Metering deviation determination method and device

Abstract

The invention provides a metering deviation determination method and device, and relates to the technical field of electric energy metering monitoring. The method comprises the following steps: firstly, acquiring a plurality of load feeder current waveforms and a plurality of power feeder current waveforms; frequency characteristic analysis is carried out on the plurality of load feeder current waveforms and the plurality of power feeder current waveforms to obtain a plurality of first characteristic vectors; constructing a relation vector through the first feature vector of the load feeder and the first feature vectors of the plurality of power supply feeders; finally, the metering deviation of each load feeder is determined according to each relation vector, the multiple first feature vectors, the multiple power supply feeder voltages and the main power supply voltage, the frequency vector is extracted based on the current waveform, then the power supply relation between the load feeder and the power supply feeder is analyzed based on the frequency vector, and the metering deviation of each load feeder is determined from the power supply relation. The metering deviation determination method is based on online data analysis determination, is good in timeliness, does not need to be disassembled and inspected, and is high in efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of electric energy metering and monitoring, and in particular to a method and device for determining metering deviation. Background Art

[0002] The electric energy metering device is a metering instrument used to measure and record the amount of power generated, the amount of power supplied (inter-supply), the amount of power consumed by the plant, the amount of power lost and the amount of power consumed by the user. The electric energy metering device refers to the whole composed of electric energy meters (active and reactive electric energy meters, maximum demand meters, multi-rate electric energy meters, etc.), metering transformers (including voltage transformers and current transformers) and secondary connecting wires.

[0003] Affected by manufacturing and external factors, the electric energy metering device will have a certain degree of deviation after a certain period of use. One way to discover and correct this deviation is to conduct regular calibration. At present, the calibration of electric energy metering devices mainly involves removing them from the use environment and calibrating the metering device under predetermined calibration conditions.

[0004] This method requires more manpower and material resources, and because it is a periodic calibration, it cannot detect deviations in the electric energy metering device in a timely manner.

[0005] Based on this, a method for determining metrological deviation needs to be developed. Summary of the invention

[0006] The embodiments of the present invention provide a method and device for determining metering deviation, so as to solve the problem that the deviation generated by the electric energy metering device cannot be discovered in a timely manner.

[0007] In a first aspect, an embodiment of the present invention provides a method for determining a measurement deviation, comprising:

[0008] Acquire multiple load feeder current waveforms and multiple power feeder current waveforms, wherein the load feeders and the power feeders respectively obtain electric energy from a main power source and generate electricity in parallel with the main power source;

[0009] Performing frequency characteristic analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple first characteristic vectors, wherein each first characteristic vector corresponds to a load feeder current waveform or a power feeder current waveform;

[0010] Constructing a relationship vector through the first eigenvector of the load feeder and the first eigenvectors of the plurality of power feeders, wherein the relationship vector represents the power supply relationship between the load feeder and the plurality of power feeders;

[0011] A metering deviation for each load feeder is determined based on each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages, and the main power voltage.

[0012] In a possible implementation, the performing frequency characteristic analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple first characteristic vectors includes:

[0013] Obtain the main power supply voltage waveform;

[0014] Extracting the frequency and phase of the main power supply voltage waveform as the target frequency and target phase;

[0015] Extracting frequency characteristics of each of the multiple load feeder current waveforms and the multiple power feeder current waveforms multiple times according to the target frequency and the target phase, to obtain multiple frequency characteristics;

[0016] A plurality of frequency characteristics of each load feeder or each power feeder are constructed as a first characteristic vector, thereby obtaining the plurality of first characteristic vectors.

[0017] In a possible implementation, extracting the frequency and phase of the main power supply voltage waveform as a target frequency and a target phase includes:

[0018] Obtaining a first preset quantity;

[0019] Discretizing the main power supply voltage waveform to obtain a first waveform data set;

[0020] Taking out the first preset amount of data from the starting position of the first waveform data set to construct a second data set;

[0021] The first preset number of data is taken out from the offset position of the first waveform data set to construct a third data set, wherein the offset position is a position one quarter of the first preset number of positions backward from the starting position;

[0022] Calculating a dot product result of the second data set and the third data set as a cross product result;

[0023] If the cross product result is greater than a first threshold, the first preset number is adjusted, and the process jumps to the step of taking out the first preset number of data from the start position of the first waveform data set to construct a second data set;

[0024] Determining the target frequency according to the discrete time length corresponding to the first preset amount of data;

[0025] The target phase is determined according to a first formula, the target frequency and the second data set, wherein the first formula is:

[0026]

[0027] Wherein, α is the target phase, ds2(n) is the nth data of the second data set, sin() is the sine function, π is the pi, nmax is the total number of data in the second data set, and arccos() is the inverse cosine function.

