A current transformer error solving method, system, device and medium

Abstract

The application discloses the technical field of electric power measurement, and discloses a current transformer error solving method, system, device and medium, which comprises the following steps: collecting operation data of a current transformer, constructing a sample analysis data set, and performing component decomposition and feature extraction on the sample data set, and then calculating a statistical threshold value; determining the condition of the current transformer, and constructing a same-phase current transformer sample; based on the calculation step of the statistical threshold value, calculating the statistical value of each sample in the same-phase current transformer sample, comparing the statistical value with the statistical threshold value, obtaining an analysis result of the state of the current transformer, and then obtaining a related solving threshold value; initializing a particle swarm through a multi-target weighted fitness function model and an improved Logistic mapping; based on the fitness of each particle, combining a grey wolf algorithm, a particle swarm optimization algorithm and a Levy flight disturbance mechanism to cooperatively optimize the particle swarm, and iteratively solving the position of each particle; and based on a double-threshold mechanism, determining whether the current transformer indeed has a problem.

Description

Technical Field

[0001] This invention relates to the technical field of power measurement, and in particular to a method, system, device, and medium for solving the error of a current transformer. Background Technology

[0002] In modern power systems, current transformers (CTs) are key equipment for ensuring reliable system operation and accurate metering. Their measurement accuracy plays a crucial role in the stability and efficiency of the power system. CTs are mainly used to proportionally transform alternating current, providing accurate current signals to measuring instruments, relay protection devices, and other applications. However, due to various factors such as manufacturing processes, operating environment (e.g., temperature, humidity, electromagnetic interference), and equipment aging caused by long-term use, CTs inevitably produce errors in ratio and phase during actual operation.

[0003] These errors may seem minor, but they can trigger a series of serious problems. In electricity metering, CT errors can directly lead to inaccurate electricity measurement, causing economic disputes between power companies and users. In the field of relay protection, inaccurate current measurements may cause relay protection devices to malfunction or fail to operate, threatening the safe and stable operation of the power system, and even causing large-scale power outages, resulting in huge losses to social production and people's lives.

[0004] Traditional CT error calculation methods are mainly based on analytical algorithms and empirical formulas, which have significant limitations. On the one hand, analytical algorithms often rely on simplified model assumptions, making it difficult to accurately reflect the complex actual operating characteristics of CT scanners, resulting in large deviations between calculated results and actual errors. On the other hand, empirical formulas are usually derived under specific conditions, lacking universality and poor adaptability to different types of CT scanners and operating environments. Furthermore, traditional methods are generally prone to getting trapped in local optima, failing to find a globally optimal solution when faced with complex error calculation scenarios, thus severely limiting the improvement of CT error calculation accuracy.

[0005] With the continuous development and intelligent upgrading of power systems, the requirements for CT measurement accuracy are increasing, and existing error solving methods are no longer sufficient to meet practical needs. Therefore, there is an urgent need for an innovative, efficient, and accurate CT error solving method to address the shortcomings of traditional methods and ensure the safe, reliable operation and accurate metering of power systems.

[0006] Therefore, based on the above-mentioned technical problems, this application proposes a current transformer error calculation method that is accurate in measurement, efficient in operation, and can ensure the stable operation of the power system. Summary of the Invention

[0007] The purpose of this invention is to overcome the shortcomings of the existing technology and provide an innovative, efficient and accurate method for solving the error of current transformers, so as to solve the shortcomings of traditional methods and ensure the safe and reliable operation and accurate metering of the power system.

[0008] To achieve the above objectives, the present invention provides a method for solving the error of a current transformer, comprising the following steps:

[0009] Step S1: Collect the operating data of current transformers on each measuring line under the same node of the substation within the first preset time period to construct a sample analysis dataset. Then, using principal component analysis (PCA), decompose and extract features from the sample analysis dataset to establish an initial steady state, and finally calculate the current transformers in the sample dataset. Statistical threshold;

[0010] Step S2: Collect the original waveform data of the current transformers of each measuring line under the same node of the substation within the second preset time period. Based on the sliding window mean filtering algorithm, filter the original waveform data to obtain the filtered original waveform data. Combined with the fast Fourier transform method, calculate the filtered original waveform data to obtain the real-time status judgment of the current transformers of each measuring line, and then construct the in-phase current transformer sample.

[0011] Step S3: Based on step S1 The steps for calculating the statistical threshold are as follows: calculate the threshold value for each sample in the in-phase current transformer sample. Statistic and each sample Statistics and Initial Steady State Statistical thresholds are compared to obtain preliminary analysis results of the current transformer's state. Then, based on the accuracy level of the current transformer, the thresholds for solving the ratio difference and phase difference in the current transformer are set.

[0012] Step S4: Based on the KCL principle, establish an initial multi-objective weighted fitness function model with the ratio difference and phase difference of each current transformer as the solution object and minimizing the primary current vector sum of the nodes as the optimization objective; after obtaining the initial multi-objective weighted fitness function model, combine the solution thresholds of the ratio difference and phase difference of the current transformers in Step S3 to constrain the initial multi-objective weighted fitness function model and obtain the optimized multi-objective weighted fitness function model;

[0013] Step S5: After obtaining the optimized multi-objective weighted fitness function model, a improved Logistic mapping is introduced to initialize the particle swarm; and based on the fitness of each particle, the particle swarm is optimized in coordination by combining the grey wolf algorithm, the particle swarm optimization algorithm and the Levy flight disturbance mechanism, so as to iteratively update the position of each particle, until a preset stop condition is met, and the solving results of the ratio error and the phase error are obtained, wherein in the iteration process, whether each particle is updated is limited by combining the solving threshold of the ratio error and the phase error of the current transformer in step S3;

[0014] Step S6: Based on the double threshold mechanism, a hypothesis testing model with two judgment thresholds is constructed, the preliminary analysis results in step S3 are taken as the first discriminant object of the hypothesis testing model, and the solving results of the final ratio error and phase error obtained in step S5 are taken as the second discriminant object of the hypothesis testing model, and based on the two judgment thresholds of the hypothesis testing model, the first discriminant object and the second discriminant object are compared and analyzed respectively, wherein if within a specified time period, the hypothesis testing model has 80% of the results showing that both discriminant objects think that the current transformer at this place has an error problem, it is determined that the current transformer at this place indeed has an error problem.

[0015] Further, the step S1 comprises the following steps:

[0016] Step S1.1: Collecting the operation data of the current transformers of the same phase of each branch under the same node of the transformer substation in a first preset time period, to establish a sample analysis data set in the form of a current transformer vector, and based on the kernel function method, a kernel matrix composed of the sample analysis data set is established, wherein the operation data is the amplitude and phase value of the current transformer;

[0017] Step S1.2: Transforming the kernel matrix based on the centering processing method to eliminate the mean shift in the feature space, and obtaining the kernel matrix after centering processing;

[0018] Step S1.3: Feature decomposition is performed on the kernel matrix after centering processing to obtain each eigenvalue and each eigenvector corresponding to each eigenvalue;

[0019] Step S1.4: After obtaining each eigenvalue and each eigenvector corresponding to each eigenvalue, the eigenvalues are sorted in descending order, and based on the calculation principle of the cumulative contribution rate, the first k larger eigenvalues with a cumulative contribution rate greater than or equal to 85% are obtained, and based on the first k larger eigenvalues, a variance contribution diagonal matrix , and a principal component direction matrix composed of the eigenvectors corresponding to the first k larger eigenvalues are constructed;

[0020] Step S1.5: After obtaining the principal component direction matrix Afterwards, the principal component projection vector of each sample in the sample analysis data set is calculated;

[0021] Step S1.6: After obtaining the principal component projection vector of each sample, the sum of squares of the residual vectors is calculated to obtain statistic;

[0022] Step S1.7: After obtaining the set of Q statistics, the 99th percentile of the normal samples in the sample analysis data set is taken as the optimal discrimination threshold by combining the quantile method, or the statistic threshold under the pre-set confidence level is determined by combining the chi-square distribution method.

