtain the Fourier transform coefficient.

[0106] In the embodiment, the active power measurement algorithm adopts the active power algorithm of the frequency domain angle, and the reactive power measurement algorithm adopts the root mean square algorithm, and then the simulation calculation of the non-sinusoidal wave signal data is performed to obtain the non-sinusoidal wave signal data.

[0107] Step 5.2: Based on the non-sinusoidal wave signal data, use the second error compensation function to perform error compensation calculation to obtain a second error compensation parameter.

[0108] In the embodiment, a non-sinusoidal wave signal refers to a signal whose waveform does not conform to the characteristics of a sinusoidal function. The presence of a non-sinusoidal wave signal will cause grid voltage fluctuations and affect the metering performance of the electric energy meter. Its superposition with broadband harmonics and interharmonics will cause the continuous accumulation of grid metering errors. Therefore, considering the impact of non-sinusoidal wave signals on electric energy metering can avoid the continuous accumulation of grid metering errors.

[0109] The second error compensation function is a preset compensation function.

[0110] Step 6: Use the historical operation data of the electric energy meter to construct an operation trajectory matrix, and perform time-varying error analysis of the electric energy meter under the new energy grid-connected working condition based on the operation trajectory matrix to obtain the time-varying error data of the electric energy meter.

[0111] Specifically, step 6 includes:

[0112] Step 6.1, obtaining historical operation data of the electric energy meter for time series chaos analysis to obtain time series chaos data;

[0113] In the implementation example, the historical operation information of the electric energy meter is obtained in the database, and the historical operation information of the electric energy meter includes the operation time, the abnormal frequency in each time period, the maintenance record and the load condition, etc. The time series chaos analysis is performed on the historical operation information of the electric energy meter. The time series chaos analysis is the chaos analysis of the time series, which is used to analyze the nonlinear time series with chaotic characteristics. The Lyapunov exponent is used to perform the time series chaos analysis on the historical operation information of the electric energy meter, and the chaotic characteristics of the time series in the historical operation information of the electric energy meter are obtained, and the time series chaos data is obtained.

[0114] Step 6.2, establish a Markov chain, and construct the operation trajectory matrix based on the Markov chain using time series chaotic data combined with a bidirectional Bayesian network;

[0115] In the embodiment, a Markov chain is established, the frequency of abnormal occurrence is calculated through the historical operation information of the electric energy meter, and the Markov chain is constructed according to the abnormal occurrence frequency combined with the state conversion stage of the electric energy meter. Based on the Markov chain, the time series chaotic data is combined with the bidirectional Bayesian network to construct an operation trajectory matrix, a time series matrix is ​​formed according to the time series chaotic data, and the possibility of abnormal occurrence is calculated according to the Markov chain using the bidirectional Bayesian network to obtain the calculation result of the possibility of abnormal occurrence, and the calculation result of the possibility of abnormal occurrence is combined with the time series matrix to obtain the operation trajectory matrix.

[0116] Step 6.3, based on the operation trajectory matrix combined with the preset environment change relationship table and the preset load change relationship table, the time-varying error analysis of the electric energy meter under the new energy grid-connected working condition is performed to obtain the time-varying error data of the electric energy meter.

[0117] In the embodiment, the preset environment change relationship table is the performance change of the electric energy meter under different environments, and the preset load transformation relationship table is the performance change of the electric energy meter under different load states. The consumption analysis of the electric energy meter changing over time is performed by combining the operation trajectory matrix with the current electric energy meter operation data to obtain the corresponding consumption data. The performance data of the electric energy meter changing over time is determined according to the current environment data and the current load data combined with the preset environment change relationship table and the preset load change relationship table, so as to constitute the time-varying error data of the electric energy meter. The time-varying error refers to that certain parameters or characteristics of a device or system will change over time, thereby causing errors.

[0118] Step 7: Calculate a target error compensation parameter based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter. The electric energy meter measures electric energy based on the target error compensation parameter to obtain an electric energy measurement result.