[0028] In a possible implementation, the extracting frequency features of each of the multiple load feeder current waveforms and the multiple power feeder current waveforms multiple times according to the target frequency and the target phase to obtain multiple frequency features includes:

[0029] Discretizing the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple second waveform data sets, wherein each second waveform data set corresponds to a load feeder current waveform or a power feeder current waveform;

[0030] Frequency features are extracted multiple times for each of the multiple second waveform data sets according to the second formula, the target frequency, and the target phase to obtain multiple frequency features, wherein the second formula is:

[0031]

[0032] Wherein, FA(2ck) is the 2ckth frequency feature, wnmax is the total number of data in the first waveform data set, WD(wn) is the wnth data in the second waveform data set, cos() is the cosine function, sin() is the sine function, ω0 is the target frequency, α is the target phase, TN is the number of discrete data in the period corresponding to the target frequency, and π is the pi.

[0033] In a possible implementation, the step of constructing a relationship vector by using the first eigenvector of the load feeder and the first eigenvectors of the plurality of power feeders includes:

[0034] For each load feeder, perform the following steps:

[0035] Sequentially taking out first eigenvectors from the first eigenvectors of the plurality of power feeders as eigenvectors to be processed;

[0036] Determining a power supply relationship coefficient according to the first eigenvector of the load feeder and the eigenvector to be processed;

[0037] Adding the power supply relationship coefficient as an element of a vector to the relationship vector;

[0038] If the traversal of the multiple power feeders is not completed, the process jumps to the step of sequentially extracting the first feature vectors from the first feature vectors of the multiple power feeders as feature vectors to be processed.

[0039] In a possible implementation, determining the power supply relationship coefficient according to the first eigenvector of the load feeder and the eigenvector to be processed includes:

[0040] The power supply relationship coefficient is determined according to the third formula, the first eigenvector of the load feeder and the eigenvector to be processed, wherein the third formula is:

[0041]

[0042] Where FA1(ck) is the ckth element of the first eigenvector of the load feeder, FA2(ck) is the ckth element of the eigenvector to be processed, ckmax is the total number of elements of the first eigenvector, and R is the power supply relationship coefficient.

[0043] In a possible implementation, determining the metering deviation of each load feeder according to each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages, and the main power supply voltage includes:

[0044] For each load feeder, perform the following steps:

[0045] Inputting the relationship vector of the load feeder, the multiple first eigenvectors, the multiple power feeder voltages and the main power supply voltage into a load feeder voltage determination model to obtain a load feeder voltage effective value indication value;

[0046] Determining a metering indication value of the load feeder according to the load feeder voltage effective value indication value and the load feeder current waveform;

[0047] A metering deviation of the load feeder is determined according to the metering indication value of the load feeder and the metering value of the load feeder.

[0048] In a possible implementation, the process of constructing the load feeder voltage determination model includes:

[0049] Get and initialize the coefficient set;

[0050] Acquire multiple independent variable data sample sets and multiple load feeder voltage samples, wherein each independent variable data sample set corresponds to a load feeder voltage sample, and the independent variable data sample set includes: load feeder relationship vector sample data, multiple first feature vector sample data, multiple power feeder voltage sample data, and main power supply voltage sample data;

[0051] Substituting the coefficient set into the load feeder voltage basic model;

[0052] Substituting the data of the plurality of independent variable data sample sets into a load feeder voltage basic model to obtain a plurality of outputs of the load feeder voltage basic model;

[0053] determining a model deviation according to a plurality of outputs of the load feeder voltage base model and the plurality of load feeder voltage samples;

[0054] If the model deviation is greater than a threshold, an optimization algorithm is used to optimize the coefficients of the coefficient set;

[0055] Otherwise, the load feeder voltage basic model is used as the load feeder voltage determination model.