[0023] Further, the step S2 includes the following steps:

[0024] Step S2.1: The original waveform data of the current transformers of each measuring line at the same node of the transformer substation is collected by a high-precision sensor within a second preset time period, wherein the original waveform data is the secondary side current waveform data of the current transformer;

[0025] Step S2.2: The original current waveform data is filtered based on the sliding window mean filtering algorithm to obtain filtered waveform data;

[0026] Step S2.3: Based on the filtered waveform data, the amplitude and phase values of each harmonic and the total harmonic distortion rate of each current transformer are calculated by combining the fast Fourier transform, and then the same-phase current transformer sample is constructed;

[0027] Step S2.4: When the total harmonic distortion rate of a current transformer exceeds a preset value one or the amplitude of a specific harmonic thereof is greater than a preset value two, it is determined that the current transformer has harmonic interference.

[0028] Further, the step S3 includes the following steps:

[0029] Step S3.1: After obtaining the same-phase current transformer sample, the calculation step of the statistic threshold in step S1 is combined to calculate the statistic of the sample;

[0030] Step S3.2: The statistic of the sample is compared with the statistic threshold in step S1.6, if the statistic exceeds the statistic threshold , it is determined that the current transformer in the transformer substation may have a problem, and the fault separation algorithm is used to locate the current transformer that may have a problem;

[0031] Step S3.3: After obtaining the preliminary analysis result of the current transformer state, the solving threshold of the ratio error and the phase error in the current transformer is divided in combination with the accuracy level of the current transformer, wherein when the current transformer is preliminarily determined as no problem in step S3.2 and the accuracy level corresponding to the current transformer is 0.1S level, the ratio error solving threshold of the current transformer is set as [-0.1%, 0.1%] and the phase error solving threshold is set as [-5', 5'];

[0032] Step S3.4: When the current transformer is preliminarily determined as having a problem in step S3.2, the coverage range of the ratio error solving threshold and the phase error solving threshold of the current transformer is expanded according to the accuracy level corresponding to the current transformer and the regulation error limit.

[0033] Further, the step S4 includes the following steps:

[0034] Step S4.1: After obtaining the samples of each current transformer, a multi-objective weighted fitness function model taking the ratio error and the phase error of each current transformer as solving objects and minimizing the sum of the node current vectors as the optimization target is established based on the KCL principle, the current transformer error solving problem is converted into the minimization problem of the sum of the node current vectors in the multi-objective weighted fitness function model, and the formula of the initial multi-objective weighted fitness function model is as follows:

[0035] ,

[0036] ,

[0037] Step S4.2: Based on the initial multi-objective weighted fitness function model, the initial multi-objective weighted fitness function model is constrained in combination with the solving threshold of the ratio error and the phase error of the current transformer in step S3.3, and an optimized multi-objective weighted fitness function model is obtained, and the formula of the optimized multi-objective weighted fitness function model is as follows:

[0038] ,

[0039] ,

[0040] , wherein,is the ratio error of the CT on the i-th line in the evaluation group,is the phase error of the CT on the i-th line in the evaluation group,is the accuracy level of the CT,is the accuracy level of the CT.​This is the rated secondary current value of the CT. Let be the secondary current phasor of the i-th sample.

[0041] Furthermore, step S5 includes the following steps:

[0042] Step S5.1: After obtaining the optimized multi-objective weighted fitness function model, the positions of the initial particles are randomly generated in the search space, an improved Logistic mapping is introduced to diffuse the initial particle population, and the optimal position Pbest and the global optimal position Gbest for each particle are set.

[0043] Step S5.2: Based on the optimized multi-objective weighted fitness function model, calculate the fitness of each initial particle, take the top three particles in fitness as α, β and δ particles, and the remaining particles as ω particles, and calculate the wolf position based on the positions of α, β and δ particles.

[0044] Step S5.3: Based on the gray wolf algorithm, calculate the updated positions of α, β and δ particles, and based on the updated positions of α, β and δ particles, calculate the updated position of the wolf; update the position of ω particle based on particle swarm optimization algorithm, and combine the Lévy flight perturbation mechanism to apply noise interference to ω particle to enhance the global search capability of ω particle;

[0045] Step S5.4: If the updated position of each particle or wolf is within the threshold range for solving the ratio difference and phase difference of the current transformer, and the fitness of each particle after the position update is better than the best fitness corresponding to each particle, then update the position of each particle to the current position and record the current fitness of each particle as the best fitness. If the best fitness of a certain particle is better than the global best fitness, then set the current position of the particle as the best position Gbest.

[0046] Step S5.5: Based on the current fitness of each particle, reset the top three particles in fitness ranking as α, β, and δ particles, and repeat steps S5.2-S5.4 to iteratively solve the ratio difference and phase difference of each current transformer until a preset stopping condition is met. Then, stop the iterative solution of the particle swarm and output the global best position Gbest to obtain the optimal solution for the error value of the current in-phase current transformer. The preset stopping condition is: when the rate of decrease of the optimized multi-objective weighted fitness function model is detected to be less than 0.5% for three consecutive times... Or the number of iterations of the particle swarm reaches 200.

[0047] Furthermore, step S6 includes the following steps:

[0048] Step S6.1: Based on the double threshold mechanism, a hypothesis testing model with two judgment thresholds is constructed, and the preliminary analysis result in step S3 is taken as the first discriminant object of the hypothesis testing model, and the final solution of the difference and phase difference obtained in step S5 is taken as the second discriminant object of the hypothesis testing model;

[0049] Step S6.2: One of the judgment thresholds of the hypothesis testing model is set as: if the Mahalanobis distance of a sample in the in-phase current transformer sample exceeds the Mahalanobis distance threshold in the kernel space, the hypothesis testing model triggers an early warning, and through the fault separation algorithm, the current transformer with the problem is located, and then the current transformer corresponding to the data is reported to exist abnormal situation;

[0050] Step S6.3: The other judgment threshold of the hypothesis testing model is set as: when the current transformer error output by the optimized multi-objective weighted fitness function model is detected to exceed the error threshold corresponding to the current transformer level in the regulation, the hypothesis testing model triggers an early warning, and reports that the current transformer corresponding to the data exists abnormal situation;

[0051] Step S6.4: Based on the two judgment thresholds of the hypothesis testing model, the first discriminant object and the second discriminant object are compared and analyzed respectively;

[0052] Step S6.5: If the hypothesis testing model has 80% of the results showing that both discriminant objects think that the current transformer at this place has an out-of-tolerance problem within a specified time, it is determined that the current transformer at this place has an out-of-tolerance problem.