[0119] Specifically, step 7 includes:

[0120] Step 7.1, taking the weighted sum of the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter as the target error compensation parameter;

[0121] In the embodiment, different parameters correspond to different weight coefficients, and corresponding weight coefficient matching can be performed in the database.

[0122] Step 7.2, determining the electric energy meter calibration parameters based on the target error compensation parameters, calibrating the electric energy meter based on the electric energy meter calibration parameters to obtain a calibrated electric energy meter, and measuring electric energy based on the target error compensation parameters to obtain an electric energy measurement result.

[0123] In the embodiment, the electric energy measurement is corrected by using the target error compensation parameter to reduce the error of the electric energy measurement to a minimum and obtain the electric energy measurement result.

[0124] The present invention also provides an electric energy metering device for new energy grid connection, such as Figure 2 As shown, including:

[0125] The data preprocessing module 31 is used to obtain the grid topology, grid voltage and grid current under the renewable energy grid-connected working condition and perform preprocessing; wherein the renewable energy grid-connected working condition includes power flow conversion working condition, low power factor working condition, bidirectional wide range working condition and wide frequency band working condition;

[0126] The broadband harmonic feature extraction module 32 is used to obtain harmonic time-frequency domain analysis data and harmonic current frequency distribution data by using the time-frequency domain analysis method according to the preprocessed grid voltage and grid current; construct a causal directed acyclic graph according to the preprocessed grid topology; determine the harmonic influence intensity based on the causal directed acyclic graph according to the wave time-frequency domain analysis data and the harmonic current frequency distribution data; integrate the harmonic time-frequency domain analysis data, the harmonic current frequency distribution data and the harmonic influence intensity to obtain broadband harmonic feature information;

[0127] The interharmonic feature extraction module 33 is used to perform phase rotation on the frequency spectrum coefficients corresponding to the preprocessed grid voltage and grid current, and calculate the first frequency, first amplitude and first phase of the interharmonic based on the frequency spectrum coefficients after phase rotation; calculate the second frequency, second amplitude and second phase of the interharmonic by using the wavelet analysis method for the preprocessed grid voltage and grid current; and use the average value of the first frequency and the second frequency, the average value of the first amplitude and the second amplitude, and the average value of the first phase and the second phase of the interharmonic as the interharmonic feature information;

[0128] The first error compensation parameter generating module 34 is used to generate a broadband harmonic characteristic vector and a first distortion characteristic vector of voltage and current by using the error of broadband harmonic affecting electric energy metering, so as to construct a broadband harmonic influence factor; generate an interharmonic characteristic vector and a second distortion characteristic vector of voltage and current by using the error of interharmonic affecting electric energy metering, so as to construct an interharmonic influence factor; construct an influence analysis topological network by using the broadband harmonic influence factor, the interharmonic influence factor, the connection strength between each broadband harmonic influence factor and the connection strength between each interharmonic influence factor; after the broadband harmonic characteristic information and the interharmonic characteristic information are input into the influence analysis topological network, the first error compensation parameter is obtained by using the electric energy metering error calculation output by the influence analysis topological network;

[0129] The second error compensation parameter generating module 35 is used to establish a non-sinusoidal signal simulation model by using the preprocessed grid topology structure; based on the non-sinusoidal signal simulation model, the preprocessed grid voltage and grid current are combined with the step wave signal to obtain a non-sinusoidal wave signal; and error compensation calculation is performed on the non-sinusoidal wave signal to obtain a second error compensation parameter;

[0130] The electric energy meter time-varying error analysis module 36 is used to construct an operation trajectory matrix using the historical operation data of the electric energy meter, and perform the electric energy meter time-varying error analysis under the new energy grid-connected working condition based on the operation trajectory matrix to obtain the electric energy meter time-varying error data;

[0131] The electric energy metering module 37 is used to calculate a target error compensation parameter based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter. The electric energy meter performs electric energy metering based on the target error compensation parameter to obtain an electric energy metering result.

[0132] The present disclosure may be a system, a method and / or a computer program product. The computer program product may include a computer-readable storage medium carrying computer-readable program instructions for causing a processor to implement various aspects of the present disclosure.