[0056] In a possible implementation, the load feeder voltage basic model is:

[0057]

[0058] Where U SET is the load feeder voltage, w IDX is the first coefficient of the IDXth order, w R is the Rth second coefficient, w fn_ck is the fn×ck third coefficient, w fn is the fourth coefficient of the fnth order, w m is the main power coefficient, X is the joint independent variable, C is the bias coefficient, IDXmax is the total number of first coefficients, ckmax is the total number of first eigenvector elements, fnmax is the total number of power feeders, Rnmax is the total number of relationship vector elements, R(Rn) is the Rnth element of the relationship vector, FA fn (ck) is the ckth element of the fnth first eigenvector, U fn is the voltage of the fnth power feeder, U m Main supply voltage.

[0059] In a second aspect, an embodiment of the present invention provides a device for determining a measurement deviation, comprising:

[0060] A waveform acquisition module, used to acquire a plurality of load feeder current waveforms and a plurality of power feeder current waveforms, wherein the load feeders and the power feeders respectively acquire electric energy from a main power source and generate electricity in parallel with the main power source;

[0061] A feature extraction module, used to perform frequency feature analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple first feature vectors, wherein each first feature vector corresponds to a load feeder current waveform or a power feeder current waveform;

[0062] A relationship building module, configured to build a relationship vector through a first characteristic vector of the load feeder and first characteristic vectors of the plurality of power feeders, wherein the relationship vector represents a power supply relationship between the load feeder and the plurality of power feeders;

[0063] as well as,

[0064] The deviation determination module is used to determine the metering deviation of each load feeder according to each relationship vector, the multiple first eigenvectors, the multiple power feeder voltages and the main power supply voltage.

[0065] The beneficial effects of the embodiments of the present invention compared with the prior art are as follows:

[0066] The present invention provides a method and device for determining metering deviation, which first obtains multiple load feeder current waveforms and multiple power feeder current waveforms, wherein the load feeder and the power feeder respectively obtain electric energy from a main power supply and generate electricity in grid connection with the main power supply; then, the multiple load feeder current waveforms and the multiple power feeder current waveforms are respectively subjected to frequency characteristic analysis to obtain multiple first characteristic vectors, wherein each first characteristic vector corresponds to a load feeder current waveform or a power feeder current waveform; then, a relationship vector is constructed through the first characteristic vector of the load feeder and the first characteristic vector of the multiple power feeders, wherein the relationship vector represents the power supply relationship between the load feeder and the multiple power feeders; finally, the metering deviation of each load feeder is determined according to each relationship vector, the multiple first characteristic vectors, the multiple power feeder voltages and the main power supply voltage. In an embodiment of the present invention, a frequency vector is extracted based on the current waveform, and then the power supply relationship between the load feeder and the power feeder is analyzed based on the frequency vector, and the metering deviation is determined based on the power supply relationship. The metering deviation determination method of the present invention is determined based on online data analysis, has good timeliness, does not require disassembly and inspection, and has high efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0068] Figure 1 is a flow chart of the implementation of the method for determining the measurement deviation provided by the embodiment of the present invention;

[0069] Figure 2 It is an application scenario diagram of the method for determining measurement deviation provided by an embodiment of the present invention;

[0070] Figure 3It is a structural schematic diagram of a measurement deviation determination device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In the following description, specific details such as specific system structures and techniques are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0072] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0073] Figure 1 The implementation flow chart of the method for determining the measurement deviation provided by the embodiment of the present invention is described in detail as follows:

[0074] In step 101, a plurality of load feeder current waveforms and a plurality of power feeder current waveforms are obtained, wherein the load feeders and the power feeders obtain electric energy from a main power source and generate electricity in parallel with the main power source.

[0075] For example, Figure 2 This is an application scenario diagram of the method for determining measurement deviation provided by the embodiment of the present invention. Figure 2 As shown, the main power source 201 provides power to the loads 204 on the multiple load feeders 203 through the bus 202. At the same time, multiple distributed power sources 206 are connected to the bus 202 through the power feeder 205 to generate power in parallel with the main power source 201, and together with the main power source 201, they provide power to the multiple loads 204 in the power grid. As can be seen from the figure, since there are multiple distributed power sources 206 connected to the power grid, the voltage of the load feeder 203 and the power flow of each feeder become more complicated, which brings difficulties to the determination of the metering deviation.

[0076] The implementation mode of the present invention analyzes and determines the voltage of the load feeder node from the aspects of frequency analysis and power supply relationship, and can determine the metering estimate value in combination with the current waveform. The deviation between the estimate value and the metering value of the metering device is the metering deviation.

[0077] To achieve the above objectives, the embodiments of the present invention obtain the current waveform of each load feeder and the current waveform of each power feeder, and these waveforms are synchronized, in other words, these waveforms start at the same start time and end at the same end time.