[0053] Based on the same inventive concept, the application also provides a current transformer error solving system, a statistical quantity threshold calculation module, a same-phase current transformer sample construction module, a current transformer state analysis module, a solving model construction module, a particle swarm optimization module, and a double threshold judgment module. The statistical quantity threshold calculation module is used to collect operation data of current transformers of each measuring line at a same node of a substation in a first preset time period, to construct a sample analysis data set, and to combine a principal component analysis method to perform component decomposition and feature extraction on the sample analysis data set, establish an initial steady state, and then calculate the statistical quantity threshold in the sample data set. The same-phase current transformer sample construction module is used to collect original waveform data of current transformers of each measuring line at a same node of a substation in a second preset time period, to perform filtering processing on the original waveform data based on a sliding window mean filtering algorithm to obtain filtered original waveform data, and to combine a fast Fourier transform method to calculate the filtered original waveform data to obtain a real-time condition judgment of each measuring line current transformer, and then construct a same-phase current transformer sample. The current transformer state analysis module is used to calculate the statistical quantity of each sample in the same-phase current transformer sample based on the calculation step of the statistical quantity threshold in step S1 of the above-mentioned current transformer error solving method, compare the statistical quantity of each sample with the statistical quantity threshold of the initial steady state to obtain a preliminary analysis result of the current transformer state, and then set the solving threshold of the ratio error and the phase error of the current transformer in combination with the accuracy level of the current transformer. The solving model construction module is used to establish an initial multi-objective weighted fitness function model taking the ratio error and the phase error of each current transformer as the solving object and the minimum of the node primary current vector as the optimization target based on the KCL principle. After obtaining the multi-objective weighted fitness function model, the solving threshold of the ratio error and the phase error of the current transformer in step S3 of the above-mentioned current transformer error solving method is combined to constrain the initial multi-objective weighted fitness function model to obtain an optimized multi-objective weighted fitness function model. The particle swarm optimization module is used to introduce an improved Logistic mapping to initialize the particle swarm after obtaining the optimized multi-objective weighted fitness function model. Based on the fitness of each particle, the particle swarm is cooperatively optimized by combining a grey wolf algorithm, a particle swarm optimization algorithm, and a Levy flight disturbance mechanism to iteratively update the position of each particle until a preset stop condition is met, and the solving result of the final ratio error and phase error is obtained. In the iteration process, whether each particle is updated is limited in combination with the solving threshold of the ratio error and the phase error of the current transformer in step S3 of the above-mentioned current transformer error solving method.The double-threshold judgment module is used for constructing a hypothesis test model with two judgment thresholds based on a double-threshold mechanism, taking the preliminary analysis result in step S3 of the above one kind of current transformer error solving method as a first discrimination object of the hypothesis test model, taking the solving result of the final difference and phase difference obtained in step S5 of the above one kind of current transformer error solving method as a second discrimination object of the hypothesis test model, and comparing and analyzing the first discrimination object and the second discrimination object respectively based on the two judgment thresholds of the hypothesis test model, wherein if the hypothesis test model has 80% of the results showing that both discrimination objects think that the current transformer at the place has an over-limit problem within a specified time length, it is determined that the current transformer at the place indeed has an over-limit problem.

[0054] Based on the same inventive concept, the application also provides a data processing device based on a current transformer error solving method, comprising a memory and a processor, the memory is used for storing a computer program, and the processor is used for executing the computer program to realize the steps of the above-mentioned data processing method based on a current transformer error solving method.

[0055] Based on the same inventive concept, the application also provides a computer readable storage medium: the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the above-mentioned current transformer error solving method.

[0056] The application adopts the above-mentioned scheme, which has the following advantages:

[0057] 1) The calculation accuracy is significantly improved: by fusing the grey wolf algorithm and the particle swarm optimization algorithm and introducing the Levy flight disturbance mechanism, the problem that the traditional algorithm is easy to fall into local optimum is effectively overcome, the optimal solution of CT error can be more accurately searched in the complex solution space, and the accuracy of error calculation is improved.

[0058] 2) Enhance the robustness of the algorithm: the Levy flight disturbance mechanism enables the algorithm to jump out of the local optimal region during the search process, increases the randomness and diversity of the search, improves the adaptability of the algorithm to different working conditions and data characteristics, and enhances the robustness of the algorithm.

[0059] 3) Improve the stability of the power system: accurate CT error calculation can provide reliable data support for protection, control and metering of the power system, avoid misoperation of relay protection, inaccurate power metering and other problems caused by current measurement error, and improve the stability and reliability of the power system, and ensure the safe operation of the power system. In the event of a power system fault, accurate current measurement can enable the relay protection device to act in time and accurately, and remove the faulty line to prevent the fault from expanding. BRIEF DESCRIPTION OF DRAWINGS

[0060] Figure 1 A flowchart of a current transformer error solving method provided by the present application is shown in FIG. 1.

[0061] Figure 2 A specific flowchart of a current transformer error solving method provided by the present application is shown in FIG. 2.

[0062] Figure 3 A structure diagram of a current transformer error solving system provided by the present application is shown in FIG. 3. DETAILED DESCRIPTION

[0063] In order to facilitate the understanding of the present application, the present application will be described more fully below with reference to the accompanying drawings. The preferred embodiments of the present application are shown in the drawings. However, the present application can be realized in many different forms and is not limited to the embodiments described herein. The purpose of providing these embodiments is to make the disclosure of the present application more thorough and comprehensive. Embodiment 1

[0064] Referring to FIG. 1, in the present embodiment, a current transformer error solving method is provided, which comprises the following steps: Figures 1-2 Step S1: collecting the operation data of the current transformers of each measuring line under the same node of the transformer substation in a first preset time period, to construct a sample analysis data set, and combining the principal component analysis method to perform component decomposition and feature extraction, to establish an initial steady state, and then to calculate the

[0065] The Q-statistic test can determine whether a time series is stationary (i.e., its statistical properties do not change over time);

[0066] Step S1 comprises the following steps:

[0067] Step S1.1: collecting the operation data of the current transformers of each branch under the same node of the transformer substation in a first preset time period (the operation data is the amplitude and phase value of the current transformer, and the above operation data is obtained in a plurality of measurements), to establish a sample analysis data set in the form of a current transformer vector, and based on the kernel function method, to establish a kernel matrix composed of the sample analysis data set, i.e., to take the operation data of the current transformers of the same node as the training data set, and then to form the kernel matrix:

[0068] ,

[0069] Wherein, n is the number of samples, and m is the number of elements of a single sample collection;

[0070] The kernel matrix (training data set) is calculated The element in the i-th row and j-th column of the matrix is:

[0071] ​ , ,

[0072] in, For kernel functions, common kernel functions include the Gaussian kernel (Radial Basis Function, RBF), the p-order polynomial kernel, and the sigmoid kernel, etc., and the specific choice needs to be made based on the data; x i Let x be the vector of the i-th row of the data matrix x. j Let be the j-th column vector of the data matrix x.

[0073] The Gaussian kernel function (RBF) described above can handle complex nonlinear relationships, and its expression is as follows:

[0074] ,

[0075] in, is a key parameter of the Gaussian kernel function.

[0076] Step S1.2: Transform the kernel matrix using a centering method to eliminate mean shift in the feature space, obtaining the centered kernel matrix. The specific expression is:

[0077] ,

[0078] in, It is an n×n identity matrix.