[0133] A computer-readable storage medium may be a tangible device that can hold and store instructions used by an instruction execution device. A computer-readable storage medium may be, for example, but not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples of computer-readable storage media (a non-exhaustive list) include: a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a static random access memory (SRAM), a portable compact disk read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanical encoding device, such as a punch card or a raised structure in a groove on which instructions are stored, and any suitable combination of the foregoing. As used herein, a computer-readable storage medium is not to be interpreted as a transient signal per se, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse through a fiber optic cable), or an electrical signal transmitted through a wire.

[0134] The computer-readable program instructions described herein can be downloaded from a computer-readable storage medium to each computing / processing device, or downloaded to an external computer or external storage device via a network, such as the Internet, a local area network, a wide area network, and / or a wireless network. The network can include copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or edge servers. The network adapter card or network interface in each computing / processing device receives the computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in the computer-readable storage medium in each computing / processing device.

[0135] The computer program instructions for performing the operation of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as "C" language or similar programming languages. Computer-readable program instructions may be executed completely on a user's computer, partially on a user's computer, as an independent software package, partially on a user's computer, partially on a remote computer, or completely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., using an Internet service provider to connect via the Internet). In some embodiments, an electronic circuit, such as a programmable logic circuit, a field programmable gate array (FPGA), or a programmable logic array (PLA), may be customized by utilizing the state information of the computer-readable program instructions, and the electronic circuit may execute the computer-readable program instructions, thereby realizing various aspects of the present disclosure.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents, and any modifications or equivalent replacements that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for measuring electric energy for new energy grid connection, characterized in that: include: Obtain the grid topology, grid voltage and grid current under the renewable energy grid-connected working condition and perform preprocessing; wherein the renewable energy grid-connected working condition includes power flow conversion working condition, low power factor working condition, bidirectional wide range working condition and wide frequency band working condition; According to the pre-processed grid voltage and grid current, the time-frequency domain analysis method is used to obtain the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data; according to the pre-processed grid topology structure, a causal directed acyclic graph is constructed; according to the wave time-frequency domain analysis data and the harmonic current frequency distribution data, the harmonic influence intensity is determined based on the causal directed acyclic graph; the harmonic time-frequency domain analysis data, the harmonic current frequency distribution data and the harmonic influence intensity are integrated to obtain the broadband harmonic characteristic information; Performing phase rotation on the frequency spectrum coefficients corresponding to the pre-processed grid voltage and grid current, and calculating the first frequency, first amplitude and first phase of the interharmonic based on the frequency spectrum coefficients after phase rotation; using the wavelet analysis method to calculate the second frequency, second amplitude and second phase of the interharmonic for the pre-processed grid voltage and grid current; taking the average value of the first frequency and the second frequency, the average value of the first amplitude and the second amplitude, and the average value of the first phase and the second phase of the interharmonic as the interharmonic characteristic information; The broadband harmonics affect the electric energy metering error by generating a broadband harmonics characteristic vector and a first distorted characteristic vector of the voltage and current to construct a broadband harmonics influence factor; the interharmonics affect the electric energy metering error by generating an interharmonics characteristic vector and a second distorted characteristic vector of the voltage and current to construct an interharmonics influence factor; the broadband harmonics influence factor, the interharmonics influence factor, the connection strength between the broadband harmonics influence factors and the connection strength between the interharmonics influence factors are used to form an impact analysis topological network; after the broadband harmonics characteristic information and the interharmonics characteristic information are input into the impact analysis topological network, the electric energy metering error output by the impact analysis topological network is used to calculate and obtain a first error compensation parameter; A non-sinusoidal signal simulation model is established using the preprocessed grid topology structure; based on the non-sinusoidal signal simulation model, a non-sinusoidal wave signal is obtained by combining the preprocessed grid voltage and grid current with a step wave signal; an error compensation calculation is performed on the non-sinusoidal wave signal to obtain a second error compensation parameter; The operation trajectory matrix is ​​constructed using the historical operation data of the electric energy meter. Based on the operation trajectory matrix, the time-varying error analysis of the electric energy meter under the condition of new energy grid connection is carried out to obtain the time-varying error data of the electric energy meter. A target error compensation parameter is calculated based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter. The electric energy meter performs electric energy measurement based on the target error compensation parameter to obtain an electric energy measurement result.