[0078] Step 102: Perform frequency characteristic analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms to obtain multiple first characteristic vectors, wherein each first characteristic vector corresponds to a load feeder current waveform or a power feeder current waveform.

[0079] In some implementations, performing frequency characteristic analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms to obtain multiple first characteristic vectors includes:

[0080] Obtain the main power supply voltage waveform;

[0081] Extracting the frequency and phase of the main power supply voltage waveform as the target frequency and target phase;

[0082] Extracting frequency characteristics of each of the multiple load feeder current waveforms and the multiple power feeder current waveforms multiple times according to the target frequency and the target phase, to obtain multiple frequency characteristics;

[0083] A plurality of frequency characteristics of each load feeder or each power feeder are constructed as a first characteristic vector, thereby obtaining the plurality of first characteristic vectors.

[0084] In some implementations, extracting the frequency and phase of the main power voltage waveform as a target frequency and a target phase includes:

[0085] Obtaining a first preset quantity;

[0086] Discretizing the main power supply voltage waveform to obtain a first waveform data set;

[0087] Taking out the first preset amount of data from the starting position of the first waveform data set to construct a second data set;

[0088] The first preset number of data is taken out from the offset position of the first waveform data set to construct a third data set, wherein the offset position is a position one quarter of the first preset number of positions backward from the starting position;

[0089] Calculating a dot product result of the second data set and the third data set as a cross product result;

[0090] If the cross product result is greater than a first threshold, the first preset number is adjusted, and the process jumps to the step of taking out the first preset number of data from the start position of the first waveform data set to construct a second data set;

[0091] Determining the target frequency according to the discrete time length corresponding to the first preset amount of data;

[0092] The target phase is determined according to a first formula, the target frequency and the second data set, wherein the first formula is:

[0093]

[0094] Wherein, α is the target phase, ds2(n) is the nth data of the second data set, sin() is the sine function, π is the pi, nmax is the total number of data in the second data set, and arccos() is the inverse cosine function.

[0095] In some embodiments, the extracting frequency features of each of the multiple load feeder current waveforms and the multiple power feeder current waveforms multiple times according to the target frequency and the target phase to obtain multiple frequency features includes:

[0096] Discretizing the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple second waveform data sets, wherein each second waveform data set corresponds to a load feeder current waveform or a power feeder current waveform;

[0097] Frequency features are extracted multiple times for each of the multiple second waveform data sets according to the second formula, the target frequency, and the target phase to obtain multiple frequency features, wherein the second formula is:

[0098]

[0099] Wherein, FA(2ck) is the 2ckth frequency feature, wnmax is the total number of data in the first waveform data set, WD(wn) is the wnth data in the second waveform data set, cos() is the cosine function, sin() is the sine function, ω0 is the target frequency, α is the target phase, TN is the number of discrete data in the period corresponding to the target frequency, and π is the pi.

[0100] Exemplarily, the frequency characteristics of the present invention are extracted by referring to the main power supply voltage waveform. Specifically, the frequency and phase of the main power supply voltage are determined, and then the frequency characteristics of the current waveform are extracted based on the frequency and phase of the main power supply voltage.

[0101] In terms of determining the frequency and phase of the main power supply voltage, the embodiment of the present invention discretizes the main power supply voltage waveform (synchronous with the above-mentioned current waveform) to obtain a first waveform data set, extracts a predetermined number of data from the starting position of this data set, and performs a predetermined offset (the offset is one-fourth of the predetermined number of data), extracts the predetermined number of data again, and performs a dot multiplication operation on the data obtained twice. If the dot product obtained is large, it means that the predetermined number of data differs greatly from the period of the main power supply voltage. After adjusting the predetermined amount, the above operation is repeated until the dot product is less than the threshold. At this time, the bus voltage frequency is determined according to the duration of the predetermined amount of data across the domain, and then the phase of the bus voltage is extracted using the first formula:

[0102]

[0103] Wherein, α is the target phase, ds2(n) is the nth data of the second data set, sin() is the sine function, π is the pi, nmax is the total number of data in the second data set, and arccos() is the inverse cosine function.

[0104] After obtaining the frequency and phase of the bus voltage, the frequency characteristics of each current waveform can be extracted. Similarly, the embodiment of the present invention also extracts the frequency characteristics after discretizing the current waveform, and the application formula is expressed as:

[0105]

[0106] Wherein, FA(2ck) is the 2ckth frequency feature, wnmax is the total number of data in the first waveform data set, WD(wn) is the wnth data in the second waveform data set, cos() is the cosine function, sin() is the sine function, ω0 is the target frequency, α is the target phase, TN is the number of discrete data in the period corresponding to the target frequency, and π is the pi.