[0079] Step S1.3: For the centered kernel matrix Eigenvalue decomposition is performed to obtain each eigenvalue and its corresponding eigenvector, which is equivalent to solving for each eigenvalue. With each feature vector The specific formula is as follows:

[0080] .

[0081] Step S1.4: After obtaining each eigenvalue and its corresponding eigenvector, sort the eigenvalues ​​in descending order. Based on the calculation principle of cumulative contribution rate, obtain the k largest eigenvalues ​​with a cumulative contribution rate greater than or equal to 85%. And based on the first k largest eigenvalues Construct the variance contribution diagonal matrix and the first k largest eigenvalues Eigenvectors with one-to-one correspondence The principal component direction matrix formed .

[0082] Step S1.5: After obtaining the principal component direction matrix Then, the principal component projection of each sample in the sample analysis dataset is calculated The specific expression is:

[0083] ,

[0084] Wherein, is the i-th row of the row vector of .

[0085] Step S1.6: After obtaining the principal component projection of each sample , the sum of squares of the residual vectors is calculated to obtain statistical quantity, specifically:

[0086] ,

[0087] In actual calculation, the above formula can be simplified by kernel function:

[0088] ,

[0089] Wherein, is the kernel function value, is the Q statistic calculated for a single sample (i.e. is the specific embodiment of the Q statistic on a single sample, and the value corresponding to each sample is obtained by a specific calculation method, so that the characteristics of each sample in the residual space can be analyzed);

[0090] It should be noted that the above kernel function and the kernel function value are the same function applied in different steps, wherein the kernel function is used to represent the similarity of samples x i and x j in the high-dimensional feature space, which can capture the nonlinear relationship between samples, and the kernel function value is used to represent the "self-reference" of the sample in the high-dimensional space, i.e. the self-similarity of the sample, which can be used as a reference for residual calculation to measure the deviation of the sample from its principal component projection.

[0091] Step S1.7: After obtaining the set of Q statistics, combine the quantile method to take the 99% quantile of the normal samples in the sample analysis dataset as the optimal discrimination threshold, or combine the chi-square distribution method to determine the statistical quantity threshold under the pre-set confidence level (such as 95%);

[0092] It should be noted that the above kernel principal component analysis (KPCA) technical principle is that the original nonlinear characteristics of the current transformer (CT) are mapped to a high-dimensional feature space through a kernel function, and principal components are extracted in the high-dimensional space to construct a more accurate anomaly detection boundary, which is more suitable for processing nonlinear distortion problems of the CT when saturated than traditional PCA; secondly, the above correlation Q statistics obeys the chi-square distribution method, which can determine whether there is a significant difference between the actual value and the theoretical expected value, thereby improving The specific steps of the statistical threshold value reliability and the chi-square distribution method are easily thought of by those skilled in the art, and are not expanded here.

[0093] Step S2: after obtaining the statistical threshold value, the original waveform data of the current transformer of each measuring line at the same node of the substation in a second preset time period is collected, the original waveform data is filtered based on the sliding window mean filtering algorithm to obtain the filtered original waveform data, and the filtered original waveform data is calculated based on the fast Fourier transform method to obtain the real-time condition judgment of each measuring line current transformer, and then the same-phase current transformer sample is constructed.

[0094] Step S2.1: the original waveform data of the same-phase current transformer of each measuring line at the same node of the substation is collected by a high-precision sensor in a second preset time period, wherein the original waveform data is the secondary side current waveform data of the current transformer, and the second preset time period is after the first preset time period (the specific selection of the above first preset time period and the second preset time period can be selected according to the actual situation).

[0095] Step S2.2: the original current waveform data is filtered based on the sliding window mean filtering algorithm (window length 200ms) to obtain the preprocessed (filtered) waveform data (the above sliding window mean filtering algorithm and harmonic amplitude calculation formula belong to common algorithm procedures, and the principle is easily thought of by those skilled in the art, so it is not described here); through the above sliding window mean filtering algorithm, transient noise can be eliminated, and data points deviating from the mean value ±3 times the standard deviation are linearly interpolated and corrected, which is more convenient for inputting the corrected current data into the subsequent multi-objective weighted fitness function model.

[0096] Step S2.3: based on the filtered waveform data, combined with fast Fourier transform (FFT), the amplitude and phase value of each harmonic of each current transformer and the total harmonic distortion rate are calculated, and then the same-phase current transformer sample is constructed, and the calculation formula of the total harmonic distortion rate is:

[0097] ,

[0098] wherein, is the square sum of all harmonic current, is the square sum of all harmonic current, is the effective value of fundamental current.

[0099] Step S2.4: When the total harmonic distortion of a certain current transformer exceeds 5% (preset value one) or the amplitude of a certain harmonic (such as 5th, 7th) is greater than 3% of the fundamental (preset value two), it is determined that the current transformer is disturbed by harmonics (under the harmonic distortion signal, the transformation characteristics of the current transformer will cause errors, and then affect the electric energy metering).

[0100] Step S3: Based on step S1 (combined with the residual space characteristics obtained by kernel principal component analysis method when constructing the initial steady state) statistical quantity threshold calculation step, calculate the statistical quantity of each sample statistical quantity of the same-phase current transformer sample, and compare the statistical quantity of each sample with the statistical quantity threshold of the initial steady state to obtain the preliminary analysis result of the current transformer state, and then set the solution threshold of the current transformer ratio error and phase error (i.e. error limit value of ratio error and phase error) combined with the accuracy level of the current transformer; In the normal operation of the current transformer, the ratio error and phase error will affect the accuracy of its electric energy metering, therefore, it is necessary to reduce the influence of the ratio error and phase error on the normal operation of the current transformer, and the specific calculation formula of the ratio error and phase error is easily thought of by those skilled in the art, which is not expanded here.

[0101] Step S3.1: After obtaining the same-phase current transformer sample, combine step S1 statistical quantity threshold calculation step, calculate the statistical quantity of the sample and the kernel matrix, specifically: for each test sample in the same-phase current transformer sample, the same kernel function method is used for calculation to obtain the kernel matrix of the sample and the training data set , wherein the i-th element in the kernel matrix is:

[0102] ,

[0103] Decenter the new kernel matrix:

[0104] ,

[0105] Map the test sample to the principal component direction of the high-dimensional feature space and calculate its projection:

[0106] ,

[0107] Simplify the calculation of samples using kernel functions Statistic:

[0108] .

[0109] Step S3.2: Transfer the sample Statistics and steps in S1.6 If the statistical thresholds are compared, Statistical quantity exceeds Statistical threshold This indicates that the current transformer group within the substation may have an out-of-tolerance problem. A fault separation algorithm is used to locate the current transformers potentially causing this problem. The contribution rate of the variables to the residual space is as follows:

[0110] ,

[0111] Normalization can be used to calculate the specific percentage contribution of each element in a single measurement, as follows:

[0112] .

[0113] Step S3.3: After obtaining the preliminary analysis results of the current transformer status, combine the accuracy class of the current transformer to divide the solution thresholds for the ratio difference and phase difference in the current transformer. Specifically, when the current transformer is preliminarily determined to be problem-free in step S3.2 and the accuracy class corresponding to the current transformer is 0.1S, the solution threshold for the ratio difference of the current transformer is set to [-0.1%, 0.1%] and the threshold for the phase difference is [-5', 5'].