2. The electric energy metering method for new energy grid connection according to claim 1 is characterized in that: include: According to the pre-processed grid voltage and grid current, the time-frequency domain analysis method is used to obtain the harmonic time-frequency domain analysis data and harmonic current frequency distribution data, including: Building a power grid network topology model based on the preprocessed power grid topology structure; Determine a frequency domain analysis window; based on the preprocessed grid voltage and grid current, use the frequency domain analysis window to perform fast Fourier transform to obtain a target spectrum, and calculate the frequency, amplitude and phase of the harmonics in the target spectrum; Based on the frequency, amplitude and phase of the harmonics, harmonic time-frequency domain analysis is performed to obtain harmonic time-frequency domain analysis data; based on the harmonic time-frequency domain analysis data, harmonic current frequency distribution analysis is performed to obtain harmonic current frequency distribution data.

3. The electric energy metering method for new energy grid connection according to claim 2 is characterized in that: include: The power grid network topology model includes nodes and directed edges. The nodes include new energy grid connection points, load nodes and other nodes. The directed edges are the transmission lines connecting the nodes, and the flow direction on the transmission lines is used as the direction of each directed edge.

4. The electric energy metering method for new energy grid connection according to claim 3 is characterized in that: include: A stable frequency reference point and a frequency domain range are set to determine a frequency domain analysis window, wherein the stable frequency reference point is the rated frequency of the power grid, and the frequency domain range is symmetrical and meets the requirements of power industry standards.

5. The electric energy metering method for new energy grid connection according to claim 4 is characterized in that: include: According to the frequency, amplitude and phase of the harmonics, the harmonic time series is embedded with time delay to obtain the phase space trajectory, the phase space trajectory is geometrically analyzed to obtain the Lyapunov characteristic index, and the time-frequency domain analysis is performed based on the Lyapunov characteristic index combined with the empirical mode decomposition to obtain the time-frequency trajectory and energy time-frequency distribution of the harmonic signal as the harmonic time-frequency domain analysis data; The harmonic current frequency distribution analysis is performed based on the harmonic time-frequency domain analysis data. The frequency separation of the harmonic current time series is performed based on the harmonic time-frequency domain analysis data to obtain the harmonic current frequency, harmonic current frequency content rate and frequency duration as the harmonic current frequency distribution data.

6. The electric energy metering method for new energy grid connection according to claim 3 is characterized in that: include: Based on the power grid network topology model, a causal directed acyclic graph is constructed; According to the harmonic time-frequency domain analysis data and harmonic current frequency distribution data, the harmonic impact intensity analysis is performed based on the causal directed acyclic graph to obtain the harmonic impact intensity, including: The nodes and directed edges in the power grid network topology model are learned according to the causal relationship discovery algorithm to obtain the causal strength between the nodes; According to the Granger causality test, the causal strengths between nodes are combined to construct a causal strength matrix, and the causal strength matrix is ​​normalized to obtain a normalized causal strength matrix; Connecting the nodes and directed edges of the directed acyclic graph according to the normalized causal strength matrix to obtain a causal directed acyclic graph; According to the harmonic time-frequency domain analysis data and the harmonic current frequency distribution data, the harmonic amplitude time series and the harmonic energy of each node in the causal directed acyclic graph are calculated; the harmonic contribution value is calculated according to the harmonic amplitude time series and the harmonic energy of each node, and the difference between the harmonic contribution value and the harmonic limit value is used as the harmonic influence intensity, wherein the harmonic limit value is set according to the national standard.

