CN110031788A - A kind of hollow coil current transformer error environment correlation analysis - Google Patents

Abstract

The invention discloses a method for analyzing the error environment correlation of an air-core coil current transformer, comprising the following steps: constructing an original matrix according to collected data within an evaluation time window; expanding the original matrix based on a Kalman filter to establish a high-dimensional matrix random matrix; standardize the high-dimensional random matrix to convert it into a non-Hermitian matrix with a row vector mean value of 0 and a variance of 1; obtain an impact correlation evaluation matrix according to the non-Hermitian matrix; Obtain the air-core coil current transformer error environment correlation evaluation index according to the influence quantity correlation evaluation matrix, and determine the relationship between the air-core coil current transformer error and the environmental parameters according to the influence quantity correlation evaluation matrix and the correlation evaluation index. The correlation between them was evaluated. Advantages: The degree of correlation between the operating error of the transformer and one or more environmental parameters can be obtained in real time, which is beneficial to control and evaluate the stability of the error state in the operation of the transformer.

Description

An error environment correlation analysis method for air-core coil current transformers

technical field

The invention belongs to the field of state evaluation of power transmission and distribution equipment, and more particularly relates to an error environment correlation analysis method of air-core coil current transformers based on high-dimensional matrix theory.

Background technique

Piezoelectric current transformer is an important measurement device that provides current information for the power metering system of substations, and its operation performance is related to the accuracy of the metering device. At present, the traditional electromagnetic current widely used not only has complex insulation structure, large volume and high cost, but also has shortcomings such as magnetic saturation and small dynamic range, which makes it difficult to meet the technical requirements of power systems. Electronic current transformers have the advantages of large dynamic range, wide frequency response range, small size and light weight, and conform to the development direction of digitalization, intelligence and networking of power systems. Air-core coil current transformer is a kind of electronic current transformer. In recent years, with the rapid development of intelligent substation construction, after years of exploration and practice, a series of achievements have been achieved.

However, the structure of the air-core coil current transformer is relatively complex, including many links, and is affected by environmental parameters such as temperature, humidity, vibration, magnetic field and primary load current during operation. From the point of view of on-site operation, the accuracy of the air-core coil current transformer still occupies a large proportion. It affects the fairness of electric energy trade settlement and hinders the popularization and application of air-core coil current transformers. It is of great significance to reveal the inherent relationship and influence law between the transformer error and various environmental parameters, and to clarify the main influence quantities, which can provide guidance for the design and process of air-core coil current transformers, and is of great significance to the error stability control and evaluation of air-core coil current transformers.

The prior art includes a model-based correlation analysis method, which analyzes the mechanism and law of the influence of environmental parameters on the error of the transformer according to the mechanism model of the effect of environmental parameters on the air-core coil current transformer. This method is highly dependent on the accuracy of the model. Assumptions and premise also affect the analysis results. Different mechanism models need to be established for different environmental parameters, which are less versatile and cannot obtain quantitative evaluation indicators of correlation.

The prior art also includes a data-driven analysis method, which does not need to build an accurate mechanism model, and obtains the correlation between the transformer error and the environmental parameters by mining, processing and analyzing the error data of the transformer and the environmental parameter data. However, the relationship between the current transformer error and environmental parameters in the air-core coil presents the characteristics of multi-coupling and high randomness. It is difficult to determine the degree of correlation between the transformer error and the environmental parameters, and the above analysis methods are not applicable.

SUMMARY OF THE INVENTION

The technical problem to be solved by the present invention is to overcome the defects of the prior art, and to provide a high-dimensional matrix theory-based air-core coil current transformer error environment correlation analysis method that does not rely on the mechanism model, which can be used for the error analysis of the air-core coil current transformer. The control method provides a reference.

In order to solve the above technical problems, the present invention provides a method for analyzing the error environment correlation of an air-core coil current transformer, which is characterized in that it includes the following steps:

S1: Collect environmental parameter data and air-core coil current transformer error data, and construct an original matrix according to the collected environmental parameter data and error data within the evaluation time window;

S2: Expand the original matrix based on the Kalman filter to establish a high-dimensional random matrix;

S3: Standardize the high-dimensional random matrix to convert it into a non-Hermitian matrix with a row vector mean of 0 and a variance of 1;

S4: obtaining the influence quantity correlation evaluation matrix according to the non-Hermitian matrix;

S5: Obtain the air-core coil current transformer error environment correlation evaluation index according to the influence amount correlation evaluation matrix, and determine the air-core coil current transformer error and environment according to the influence amount correlation evaluation matrix and the correlation evaluation index. The correlation between the parameters is evaluated.