[0114] Step S3.4: When it is initially determined in step S3.2 that there is a problem with the current transformer, expand the coverage of the solution threshold for the ratio difference and phase difference in the current transformer according to the accuracy class and procedure error limit corresponding to the current transformer.

[0115] Step S4: After obtaining the samples of in-phase current transformers, based on the KCL principle, establish an initial multi-objective weighted fitness function model with the ratio difference and phase difference of each current transformer as the solution object and minimizing the node primary current vector sum as the optimization objective; and after obtaining the multi-objective weighted fitness function model, combine the solution threshold of the ratio difference and phase difference of the current transformers in step S3 to constrain the initial multi-objective weighted fitness function model to obtain the optimized multi-objective weighted fitness function model.

[0116] Step S4.1: After obtaining the samples of the current transformers, an initial multi-objective weighted fitness function model is established based on the KCL principle, taking the ratio error and phase error of each current transformer as the solving objects and the sum of the primary current vectors of the nodes and the minimum as the optimization target. The formula of the initial multi-objective weighted fitness function model is:

[0117] ,

[0118] wherein, is the ratio error of the CT on the i-th line in the evaluation group, is the phase error of the CT on the i-th line in the evaluation group, is the secondary side current phasor of the i-th sample.

[0119] Step S4.2: After obtaining the initial multi-objective weighted fitness function model, the initial multi-objective weighted fitness function model is constrained in combination with the solving threshold of the ratio error and phase error of the current transformer in step S3.3, to obtain an optimized multi-objective weighted fitness function model. The formula of the above-mentioned optimized multi-objective weighted fitness function model is:

[0120] ,

[0121] ,

[0122] wherein, is the ratio error of the CT on the i-th line in the evaluation group, is the phase error of the CT on the i-th line in the evaluation group, is the accuracy level of the CT, is the rated secondary current value of the CT, is the secondary side current phasor of the i-th sample.

[0123] The above-mentioned s.t. is the abbreviation of “subject to”, meaning “limited by” or “satisfy the following constraint conditions”, which is used to separate the objective function of the optimization problem from the constraint conditions, indicating that when finding the optimal solution, the solution must satisfy both the optimization direction of the objective function and all constraint conditions, so as to ensure that the optimization result is both the minimum error and meets the physical limit of the CT and the accuracy level of the CT.

[0124] Step S5: After obtaining the optimized multi-objective weighted fitness function model, an improved Logistic mapping is introduced to initialize the particle swarm; and based on the fitness of each particle, the particle swarm is optimized in collaboration by combining the grey wolf algorithm, the particle swarm optimization algorithm and the Levy flight disturbance mechanism, so as to iteratively update the position of each particle, until a preset stop condition is met, and the solving result of the final ratio error and phase error is obtained, wherein in the iteration process, whether each particle is updated is limited in combination with the solving threshold of the current transformer ratio error and phase error in step S3.

[0125] Step S5.1: After obtaining the optimized multi-objective weighted fitness function model, the position and update speed of the initial particle and other parameters are randomly generated in the search space, and then the improved Logistic mapping is introduced to diffuse the initial particle swarm (generally, the initial particle swarm is 30-60 by default, so as to balance the calculation accuracy and single calculation speed), and the best position of the i-th particle and the global best position of all particles ;

[0126] In the above initial particles, the random generation formula of the position of each particle is:

[0127] ,

[0128] The diffusion formula of the improved Logistic mapping is:

[0129] ,

[0130] wherein, and respectively represent the minimum and maximum values of the j-th dimension, is a random number generated in [0, 1]; is a random number obeying the standard normal distribution;

[0131] Secondly, the speed initialization formula can be consistent with the position formula, and the speed boundary is usually 10%-20% of the position boundary.

[0132] It should be noted that by means of the improved Logistic chaotic mapping, an initial position is generated for the sample as the initial particle position, which ensures the diversity of the initial position of the particle through iterative calculation, avoids premature convergence of the particle in the optimization process, and can effectively avoid the algorithm from falling into a local optimal solution, improve the global search ability, and thus make the algorithm more accurate and efficient.

[0133] Step S5.2: Calculate the fitness of each initial particle based on the optimized multi-objective weighted fitness function model. Since the goal of the optimized multi-objective weighted fitness function model is to minimize, the fitness function can take the absolute value of its reciprocal. The specific formula is:

[0134] ,

[0135] At this time, the higher the fitness, the better the position. The top three particles in the fitness ranking are α, β, and δ particles, corresponding to positions , and . The remaining particles are ω particles, and the wolf position is calculated based on the positions of α, β, and δ particles. The specific calculation formula is:

[0136] ,

[0137] The wolf position is determined by calculating the average of the positions of α, β, and δ particles, which can guide other particles to move to more potential areas, thereby improving the search efficiency of the algorithm, avoiding the search process from falling into a local optimal solution, and helping to explore more widely in the solution space to find better solutions.

[0138] Step S5.3: Update the positions of α, β, and δ particles based on the grey wolf algorithm. Specifically:

[0139] First, generate random coefficients A and C:

[0140] ,

[0141] ,

[0142] where a is the convergence factor (linearly decaying from 2 to 0 according to the number of iterations), and are random numbers in the range [0, 1];

[0143] Calculate the positions of updated α, β, and δ particles, and update the position of the wolf based on the positions of updated α, β, and δ particles:

[0144] ,

[0145] ,

[0146] ,

[0147] ,

[0148] The position of the updated ω particle is calculated based on the particle swarm optimization algorithm, and the specific formula is:

[0149]

[0150]

[0151] wherein, is an inertia weight, and are learning factors, and are random numbers in the range of [0, 1];

[0152] The position of the ω particle is randomly disturbed (noise interference is applied) to enhance the global search ability of the ω particle by combining the Levy flight disturbance mechanism, and the formula is:

[0153]

[0154] wherein, is a Levy flight intensity coefficient (default 0.5), is 1.5, is a random number subject to a standard normal distribution, is a hierarchical weight of the grey wolf algorithm.

[0155] Step S5.4: If the updated position of each particle or wolf is within the solution threshold range of the current transformer ratio difference and phase difference, and the fitness of the updated position of each particle is better than the best fitness corresponding to each particle, the position of each particle is updated to the current position, and the current fitness of each particle is recorded as the best fitness. If the best fitness of a particle is better than the global best fitness, the current position of the particle is set as the best position Gbest.

[0156] Step S5.5: Based on the current fitness of each particle, the particles with the top three fitness are re-set as the α, β and δ particles, and steps S5.2-S5.4 are repeated to iteratively solve the ratio difference and phase difference of each current transformer. When the preset stopping condition is met, the iterative solution of the particle swarm is stopped, and the global best position Gbest is output to obtain the optimal solution of the current current transformer error value. The preset stopping condition is that when the optimization of the multi-objective weighted fitness function model is detected for 3 times, the drop rate is < 0.0001, or the iteration number of the particle swarm reaches 200 times.

[0157] ​​​​Unlike the traditional algorithm, the error calculation result will appear large deviation under the complex working conditions such as harmonic interference in the power system. The grey wolf algorithm used in the embodiment can still accurately calculate the CT error under the same condition. The introduction of the Levy flight disturbance mechanism enables the grey wolf algorithm to jump out of the local optimal region in the search process, thereby increasing the randomness and diversity of the search, improving the adaptability of the grey wolf algorithm to different working conditions and data characteristics, and improving the CT error accuracy level from 0.2 level of the traditional algorithm to 0.1 level, improving the accuracy of error calculation and enhancing the robustness of the algorithm.