7. The electric energy metering method for new energy grid connection according to claim 1 is characterized in that: include: Performing discrete Fourier transform on the preprocessed grid voltage and grid current to obtain frequency spectrum coefficients; Performing phase rotation on the frequency spectrum coefficients, and calculating a first frequency, a first amplitude, and a first phase of the interharmonics based on the frequency spectrum coefficients after the phase rotation; The pre-processed grid voltage and grid current are converted into analog voltage signals and analog current signals, and the analog voltage signals and analog current signals are decomposed by wavelet packets using wavelet basis functions to obtain frequency band wavelet coefficients; a reconstructed signal is obtained based on the frequency band wavelet coefficients, and a target wavelet basis function is determined using the reconstructed signal, the analog voltage signal, and the analog current signal; Using the target wavelet basis function to detect the interharmonic components of the preprocessed grid voltage and grid current, and obtain the second frequency, second phase and second amplitude of the corresponding interharmonics; The average value of the first frequency and the second frequency of the interharmonic is calculated as the target frequency of the interharmonic, the average value of the first amplitude and the second amplitude of the interharmonic is calculated as the target amplitude of the interharmonic, and the average value of the first phase and the second phase of the interharmonic is calculated as the target phase of the interharmonic; the target frequency, target amplitude and target phase of the interharmonic are used as the interharmonic characteristic information.

8. The electric energy metering method for new energy grid connection according to claim 7 is characterized in that: include: The number of wavelet packet decomposition layers is determined according to the sampling frequency and fundamental frequency of the analog voltage signal and the analog current signal, satisfying the following relationship: Where n is the number of wavelet packet decomposition layers, p is the sampling frequency, j is the base frequency, and σ is the correction coefficient.

9. The electric energy metering method for new energy grid connection according to claim 1 is characterized in that: include: Obtaining a covariance matrix of an error data set of broadband harmonics affecting electric energy metering as a first characteristic data matrix, and obtaining a covariance matrix of an error data set of interharmonics affecting electric energy metering as a second characteristic matrix; Performing matrix element composition analysis on the first characteristic data matrix to generate a broadband harmonic characteristic vector and a first distortion characteristic vector of voltage and current; performing matrix element composition analysis on the second characteristic matrix to generate an interharmonic characteristic vector and a second distortion characteristic vector of voltage and current; Performing feature interaction on the broadband harmonic eigenvector and the first distortion eigenvector of the voltage and current to determine the influence intensity of the broadband harmonics and the influence intensity of the broadband harmonics on the electric energy metering error as the broadband harmonic influence factor; performing interharmonic influence factor analysis on the interharmonic eigenvector and the second distortion eigenvector of the voltage and current to determine the influence intensity of the harmonics and the influence intensity of the interharmonics on the electric energy metering error as the interharmonic influence factor; Determine a first connection strength between each broadband harmonic impact factor and a second connection strength between each interharmonic impact factor, construct a first analysis network according to the first connection strength and the broadband harmonic impact factor, construct a second analysis network according to the second connection strength and the interharmonic impact factor, and the first analysis network and the second analysis network constitute an impact analysis topology network; After the broadband harmonic characteristic information and the interharmonic characteristic information are input into the impact analysis topology network, the first error compensation function is used to calculate the first error compensation parameter for the electric energy metering error output by the impact analysis topology network; wherein the first error compensation function is a preset compensation function.

10. The electric energy metering method for new energy grid connection according to claim 1, characterized in that: include: A non-sinusoidal signal simulation model is established using the pre-processed grid topology structure; based on the non-sinusoidal signal simulation model, the pre-processed grid voltage and grid current are combined with the step wave signal to perform Fourier transform calculation to obtain the Fourier transform coefficient; Based on the Fourier transform coefficient, the non-sinusoidal wave signal data is calculated by using the active power measurement algorithm and the reactive power measurement algorithm to obtain the non-sinusoidal wave signal data; Based on the non-sinusoidal wave signal data, an error compensation calculation is performed using a second error compensation function to obtain a second error compensation parameter.

11. The electric energy metering method for new energy grid connection according to claim 1, characterized in that: include: Obtain historical operation data of the electric energy meter to perform time series chaos analysis to obtain time series chaos data; Establish a Markov chain, and construct the operation trajectory matrix based on the Markov chain using time series chaotic data combined with a bidirectional Bayesian network; Based on the operation trajectory matrix combined with the preset environmental change relationship table and the preset load change relationship table, the time-varying error analysis of the electric energy meter under the new energy grid-connected working condition is carried out to obtain the time-varying error data of the electric energy meter.