Further, in step S1, an environmental parameter matrix is constructed through the collected environmental parameter data Among them, the element P _ij represents the measured value of the i-th measurable environmental parameter at time j, i is the serial number of the measurable environmental parameter, i=1, 2, ... M, M is the number of environmental parameters, and j is the measured environmental parameter. Serial number, j=1, 2,...T, T is the number of measurements; build the error state matrix by the collected error data Among them, the element Q _ij represents the measured value of the ith transformer error parameter at time j, i is the serial number of the transformer error parameter, i=1, 2,...N, N is the number of transformer error parameters, and j is the number of transformer error parameters. The serial number of the measurement, j=1, 2,...T, the original matrix constructed is where k=M+N.

Further, in step S2, the high-dimensional random matrix obtained after expansion is k' is the number of expanded state parameters, the value range of N' satisfies k'/T∈(0,1], and T is the number of measurements.

Further, in step S3, the non-Hermitian matrix is in Represents the mean value of the sample x _i , σ(x _ij ) represents the standard deviation of the sample x _i , x _i is the row vector of the high-dimensional random matrix D ₃ , x _i =(x _i1 ,x _i2 ,...,x _iT ), 1≤i≤k', k' is the number of expanded state parameters, T is the number of measurements, and y _ij is a new variable obtained by the standardization method of the variable x _ij in the high-dimensional random matrix D ₃ .

Further, the step S4 is specifically:

Calculate the singular value equivalent matrix Du of the non _- Hermitian matrix;

Calculate the matrix product Z according to the _singular value equivalent matrix Du;

An error state evaluation matrix Z ₂ is obtained from the matrix product Z.

Further, the matrix product L=1, the error state evaluation matrix in, z _i is the row vector of matrix Z, z _i =(z _i1 ,z _i2 ,...,z _iT ), 1≤i≤k', is the row vector of matrix Z ₂ , σ(z _i ) _represents the standard deviation of zi, k' is the number of state parameters after expansion, and T is the number of measurements.

Further, in step S5, the correlation evaluation index includes d _MSR and I _MSR , where d _MSR =ε _ev -ε _ref , and the integral of d _MSR over time is I _MSR : Among them, t ₁ and t ₂ represent the start time and end time of the evaluation, _{λi is the eigenvalue of the corresponding original matrix, λwi is} the eigenvalue of the corresponding reference matrix, n and _n2 are the number of eigenvalues of the corresponding original matrix and reference matrix, respectively, E() represents the expected eigenvalue sample, so the reference matrix The reference matrix is composed of an air-core coil error state matrix and a Gaussian white noise matrix, where D ₁ is the environmental parameter matrix; D _N is the noise matrix, whose dimensions are the same as the environmental parameter matrix, and the elements are random variables obeying standard normal distribution. The value is the same as the magnitude of the white Gaussian noise superimposed in the matrix expansion.

An error state monitoring system for an air-core coil current transformer, characterized in that it includes an original matrix building module, a high-dimensional random matrix building module, a standardized processing module, an influence quantity correlation evaluation matrix module, and a correlation evaluation module;

The original matrix building module is used to collect environmental parameter data and air-core coil current transformer error data, and build an original matrix according to the collected environmental parameter data and error data within the evaluation time window;

The high-dimensional random matrix building module is used to expand the original matrix based on the Kalman filter to establish a high-dimensional random matrix;

The standardization processing module is used to standardize the high-dimensional random matrix to convert it into a non-Hermitian matrix with a row vector mean of 0 and a variance of 1;

The influence quantity correlation assessment matrix module is used to obtain the influence quantity correlation assessment matrix according to the non-Hermitian matrix;

The correlation evaluation module is used to obtain the air-core coil current transformer error environment correlation evaluation index according to the influence amount correlation evaluation matrix, and to evaluate the air-core coil according to the influence amount correlation evaluation matrix and the correlation evaluation index. The correlation between current transformer errors and environmental parameters is evaluated.

Further, the original matrix building module is used to build the environmental parameter matrix by the collected environmental parameter data Among them, the element P _ij represents the measured value of the i-th measurable environmental parameter at time j, i is the serial number of the measurable environmental parameter, i=1, 2, ... M, M is the number of environmental parameters, and j is the measured environmental parameter. Serial number, j=1, 2,...T, T is the number of measurements; build the error state matrix by the collected error data Among them, the element Q _ij represents the measured value of the ith transformer error parameter at time j, i is the serial number of the transformer error parameter, i=1, 2, ... N, N is the number of transformer error parameters, and j is the The serial number of the measurement, j=1, 2,...T, the original matrix constructed is where k=M+N.