[0158] Step S6: Based on the double threshold mechanism, a hypothesis test model with two judgment thresholds is constructed, the preliminary analysis result in step S3 is taken as the first discriminant object of the hypothesis test model, the comparison analysis result of the threshold value obtained by solving the final ratio difference and phase difference in step S5 and the ratio difference and phase difference in step S3 is taken as the second discriminant object of the hypothesis test model, and based on the two judgment thresholds of the hypothesis test model, the first discriminant object and the second discriminant object are compared and analyzed respectively, wherein if within a preset time period 3, the hypothesis test model has 80% of the results showing that both discriminant objects think that the current transformer at this place has an out-of-tolerance problem, it is determined that the current transformer at this place indeed has an out-of-tolerance problem.

[0159] Step S6.1: Based on the double threshold mechanism, a hypothesis test model with two judgment thresholds is constructed, and the preliminary analysis result in step S3 is taken as the first discriminant object of the hypothesis test model, and the solving result of the final ratio difference and phase difference obtained in step S5 is taken as the second discriminant object of the hypothesis test model.

[0160] Step S6.2: One of the judgment thresholds of the hypothesis test model is set to: if the Mahalanobis distance of a sample in the in-phase current transformer sample exceeds the Mahalanobis distance threshold in the kernel space, the hypothesis test model triggers a warning, and through the fault separation algorithm, the current transformer with the problem is located, and then the current transformer corresponding to the data is reported to exist abnormal situation.

[0161] Step S6.3: The other judgment threshold of the hypothesis test model is set to: when it is detected that the error of the current transformer output by the optimized multi-objective weighted fitness function model exceeds the error threshold of the corresponding current transformer level in the regulation, the hypothesis test model triggers a warning, and reports that the current transformer corresponding to the data exists abnormal situation.

[0162] Step S6.4: Based on the two judgment thresholds of the hypothesis test model, the first discriminant object and the second discriminant object are compared and analyzed respectively.

[0163] Step S6.5: If within a specified time period (which can be set according to relevant regulations or actual conditions), the assumption test model shows that both discriminators believe that the current transformer at the location has an error problem, it is determined that the current transformer at the location has an error problem.

[0164] In summary, the current transformer error solving method in the embodiment combines the advantages of the Grey Wolf Optimizer (GWO) and the Particle Swarm Optimization (PSO), including the Levy flight disturbance mechanism and the design of the multi-objective weighted fitness function. Considering the fast convergence speed of the Particle Swarm Optimization (PSO), but due to the sensitivity of its parameters, the Particle Swarm Optimization (PSO) is prone to fall into a local optimal solution, resulting in deviation from the global optimal result. To solve this problem, the current transformer error solving method in the embodiment combines the idea of the Grey Wolf Optimizer (GWO), and in the search process, based on the role setting of different levels of "grey wolves" (such as alpha, beta, and delta wolves), the particle swarm is guided to search for the global optimal solution more efficiently. Secondly, accurate CT error calculation can provide reliable data support for the protection, control, and metering of the power system, avoiding problems such as relay protection misoperation and inaccurate power metering caused by current measurement error, thereby improving the stability and reliability of the power system and ensuring the safe operation of the power system. In the event of a power system fault, accurate current measurement can enable the relay protection device to act in a timely and accurate manner, removing the faulty line to prevent the fault from expanding. Example 2

[0165] Reference Figure 3As shown, based on the same inventive concept, the application also provides a current transformer error solving system, a statistical quantity threshold calculation module, a same-phase current transformer sample construction module, a current transformer state analysis module, a solving model construction module, a particle swarm optimization module, and a double threshold judgment module. The statistical quantity threshold calculation module is used to collect operation data of current transformers of each measuring line at a same node of a substation in a first preset time period, to construct a sample analysis data set, and to combine a principal component analysis method to perform component decomposition and feature extraction on the sample analysis data set, establish an initial steady state, and then calculate the statistical quantity threshold in the sample data set. The same-phase current transformer sample construction module is used to collect original waveform data of current transformers of each measuring line at a same node of a substation in a second preset time period, to perform filtering processing on the original waveform data based on a sliding window mean filtering algorithm to obtain filtered original waveform data, and to combine a fast Fourier transform method to calculate the filtered original waveform data to obtain a real-time condition judgment of each measuring line current transformer, and then construct a same-phase current transformer sample. The current transformer state analysis module is used to calculate the statistical quantity of each sample in the same-phase current transformer sample based on the calculation step of the statistical quantity threshold in step S1 of the current transformer error solving method in the above embodiment, compare the statistical quantity of each sample with the statistical quantity threshold of the initial steady state to obtain a preliminary analysis result of the current transformer state, and then set the solving threshold of the ratio error and the phase error of the current transformer in combination with the accuracy level of the current transformer. The solving model construction module is used to establish an initial multi-objective weighted fitness function model taking the ratio error and the phase error of each current transformer as a solving object and the minimum of the node primary current vector as an optimization target based on the KCL principle. After obtaining the multi-objective weighted fitness function model, the solving threshold of the ratio error and the phase error of the current transformer in step S3 of the current transformer error solving method in the above embodiment is combined to constrain the initial multi-objective weighted fitness function model to obtain an optimized multi-objective weighted fitness function model. The particle swarm optimization module is used to introduce an improved Logistic mapping to initialize the particle swarm after obtaining the optimized multi-objective weighted fitness function model. Based on the fitness of each particle, the particle swarm is cooperatively optimized in combination with a grey wolf algorithm, a particle swarm optimization algorithm, and a Levy flight disturbance mechanism to iteratively update the position of each particle until a preset stop condition is met, and the solving result of the final ratio error and phase error is obtained. In the iteration process, whether each particle is updated is limited in combination with the solving threshold of the ratio error and the phase error of the current transformer in step S3 of the current transformer error solving method in the above embodiment.The double-threshold judgment module is configured to construct a hypothesis testing model with two judgment thresholds based on a double-threshold mechanism, take the preliminary analysis result in step S3 of the current error solving method based on a current transformer as the first discrimination object of the hypothesis testing model, take the solving result of the final ratio difference and phase difference obtained in step S5 of the current error solving method based on a current transformer as the second discrimination object of the hypothesis testing model, and perform comparative analysis on the first discrimination object and the second discrimination object based on the two judgment thresholds of the hypothesis testing model, wherein if the hypothesis testing model has 80% of the results showing that both discrimination objects think that the current transformer at the position has an over-error problem within a specified time length, it is determined that the current transformer at the position indeed has an over-error problem. Embodiment 3

[0166] Based on the same inventive concept, the present application further provides a data processing device based on a current transformer error solving method, comprising a memory and a processor, the memory is used to store a computer program, and the processor is used to execute the computer program to realize the steps of the data processing method of the current error solving method based on a current transformer in the above embodiment. Embodiment 4

[0167] Based on the same inventive concept, the present application further provides a computer readable storage medium: the computer readable storage medium stores a computer program, and the computer program is executed by a processor to realize the steps of the current error solving method in the above embodiment.