[0107] Step 103: construct a relationship vector through the first eigenvector of the load feeder and the first eigenvectors of the plurality of power feeders, wherein the relationship vector represents the power supply relationship between the load feeder and the plurality of power feeders.

[0108] In some embodiments, constructing a relationship vector by using the first eigenvector of the load feeder and the first eigenvectors of the plurality of power feeders includes:

[0109] For each load feeder, perform the following steps:

[0110] Sequentially taking out first eigenvectors from the first eigenvectors of the plurality of power feeders as eigenvectors to be processed;

[0111] Determining a power supply relationship coefficient according to the first eigenvector of the load feeder and the eigenvector to be processed;

[0112] Adding the power supply relationship coefficient as an element of a vector to the relationship vector;

[0113] If the traversal of the multiple power feeders is not completed, the process jumps to the step of sequentially extracting the first feature vectors from the first feature vectors of the multiple power feeders as feature vectors to be processed.

[0114] In some implementations, determining the power supply relationship coefficient according to the first eigenvector of the load feeder and the eigenvector to be processed includes:

[0115] The power supply relationship coefficient is determined according to the third formula, the first eigenvector of the load feeder and the eigenvector to be processed, wherein the third formula is:

[0116]

[0117] Where FA1(ck) is the ckth element of the first eigenvector of the load feeder, FA2(ck) is the ckth element of the eigenvector to be processed, ckmax is the total number of elements of the first eigenvector, and R is the power supply relationship coefficient.

[0118] Exemplarily, in terms of power supply relationship analysis, since the analysis is performed on the load feeder, in terms of relationship vectors, the embodiment of the present invention extracts the power supply relationship with each power feeder for the load feeder, and constructs these power supply relationships as relationship vectors. Specifically, the embodiment of the present invention uses the third formula to extract the relationship coefficient between the load feeder and the power feeder segment:

[0119]

[0120] Where FA1(ck) is the ckth element of the first eigenvector of the load feeder, FA2(ck) is the ckth element of the eigenvector to be processed, ckmax is the total number of elements of the first eigenvector, and R is the power supply relationship coefficient.

[0121] After the extraction is completed, the relationship vector is obtained by arranging them in a predetermined order.

[0122] Step 104: determining a metering deviation of each load feeder according to each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages, and the main power voltage.

[0123] In some embodiments, determining the metering deviation of each load feeder according to each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages, and the main power supply voltage comprises:

[0124] For each load feeder, perform the following steps:

[0125] Inputting the relationship vector of the load feeder, the multiple first eigenvectors, the multiple power feeder voltages and the main power supply voltage into a load feeder voltage determination model to obtain a load feeder voltage effective value indication value;

[0126] Determining a metering indication value of the load feeder according to the load feeder voltage effective value indication value and the load feeder current waveform;

[0127] A metering deviation of the load feeder is determined according to the metering indication value of the load feeder and the metering value of the load feeder.

[0128] In some implementations, the process of constructing the load feeder voltage determination model includes:

[0129] Get and initialize the coefficient set;

[0130] Acquire multiple independent variable data sample sets and multiple load feeder voltage samples, wherein each independent variable data sample set corresponds to a load feeder voltage sample, and the independent variable data sample set includes: load feeder relationship vector sample data, multiple first feature vector sample data, multiple power feeder voltage sample data, and main power supply voltage sample data;

[0131] Substituting the coefficient set into the load feeder voltage basic model;

[0132] Substituting the data of the plurality of independent variable data sample sets into a load feeder voltage basic model to obtain a plurality of outputs of the load feeder voltage basic model;

[0133] determining a model deviation according to a plurality of outputs of the load feeder voltage base model and the plurality of load feeder voltage samples;

[0134] If the model deviation is greater than a threshold, an optimization algorithm is used to optimize the coefficients of the coefficient set;

[0135] Otherwise, the load feeder voltage basic model is used as the load feeder voltage determination model.