Further, the high-dimensional random matrix established by the high-dimensional random matrix building module is k' is the number of expanded state parameters, the value range of N' satisfies k'/T∈(0,1], and T is the number of measurements.

Further, the non-Hermitian matrix obtained by the standardized processing module is that the non-Hermitian matrix is in Represents the mean value of the sample x _i , σ(x _ij ) represents the standard deviation of the sample x _i , x _i is the row vector of the high-dimensional random matrix D ₃ , x _i =(x _i1 ,x _i2 ,...,x _iT ), 1≤i≤k', k' is the number of expanded state parameters, T is the number of measurements, and y _ij is a new variable obtained by the standardization method of the variable x _ij in the high-dimensional random matrix D ₃ .

Further, the influence quantity correlation evaluation matrix module is used to calculate the singular value equivalent matrix D _u of the non-Hermitian matrix, and calculate the matrix product Z according to the singular value equivalent matrix D _u , and according to the matrix The product Z obtains the error state evaluation matrix Z ₂ ;

the matrix product The error state evaluation matrix in, z _i is the row vector of matrix Z, z _i =(z _i1 ,z _i2 ,...,z _iT ), 1≤i≤k', is the row vector of matrix Z ₂ , σ(z _{i )} is the standard deviation of zi, k' is the number of state parameters after expansion, and T is the number of measurements

Further, the correlation evaluation index includes d _MSR and I _MSR , where d _MSR =ε _ev -ε _ref , and the integral of d _MSR over time is I _MSR : Among them, t ₁ and t ₂ represent the start time and end time of the evaluation, _{λi is the eigenvalue of the corresponding original matrix, λwi is} the eigenvalue of the corresponding reference matrix, n and _n2 are the number of eigenvalues of the corresponding original matrix and reference matrix, respectively, E() represents the expected eigenvalue sample, so the reference matrix The reference matrix is composed of an air-core coil error state matrix and a Gaussian white noise matrix, where D ₁ is the environmental parameter matrix; D _N is the noise matrix, whose dimensions are the same as the environmental parameter matrix, and the elements are random variables obeying standard normal distribution. The value is the same as the magnitude of the white Gaussian noise superimposed in the matrix expansion

Beneficial effects achieved by the present invention:

The invention does not need to establish any physical model, has no assumptions and simplified conditions, and only quantifies the internal relationship between the transformer error and the environmental parameters as a correlation evaluation index according to the error data of the air-core coil current transformer and the environmental parameter data, and evaluates according to the correlation. The size and change trend of the index can obtain the correlation degree between the operating error of the transformer and one or more environmental parameters in real time, which is beneficial to control and evaluate the stability of the error state in the operation of the transformer.

Description of drawings

Fig. 1 is the evaluation flow schematic diagram of the present invention;

Figure 2 is a schematic diagram of an online monitoring platform for the error state of an air-core coil current transformer;

Fig. 3 is the eigenvalue distribution diagram of the influence quantity correlation evaluation matrix _Dev1 ;

Fig. 4 is the eigenvalue distribution diagram of the influence quantity correlation evaluation matrix _Dev2 ;

Fig. 5 is the change trend diagram of the correlation evaluation index of the correlation evaluation matrix D _ev1 corresponding to the influence amount;

FIG. 6 is a change trend diagram of the correlation evaluation index corresponding to the correlation evaluation matrix D _ev2 of the influence amount.

Among them, 1 is an air-core coil current transformer, 2 is an electromagnetic current transformer, 3 is an environmental monitoring unit, 4 is an optical fiber remote transmission unit, 5 is a signal acquisition unit, 6 is a data processing unit, 7 is a time synchronization unit, 8 is the switch and 9 is the server.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, and cannot be used to limit the protection scope of the present invention.

The invention only needs to establish a high-dimensional random matrix according to the error data and environmental parameter data of the air-core coil current transformer, and obtain the correlation evaluation index from the high-dimensional random matrix, and the correlation evaluation index reflects the statistical distribution law of the elements of the high-dimensional random matrix. It can be used to characterize the correlation between the error state of the transformer and the environmental parameters, according to which the correlation of the error state of the transformer can be analyzed.