[0168] The above-mentioned embodiments are only the preferred embodiments of the present application, and do not limit the present application in any form. Any skilled person in the art can make more possible changes and decorations, or modifications to the technical solution of the present application without departing from the scope of the technical solution of the present application, using the disclosed technical content. Therefore, any equivalent changes made according to the idea of the present application without departing from the technical solution of the present application shall be covered within the protection scope of the present application.

Claims

1. A method for solving the error of a current transformer, characterized in that: Includes the following steps: Step S1: Collect the operating data of current transformers on each measuring line under the same node of the substation within the first preset time period to construct a sample analysis dataset. Then, using principal component analysis (PCA), decompose and extract features from the sample analysis dataset to establish an initial steady state, and finally calculate the current transformers in the sample dataset. Statistical threshold; Step S2: Collect the original waveform data of the current transformers of each measuring line under the same node of the substation within the second preset time period. Based on the sliding window mean filtering algorithm, filter the original waveform data to obtain the filtered original waveform data. Combined with the fast Fourier transform method, calculate the filtered original waveform data to obtain the real-time status judgment of the current transformers of each measuring line, and then construct the in-phase current transformer sample. Step S3: Based on step S1 The steps for calculating the statistical threshold are as follows: calculate the threshold value for each sample in the in-phase current transformer sample. Statistics, and the values ​​of each sample Statistics and initial steady state Statistical thresholds are compared to obtain preliminary analysis results of the current transformer's state. Then, based on the accuracy level of the current transformer, the thresholds for solving the ratio difference and phase difference in the current transformer are set. Step S4: Based on the KCL principle, establish an initial multi-objective weighted fitness function model with the ratio difference and phase difference of each current transformer as the solution object and minimizing the primary current vector sum of the nodes as the optimization objective; after obtaining the initial multi-objective weighted fitness function model, combine the solution thresholds of the ratio difference and phase difference of the current transformers in Step S3 to constrain the initial multi-objective weighted fitness function model and obtain the optimized multi-objective weighted fitness function model; Step S5: After obtaining the optimized multi-objective weighted fitness function model, an improved Logistic mapping is introduced to initialize the particle swarm. Based on the fitness of each particle, the particle swarm is collaboratively optimized by combining the Grey Wolf algorithm, the particle swarm optimization algorithm, and the Lévy flight perturbation mechanism to iteratively update the position of each particle until the preset stopping condition is met, and the final solution results of the ratio difference and phase difference are obtained. During the iteration process, the solution threshold of the current transformer ratio difference and phase difference in step S3 is used to restrict whether each particle is updated. Step S6: Based on the dual-threshold mechanism, construct a hypothesis testing model with two judgment thresholds. Use the preliminary analysis results in Step S3 as the first discrimination object of the hypothesis testing model, and use the final solution results of the ratio difference and phase difference obtained in Step S5 as the second discrimination object of the hypothesis testing model. Based on the two judgment thresholds of the hypothesis testing model, compare and analyze the first discrimination object and the second discrimination object respectively. If, within a specified time, 80% of the results of the hypothesis testing model show that both discrimination objects believe that the current transformer has an out-of-tolerance problem, then it is determined that the current transformer does indeed have an out-of-tolerance problem.

2. The method for solving the error of a current transformer according to claim 1, characterized in that: Step S1 includes the following steps: Step S1.1: Collect the operating data of the in-phase current transformers of each branch under the same node of the substation within the first preset time period to establish a sample analysis dataset in vector form of the current transformers, and establish a kernel matrix composed of the sample analysis dataset based on the kernel function method, wherein the operating data is the amplitude and phase value of the current transformers; Step S1.2: Transform the kernel matrix based on the centering method to eliminate the mean shift in the feature space and obtain the centered kernel matrix; Step S1.3: Perform eigenvalue decomposition on the centered kernel matrix to obtain each eigenvalue and each eigenvector corresponding to each eigenvalue; Step S1.4: After obtaining each eigenvalue and its corresponding eigenvector, sort the eigenvalues ​​in descending order. Based on the calculation principle of cumulative contribution rate, obtain the k largest eigenvalues ​​with a cumulative contribution rate greater than or equal to 85%, and construct a variance contribution diagonal matrix based on the k largest eigenvalues. The principal component direction matrix consists of the eigenvectors corresponding one-to-one with the first k largest eigenvalues. ; Step S1.5: Obtain the principal component direction matrix Then, calculate the principal component projection vectors of each sample in the sample analysis dataset; Step S1.6: After obtaining the principal component projection vectors of each sample, the Q statistic is obtained by calculating the sum of squares of the residual vectors; Step S1.7: After obtaining the set of Q statistics, combine the quantile method with the 99th percentile of the normal samples in the sample analysis dataset as the optimal discrimination threshold, or combine the chi-square distribution method to determine the pre-set reliability level. Statistical threshold.

3. The method for solving the error of a current transformer according to claim 1, characterized in that: Step S2 includes the following steps: Step S2.1: Collect the original waveform data of the current transformers of the same phase of each measuring line under the same node of the substation through a high-precision sensor within the second preset time period. The original waveform data is the secondary current waveform data of the current transformer. Step S2.2: Based on the sliding window mean filtering algorithm, filter the original current waveform data to obtain the filtered waveform data; Step S2.3: Based on the filtered waveform data, combined with the fast Fourier transform, calculate the amplitude and phase values ​​of each harmonic of each current transformer, as well as the total harmonic distortion rate, and then construct the in-phase current transformer sample. Step S2.4: When the total harmonic distortion rate of a current transformer exceeds a preset value one or the amplitude of a specific harmonic exceeds a preset value two, it is determined that the current transformer has harmonic interference.

4. The method for solving the error of a current transformer according to claim 1, characterized in that: Step S3 includes the following steps: Step S3.1: After obtaining the in-phase current transformer sample, combine it with the information from step S1. The steps for calculating the threshold of a statistic, including calculating the sample... Statistic; Step S3.2: Transfer the sample Statistics and steps in S1.6 If the statistical thresholds are compared, Statistical quantity exceeds If the statistical threshold is set, it is determined that there may be a problem with the current transformer in the substation. The fault separation algorithm is then used to locate the current transformer that may have a problem. Step S3.3: After obtaining the preliminary analysis results of the current transformer status, combine the accuracy class of the current transformer to divide the solution thresholds for the ratio difference and phase difference in the current transformer. Specifically, when the current transformer is preliminarily determined to be problem-free in step S3.2 and the accuracy class corresponding to the current transformer is 0.1S, the solution threshold for the ratio difference of the current transformer is set to [-0.1%, 0.1%], and the solution threshold for the phase difference is set to [-5', 5']. Step S3.4: When it is initially determined in step S3.2 that there is a problem with the current transformer, expand the coverage of the ratio difference solution threshold and phase difference solution threshold of the current transformer according to the accuracy class and procedure error limit corresponding to the current transformer.