[0136] In some implementations, the load feeder voltage basic model is:

[0137]

[0138] Where U SET is the load feeder voltage, w IDX is the first coefficient of the IDXth order, w R is the Rth second coefficient, wfn_ck is the fn×ck third coefficient, w fn is the fourth coefficient of the fnth order, w m is the main power coefficient, X is the joint independent variable, C is the bias coefficient, IDXmax is the total number of first coefficients, ckmax is the total number of first eigenvector elements, fnmax is the total number of power feeders, Rnmax is the total number of relationship vector elements, R(Rn) is the Rnth element of the relationship vector, FA fn (ck) is the ckth element of the fnth first eigenvector, U fn is the voltage of the fnth power feeder, U m Main supply voltage.

[0139] Exemplarily, an implementation mode of the present invention is to input the relationship vector of the load feeder, multiple first eigenvectors, multiple power feeder voltages and the main power supply voltage into the load feeder voltage determination model to obtain the indication value of the effective value of the load feeder voltage, and then extract the active current through the current waveform of the load feeder to obtain the active current metering indication value, and extract the reactive current through the current waveform of the load feeder to obtain the reactive current metering indication value, and these indication values ​​are then compared with the metering value of the metering device to determine the deviation of the metering device.

[0140] The load feeder voltage determination model is constructed based on the load feeder voltage basic model. The load feeder voltage basic model in one application scenario is:

[0141]

[0142] Where U SET is the load feeder voltage, w IDX is the first coefficient of the IDXth order, w R is the Rth second coefficient, w fn_ck is the fn×ck third coefficient, w fn is the fourth coefficient of the fnth order, w m is the main power coefficient, X is the joint independent variable, C is the bias coefficient, IDXmax is the total number of first coefficients, ckmax is the total number of first eigenvector elements, fnmax is the total number of power feeders, Rnmax is the total number of relationship vector elements, R(Rn) is the Rnth element of the relationship vector, FA fn (ck) is the ckth element of the fnth first eigenvector, U fn is the voltage of the fnth power feeder, U m Main supply voltage.

[0143] There are multiple parameters to be determined in this basic model. In terms of parameter determination, the implementation mode of the present invention is determined based on some sample sets. Specifically, each sample set corresponds to a load feeder voltage sample, and the independent variable data sample set includes: relationship vector sample data of the load feeder, multiple first eigenvector sample data, multiple power feeder voltage sample data and main power supply voltage sample data.

[0144] After the sample set is input into the basic model, the output of the basic model will deviate from the load feeder voltage sample. According to these deviations, the coefficients of the basic model are modified by using an optimization algorithm, such as a particle swarm algorithm, a gray wolf algorithm, etc. After several iterations, the basic model can more accurately fit the relationship between the independent variables (the relationship vector of the load feeder, multiple first eigenvectors, multiple power feeder voltages, and the main power supply voltage) and the dependent variable (load feeder voltage). At this time, the basic model can be used as a load feeder voltage determination model.

[0145] The embodiment of the present invention first obtains multiple load feeder current waveforms and multiple power feeder current waveforms, wherein the load feeder and the power feeder respectively obtain electric energy from a main power supply and generate electricity in grid connection with the main power supply; then the multiple load feeder current waveforms and the multiple power feeder current waveforms are respectively subjected to frequency characteristic analysis to obtain multiple first characteristic vectors, wherein each first characteristic vector corresponds to a load feeder current waveform or a power feeder current waveform; then a relationship vector is constructed through the first characteristic vector of the load feeder and the first characteristic vector of the multiple power feeders, wherein the relationship vector characterizes the power supply relationship between the load feeder and the multiple power feeders; finally, the metering deviation of each load feeder is determined according to each relationship vector, the multiple first characteristic vectors, the multiple power feeder voltages and the main power supply voltage. The embodiment of the present invention extracts a frequency vector based on the current waveform, and then analyzes the power supply relationship between the load feeder and the power feeder based on the frequency vector, and determines the metering deviation based on the power supply relationship. The metering deviation determination method of the present invention is determined based on online data analysis, has good timeliness, does not require disassembly and inspection, and has high efficiency.