12. The electric energy metering method for new energy grid connection according to claim 1, characterized in that: include: Taking the weighted sum of the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter as the target error compensation parameter; The electric energy meter calibration parameters are determined based on the target error compensation parameters, the electric energy meter is calibrated based on the electric energy meter calibration parameters to obtain a calibrated electric energy meter, and the calibrated electric energy meter measures electric energy based on the target error compensation parameters to obtain an electric energy measurement result.

13. An electric energy metering device for new energy grid connection, characterized in that: include: A data preprocessing module is used to obtain the grid topology, grid voltage and grid current under the renewable energy grid-connected working condition and perform preprocessing; wherein, the renewable energy grid-connected working condition includes power flow conversion working condition, low power factor working condition, bidirectional wide range working condition and wide frequency band working condition; The broadband harmonic feature extraction module is used to obtain harmonic time-frequency domain analysis data and harmonic current frequency distribution data based on the preprocessed grid voltage and grid current by using the time-frequency domain analysis method; construct a causal directed acyclic graph based on the preprocessed grid topology; determine the harmonic influence intensity based on the causal directed acyclic graph based on the wave time-frequency domain analysis data and the harmonic current frequency distribution data; integrate the harmonic time-frequency domain analysis data, the harmonic current frequency distribution data and the harmonic influence intensity to obtain broadband harmonic feature information; An interharmonic feature extraction module is used to perform phase rotation on the frequency spectrum coefficients corresponding to the preprocessed grid voltage and grid current, and calculate the first frequency, first amplitude and first phase of the interharmonic based on the frequency spectrum coefficients after phase rotation; use the wavelet analysis method to calculate the second frequency, second amplitude and second phase of the interharmonic for the preprocessed grid voltage and grid current; and use the average value of the first frequency and the second frequency, the average value of the first amplitude and the second amplitude, and the average value of the first phase and the second phase of the interharmonic as the interharmonic feature information; A first error compensation parameter generating module is used to generate a broadband harmonic characteristic vector and a first distortion characteristic vector of voltage and current by using the error of broadband harmonics affecting electric energy metering, so as to construct a broadband harmonic influence factor; generate an interharmonic characteristic vector and a second distortion characteristic vector of voltage and current by using the error of interharmonics affecting electric energy metering, so as to construct an interharmonic influence factor; construct an influence analysis topological network by using the broadband harmonic influence factor, the interharmonic influence factor, the connection strength between each broadband harmonic influence factor, and the connection strength between each interharmonic influence factor; after broadband harmonic characteristic information and interharmonic characteristic information are input into the influence analysis topological network, the first error compensation parameter is obtained by using the electric energy metering error calculation output by the influence analysis topological network; The second error compensation parameter generating module is used to establish a non-sinusoidal signal simulation model by using the preprocessed power grid topology structure; based on the non-sinusoidal signal simulation model, the preprocessed power grid voltage and power grid current are combined with the step wave signal to obtain a non-sinusoidal wave signal; and the error compensation calculation of the non-sinusoidal wave signal is performed to obtain a second error compensation parameter; The electric energy meter time-varying error analysis module is used to construct an operation trajectory matrix using the historical operation data of the electric energy meter, and to perform the electric energy meter time-varying error analysis under the new energy grid-connected working condition based on the operation trajectory matrix to obtain the electric energy meter time-varying error data; The electric energy metering module is used to calculate the target error compensation parameter based on the first error compensation parameter, the second error compensation parameter and the time-varying error data of the electric energy meter. The electric energy meter performs electric energy metering based on the target error compensation parameter to obtain an electric energy metering result.

14. A terminal comprising a processor and a storage medium; characterized in that: The storage medium is used to store instructions; The processor is configured to operate according to the instructions to execute the steps of the method according to any one of claims 1 to 12.

15. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the steps of the method according to any one of claims 1 to 12 are implemented.

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