In the present invention, the method for analyzing the error environment correlation of the air-core coil current transformer based on the high-dimensional matrix theory includes the following steps:

Step 1: Online monitoring of air-core coil current transformer errors and environmental parameters, within the evaluation time window, construct the environmental parameter matrix, the error state matrix and the original matrix;

Since the time series data obtained by online monitoring has time-varying characteristics, the real-time processing method of sliding time window is adopted, that is, the end time of the previous time window is the start time of the next time window, and the environmental parameters of the current time and historical time are obtained. and error data, arrange the data at each sampling moment in time series, and the data at the current moment and historical moment can be included in the original matrix. The length of the sliding time window ranges from 100 to ∞(s). Preferably, the length of the sliding time window can be selected to be 1800s.

The error types of air-core coil current transformers include ratio difference and angular difference, and the types of environmental parameters include non-electrical parameters and electrical parameters. Electrical parameters can be divided into magnetic field parameters and primary load current parameters (referred to as load parameters), and non-electrical parameters can be divided into are temperature parameters, humidity parameters and vibration parameters. In each intercepted evaluation time window, M environmental parameters are measured T times, and N transformer error data are measured T times. The value range of M is 1 to 5, preferably, M is selected to be 5; the value range of N is 1 to 2, preferably, the value of N is selected to be 2; the value range of T is 300 to ∞, preferably, T The selection is 360. All measurement data can form the original matrix D:

Among them, k=M+N, x _ij represents the value of the jth measurement of the i-th parameter, i is the serial number of the parameter, i=1, 2, ... k, j is the serial number of the measurement times, j=1, 2 , ... T.

Step 2: Expand the original matrix D based on the single-state Kalman filter to obtain a high-dimensional random matrix D ₃ ;

Since there are few types of error data and environmental parameters of the air-core coil current transformer, even after combining the two, the dimension of the constructed influence quantity correlation evaluation matrix is still small, which cannot meet the construction conditions of high-dimensional random matrix. In order to solve this problem, the matrix expansion method based on the single-state Kalman filter equation is adopted.

Based on the Kalman filter equation, the accurate measurement value of the measurement system is estimated as: Among them, x _k is the system state space quantity at the current moment, x _k+1 is the system state space quantity at the next moment, y _k is the system measurement value; ξ _k is the 0-mean model noise; η _k is the 0-mean measurement noise.

By changing the value of the measurement noise η _k , the output values of multiple sets of Kalman filters can be obtained, and the value range of η _k can be 0.1V _rms ~ 10V _rms , where V _rms is the effective value of the Kalman filter input signal. The output of the Kalman filter is taken as a matrix row, the state parameters are changed from k to k', the dimension of the matrix is expanded, and the value range of k' needs to ensure k'/T∈(0,1], preferably, k' is chosen to be 20, and a high-dimensional random matrix D ₃ is constructed accordingly:

Step ₃ : Standardize the high-dimensional random matrix D3 to convert it into a non-Hermitian matrix with a row vector mean of 0 and a variance of 1;

The matrix D ₃ becomes a non-Hermitian matrix D _std after the following normalization operation: in Represents the mean value of the sample x _i , σ(x _ij ) represents the standard deviation of the sample x _i , x _i =(x _i1 ,x _i2 ,...,x _iT ), 1≤i≤N' is the matrix D ₃ row vector. Make the matrix D _std =(y _ij ) _k'×T after the normalization operation satisfy Wherein, y _i =(y _i1 , y _i2 ,...,y _iT ), 1≤i≤N'.

Step 4: Establish the influence quantity correlation evaluation matrix through the calculation of singular value equivalent matrix, the calculation of matrix product and the calculation of evaluation matrix;

First, find the singular value equivalent matrix D _u of the non-Hermitian matrix:

in, represents the conjugate device of the matrix D _std , where U is the Haar unitary matrix.

Then, compute the matrix product Z: Wherein, D _u,i represents each independent singular value equivalent matrix, and the value range of L is 1～∞, preferably, L is 1.

Finally, based on the matrix product Z, the influence quantity correlation evaluation matrix Z ₂ is obtained:

in, z _i =(z _i1 ,z _i2 ,...,z _iT ), 1≤i≤k' is the row vector of matrix Z, is the row vector of matrix Z ₂ , and σ(z _i ) represents the standard deviation of z _i .

Step 5: Establish an evaluation index of the air-core coil current transformer error environment correlation, and the evaluation index is represented by a linear eigenvalue statistic; according to the influence quantity correlation evaluation matrix Z ₂ and the correlation evaluation index, the air-core coil current transformer error and environmental parameters are determined. correlations between them were analyzed.