5. The method for solving the error of a current transformer according to claim 4, characterized in that: Step S4 includes the following steps: Step S4.1: After obtaining the samples of each in-phase current transformer, based on the KCL principle, establish a multi-objective weighted fitness function model with the ratio difference and phase difference of each current transformer as the solution object and minimizing the primary current vector sum of the nodes as the optimization objective. This transforms the current transformer error solution problem into the problem of minimizing the primary current vector sum in the multi-objective weighted fitness function model. The formula for the initial multi-objective weighted fitness function model is: ， in, To evaluate the ratio error of CT on the i-th line in the population, To evaluate the phase error of the CT on the i-th line in the population, Let be the secondary current phasor of the i-th sample; Step S4.2: Based on the initial multi-objective weighted fitness function model, and combined with the threshold values ​​for the current transformer ratio difference and phase difference obtained in Step S3.3, constraints are applied to the initial multi-objective weighted fitness function model to obtain the optimized multi-objective weighted fitness function model. The formula for the optimized multi-objective weighted fitness function model is as follows: ， ， in, To evaluate the ratio error of CT on the i-th line in the population, To evaluate the phase error of the CT on the i-th line in the population, The accuracy level of CT scans. This is the rated secondary current value of the CT. Let be the secondary current phasor of the i-th sample.

6. The method for solving the error of a current transformer according to claim 1, characterized in that: Step S5 includes the following steps: Step S5.1: After obtaining the optimized multi-objective weighted fitness function model, the positions of the initial particles are randomly generated in the search space, an improved Logistic mapping is introduced to diffuse the initial particle population, and the optimal position Pbest and the global optimal position Gbest for each particle are set. Step S5.2: Based on the optimized multi-objective weighted fitness function model, calculate the fitness of each initial particle, take the top three particles in fitness as α, β and δ particles, and the remaining particles as ω particles, and calculate the wolf position based on the positions of α, β and δ particles. Step S5.3: Based on the gray wolf algorithm, calculate the updated positions of α, β and δ particles, and based on the updated positions of α, β and δ particles, calculate the updated position of the wolf; update the position of ω particle based on particle swarm optimization algorithm, and combine the Lévy flight perturbation mechanism to apply noise interference to ω particle to enhance the global search capability of ω particle; Step S5.4: If the updated position of each particle or wolf is within the threshold range for solving the ratio difference and phase difference of the current transformer, and the fitness of each particle after the position update is better than the best fitness corresponding to each particle, then update the position of each particle to the current position and record the current fitness of each particle as the best fitness. If the best fitness of a certain particle is better than the global best fitness, then set the current position of the particle as the best position Gbest. Step S5.5: Based on the current fitness of each particle, reset the top three particles in fitness ranking as α, β, and δ particles, and repeat steps S5.2-S5.4 to iteratively solve the ratio difference and phase difference of each current transformer until a preset stopping condition is met. Then stop the iterative solution of the particle swarm and output the global best position Gbest to obtain the optimal solution of the error value of the current in-phase current transformer. The preset stopping condition is: when the rate of decrease of the optimized multi-objective weighted fitness function model is detected to be <10 for three consecutive times. -6 Or the number of iterations of the particle swarm reaches 200.

7. The method for solving the error of a current transformer according to claim 1, characterized in that: Step S6 includes the following steps: Step S6.1: Based on the dual threshold mechanism, construct a hypothesis testing model with two judgment thresholds, and use the preliminary analysis results in step S3 as the first discriminant of the hypothesis testing model, and use the final solution results of the ratio difference and phase difference obtained in step S5 as the second discriminant of the hypothesis testing model. Step S6.2: Set one of the judgment thresholds of the hypothesis testing model as follows: if a sample in the in-phase current transformer sample exceeds the core space Mahalanobis distance threshold, the hypothesis testing model triggers an early warning and locates the problematic current transformer through the fault separation algorithm, and then reports that the current transformer corresponding to the data has an abnormal situation. Step S6.3: Set another judgment threshold for the hypothesis testing model as follows: when the error of the current transformer output by the optimized multi-objective weighted fitness function model exceeds the error threshold of the corresponding current transformer level in the procedure, the hypothesis testing model triggers an early warning and reports that there is an abnormality in the current transformer corresponding to the data. Step S6.4: Based on the two judgment thresholds of the hypothesis testing model, perform comparative analysis on the first and second discriminant objects respectively; Step S6.5: If, within the specified time period, 80% of the results of the hypothesis testing model show that both judgment objects believe that the current transformer at that location has an out-of-tolerance problem, then it is determined that the current transformer at that location has an out-of-tolerance problem.

8. A current transformer error solving system, characterized in that, include: The system comprises a statistical threshold calculation module, a current transformer sample construction module, a current transformer state analysis module, a solution model construction module, a particle swarm optimization module, and a dual threshold judgment module. The statistical threshold calculation module collects operating data from current transformers on each measuring line under the same node of the substation within a first preset time period to construct a sample analysis dataset. It then uses principal component analysis (PCA) to decompose and extract features from the sample analysis dataset, establishing an initial steady state and calculating the statistical threshold in the sample dataset. The current transformer sample construction module collects raw waveform data from current transformers on each measuring line under the same node of the substation within a second preset time period. Based on a sliding window mean filtering algorithm, it filters the raw waveform data to obtain filtered raw waveform data. In addition, by combining the fast Fourier transform method, the filtered original waveform data is calculated to obtain the real-time status judgment of the current transformers of each measurement line, and then a sample of in-phase current transformers is constructed. The current transformer state analysis module is used to calculate the statistical threshold in step S1 of the current transformer error solution method according to any one of claims 1 to 7, calculate the statistical value of each sample in the in-phase current transformer sample, and compare the statistical value of each sample with the initial steady-state statistical threshold to obtain the preliminary analysis results of the current transformer state. Then, combined with the accuracy level of the current transformer, the solution thresholds for the ratio difference and phase difference in the current transformer are set. The solution model construction module is used to establish a solution model based on the KCL principle, with the ratio difference and phase difference of each current transformer as the solution object, and the node primary current vector... The initial multi-objective weighted fitness function model is optimized by minimizing the sum of quantities. After obtaining the multi-objective weighted fitness function model, the initial multi-objective weighted fitness function model is constrained by the solution thresholds for the current transformer ratio difference and phase difference in step S3 of the current transformer error solution method according to any one of claims 1 to 7, thereby obtaining an optimized multi-objective weighted fitness function model. The particle swarm optimization module is used to introduce an improved Logistic mapping to initialize the particle swarm after obtaining the optimized multi-objective weighted fitness function model. Based on the fitness of each particle, the gray wolf algorithm is combined with the optimization module. The particle swarm optimization algorithm and the Lévy flight perturbation mechanism are used to collaboratively optimize the particle swarm, iteratively updating the position of each particle until a preset stopping condition is met, thus obtaining the final solution results for the ratio difference and phase difference. During the iteration process, the solution thresholds for the ratio difference and phase difference of the current transformer in step S3 of any one of claims 1 to 7 are used to restrict whether each particle is updated. The dual-threshold judgment module is used to construct a hypothesis testing model with two judgment thresholds based on the dual-threshold mechanism, using the current transformer error solution method according to any one of claims 1 to 7. The preliminary analysis results in step S3 are used as the first discrimination object of the hypothesis testing model. The final ratio difference and phase difference calculation results obtained in step S5 of the current transformer error solution method according to any one of claims 1 to 7 are used as the second discrimination object of the hypothesis testing model. Based on the two judgment thresholds of the hypothesis testing model, the first discrimination object and the second discrimination object are compared and analyzed respectively. If, within a specified time, 80% of the results of the hypothesis testing model show that both discrimination objects believe that the current transformer has an out-of-tolerance problem, then it is determined that the current transformer does indeed have an out-of-tolerance problem.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of a current transformer error solving method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed, it implements the steps of the current transformer error solving method as described in any one of claims 1 to 7.

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