[0146] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0147] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0148] Figure 3The schematic diagram of the structure of the measurement deviation determination device provided by the embodiment of the present invention is shown. For the convenience of explanation, only the part related to the embodiment of the present invention is shown, which is described in detail as follows:

[0149] like Figure 3 As shown, the measurement deviation determination device 3 includes: a waveform acquisition module 301, a feature extraction module 302, a relationship construction module 303 and a deviation determination module 304, wherein:

[0150] The waveform acquisition module 301 is used to acquire a plurality of load feeder current waveforms and a plurality of power feeder current waveforms, wherein the load feeders and the power feeders respectively acquire electric energy from a main power source and generate electricity in parallel with the main power source;

[0151] A feature extraction module 302 is used to perform frequency feature analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms to obtain multiple first feature vectors, wherein each first feature vector corresponds to a load feeder current waveform or a power feeder current waveform;

[0152] A relationship building module 303 is used to build a relationship vector through the first characteristic vector of the load feeder and the first characteristic vectors of the plurality of power feeders, wherein the relationship vector represents the power supply relationship between the load feeder and the plurality of power feeders;

[0153] The deviation determination module 304 is used to determine the metering deviation of each load feeder according to each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages and the main power supply voltage.

[0154] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0155] Those skilled in the art will appreciate that the templates, units, and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0156] If the module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, it can implement the steps of the above-mentioned various metrology deviation determination method implementation methods. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory, random access memory, electrical carrier signal, telecommunication signal and software distribution medium.

[0157] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A method for determining a measurement deviation, characterized in that: include: Acquire multiple load feeder current waveforms and multiple power feeder current waveforms, wherein the load feeders and the power feeders respectively obtain electric energy from a main power source and generate electricity in parallel with the main power source; Performing frequency characteristic analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple first characteristic vectors, wherein each first characteristic vector corresponds to a load feeder current waveform or a power feeder current waveform; Constructing a relationship vector through the first eigenvector of the load feeder and the first eigenvectors of the plurality of power feeders, wherein the relationship vector represents the power supply relationship between the load feeder and the plurality of power feeders; A metering deviation for each load feeder is determined based on each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages, and the main power voltage.

2. The method for determining measurement deviation according to claim 1, characterized in that: The performing frequency characteristic analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple first characteristic vectors includes: Obtain the main power supply voltage waveform; Extracting the frequency and phase of the main power supply voltage waveform as the target frequency and target phase; Extracting frequency characteristics of each of the multiple load feeder current waveforms and the multiple power feeder current waveforms multiple times according to the target frequency and the target phase, to obtain multiple frequency characteristics; A plurality of frequency characteristics of each load feeder or each power feeder are constructed as a first characteristic vector, thereby obtaining the plurality of first characteristic vectors.

3. The method for determining measurement deviation according to claim 2, characterized in that: The extracting the frequency and phase of the main power supply voltage waveform as the target frequency and the target phase comprises: Obtaining a first preset quantity; Discretizing the main power supply voltage waveform to obtain a first waveform data set; Taking out the first preset amount of data from the starting position of the first waveform data set to construct a second data set; The first preset number of data is taken out from the offset position of the first waveform data set to construct a third data set, wherein the offset position is a position one quarter of the first preset number of positions backward from the starting position; Calculating a dot product result of the second data set and the third data set as a cross product result; If the cross product result is greater than a first threshold, the first preset number is adjusted, and the process jumps to the step of taking out the first preset number of data from the start position of the first waveform data set to construct a second data set; Determining the target frequency according to the discrete time length corresponding to the first preset amount of data; The target phase is determined according to a first formula, the target frequency and the second data set, wherein the first formula is: Wherein, α is the target phase, ds2(n) is the nth data of the second data set, sin( ) is the sine function, π is the pi, nmax is the total number of data of the second data set, and arccos( ) is the inverse cosine function.

4. The method for determining measurement deviation according to claim 2, characterized in that: The extracting frequency characteristics of each of the multiple load feeder current waveforms and the multiple power feeder current waveforms multiple times according to the target frequency and the target phase to obtain multiple frequency characteristics includes: Discretizing the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple second waveform data sets, wherein each second waveform data set corresponds to a load feeder current waveform or a power feeder current waveform; Frequency features are extracted multiple times for each of the multiple second waveform data sets according to the second formula, the target frequency, and the target phase to obtain multiple frequency features, wherein the second formula is: Wherein, FA(2ck) is the 2ckth frequency feature, wnmax is the total number of data in the first waveform data set, WD(wn) is the wnth data in the second waveform data set, cos() is the cosine function, sin() is the sine function, ω0 is the target frequency, α is the target phase, TN is the number of discrete data in the period corresponding to the target frequency, and π is the pi.

5. The method for determining measurement deviation according to claim 1, characterized in that: The step of constructing a relationship vector by using the first eigenvector of the load feeder and the first eigenvectors of the plurality of power feeders includes: For each load feeder, perform the following steps: Sequentially taking out first eigenvectors from the first eigenvectors of the plurality of power feeders as eigenvectors to be processed; Determining a power supply relationship coefficient according to the first eigenvector of the load feeder and the eigenvector to be processed; Adding the power supply relationship coefficient as an element of a vector to the relationship vector; If the traversal of the multiple power feeders is not completed, the process jumps to the step of sequentially extracting the first feature vectors from the first feature vectors of the multiple power feeders as feature vectors to be processed.

6. The method for determining measurement deviation according to claim 5, characterized in that: The step of determining the power supply relationship coefficient according to the first eigenvector of the load feeder and the eigenvector to be processed includes: The power supply relationship coefficient is determined according to the third formula, the first eigenvector of the load feeder and the eigenvector to be processed, wherein the third formula is: Where FA1(ck) is the ckth element of the first eigenvector of the load feeder, FA2(ck) is the ckth element of the eigenvector to be processed, cjmax is the total number of elements of the first eigenvector, and R is the power supply relationship coefficient.

7. The method for determining measurement deviation according to any one of claims 1 to 6, characterized in that: Determining the metering deviation of each load feeder according to each relationship vector, the plurality of first eigenvectors, the plurality of power feeder voltages, and the main power supply voltage comprises: For each load feeder, perform the following steps: Inputting the relationship vector of the load feeder, the multiple first eigenvectors, the multiple power feeder voltages and the main power supply voltage into a load feeder voltage determination model to obtain a load feeder voltage effective value indication value; Determining a metering indication value of the load feeder according to the load feeder voltage effective value indication value and the load feeder current waveform; A metering deviation of the load feeder is determined according to the metering indication value of the load feeder and the metering value of the load feeder.

8. The method for determining measurement deviation according to claim 7, characterized in that: The construction process of the load feeder voltage determination model includes: Get and initialize the coefficient set; Acquire multiple independent variable data sample sets and multiple load feeder voltage samples, wherein each independent variable data sample set corresponds to a load feeder voltage sample, and the independent variable data sample set includes: load feeder relationship vector sample data, multiple first feature vector sample data, multiple power feeder voltage sample data, and main power supply voltage sample data; Substituting the coefficient set into the load feeder voltage basic model; Substituting the data of the plurality of independent variable data sample sets into a load feeder voltage basic model to obtain a plurality of outputs of the load feeder voltage basic model; determining a model deviation according to a plurality of outputs of the load feeder voltage base model and the plurality of load feeder voltage samples; If the model deviation is greater than a threshold, an optimization algorithm is used to optimize the coefficients of the coefficient set; Otherwise, the load feeder voltage basic model is used as the load feeder voltage determination model.

9. The method for determining measurement deviation according to claim 8, characterized in that: The load feeder voltage basic model is: Where U SET is the load feeder voltage, w IDX is the first coefficient of the IDXth order, w R is the Rth second coefficient, w fn_ck is the fn×ck third coefficient, w fn is the fourth coefficient of the fnth order, w m is the main power coefficient, X is the joint independent variable, C is the bias coefficient, IDXmax is the total number of first coefficients, ckmax is the total number of first eigenvector elements, fnmax is the total number of power feeders, Rnmax is the total number of relationship vector elements, R(Rn) is the Rnth element of the relationship vector, FA fn (ck) is the ckth element of the fnth first eigenvector, U fn is the voltage of the fnth power feeder, U m Main supply voltage.

10. A device for determining a measurement deviation, characterized in that: Used to implement the method for determining a measurement deviation according to any one of claims 1 to 9, the measurement deviation determination device comprises: A waveform acquisition module, used to acquire a plurality of load feeder current waveforms and a plurality of power feeder current waveforms, wherein the load feeders and the power feeders respectively acquire electric energy from a main power source and generate electricity in parallel with the main power source; A feature extraction module, used to perform frequency feature analysis on the multiple load feeder current waveforms and the multiple power feeder current waveforms respectively to obtain multiple first feature vectors, wherein each first feature vector corresponds to a load feeder current waveform or a power feeder current waveform; A relationship building module, configured to build a relationship vector through a first characteristic vector of the load feeder and first characteristic vectors of the plurality of power feeders, wherein the relationship vector represents a power supply relationship between the load feeder and the plurality of power feeders; as well as, The deviation determination module is used to determine the metering deviation of each load feeder according to each relationship vector, the multiple first eigenvectors, the multiple power feeder voltages and the main power supply voltage.

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