The linear eigenvalue statistic can reflect the eigenvalue distribution of a random matrix. For a random matrix, a single eigenvalue cannot reflect the statistical law of the matrix elements in the evaluation time window, while the trace of the matrix can reflect the statistical characteristics of the matrix elements. The reference matrix can be formed by the air-core coil error state matrix and the Gaussian white noise matrix Among them, D _N is the noise matrix, the dimension is the same as the environment parameter matrix, the elements are random variables obeying the standard normal distribution, and the amplitude is the same as that of the Gaussian white noise superimposed in the matrix expansion. The eigenvalues of the reference matrix can constitute a sample of eigenvalues The eigenvalues of the error state evaluation matrix Z ₂ can constitute the eigenvalue samples V={λ ₁ ,λ ₂ ,...λ _n }, and the central moment of the matrix is calculated. and Among them, _λwi is the eigenvalue corresponding to the reference matrix, λi is the eigenvalue corresponding to the original matrix, and _n and _n2 are the number of eigenvalues corresponding to the original matrix and the reference matrix, respectively. E() represents the eigenvalue sample expectation. Define the correlation evaluation index d _MSR : d _MSR =ε _ev -ε _ref , the integral of d _MSR over time is I _MSR : where t ₁ and t ₂ represent the start time and end time of the evaluation.

If there is a correlation between the error of the air-core coil current transformer and the environmental parameters, the singular value equivalent matrix of the random matrix constructed by the transformer error data and the environmental parameter data will satisfy the single-loop theorem, and the eigenvalues will be uniformly distributed in certain Inside the ring of inner and outer radii; otherwise, the eigenvalue distribution will change and the distribution will no longer be uniform.

In order to further understand the present invention, the single ring theorem in the present invention is briefly described below:

In practical applications, if the matrix is a non-Hermitian matrix, and the row vector of matrix A satisfies the mean of 0 and the variance of 1. For multiple non-Hermitian matrices A _i , define the matrix product Among them, A _u,i is the singular value equivalent matrix of A _i . Normalize the matrix A ₂ to A _std so that it satisfies σ ² ( _ai )=1/n, where a _i is the row vector of the matrix A _std , then the limit spectral distribution of A _std converges to the probability density function according to probability 1 for: In formula (12), c=m/n∈(0,1], m,n→∞. The distribution of the eigenvalues of A _std in the complex plane is a ring, and the radius of the inner ring is (1-c) ^{L /2} , the radius of the outer ring is 1. In the normal condition of the equipment, the matrix A _std satisfies the following properties: the standardized product matrix obtained by the singular value equivalent matrix through Haar unitary matrix transformation should satisfy the single ring theorem.

The present invention will be further described below with reference to the accompanying drawings and specific embodiments. The embodiments are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

As shown in Figure 1, the present invention analyzes the correlation between the air-core coil current transformer error and environmental parameters according to the following steps:

(1) Build the air-core coil current transformer error state monitoring platform as shown in Figure 2. The platform includes: an environmental monitoring unit 3, an optical fiber remote transmission unit 4, a signal acquisition unit 5, a data processing unit 6, and a time synchronization unit 7. A 0.2-class air-core coil current transformer 1 and a 0.2-class electromagnetic current transformer 2 are installed in the platform. Taking the output of the electromagnetic current transformer 2 as the standard signal, the comparison result of the error of the air-core coil current transformer 1 can be obtained. The environmental monitoring unit 3 can collect the environmental parameters at the installation place of the transformer, including parameters such as temperature, humidity, vibration, and magnetic field; the optical fiber remote transmission unit 4 standardizes the data of the environmental monitoring unit 3 and sends it to a data processing unit 6 The data processing unit 6 transmits the data to the server 9 through the switch 8, and the monitoring data is stored in the server 9; the signal acquisition unit 5 can collect the output data of the digital electromagnetic current transformer; the data processing unit 6 receives the signal acquisition unit 5 simultaneously The output data and the sampled value message data of the air-core coil current transformer 1. The original random matrix D is constructed according to the environmental parameters and the error data of the air core coil current transformer 1;

(2) Based on the Kalman filter, the matrix D is expanded to form a high-dimensional random matrix D ₃ . The ratio difference data, angular difference data, non-electrical parameter data, and electrical parameter data of the air-core coil current transformer are used to form the original matrix, and the matrix expansion method based on Kalman filter is used to expand each original matrix. The scale is shown in Table 1. The air-core coil current transformer error and environmental parameters are calculated every 10min, and the sliding time window length is 24h, and a total of 144 calculations are performed. This is the number of columns of the original matrix. After expansion by Kalman filter, a 20×144 Extended matrix.

Since only the phase of the electronic transformer is calculated, the row number of the original matrix is 1, while the phase of the electronic transformer is calculated once every 10s, the sliding time window length is 1h, and a total of 360 calculations are performed, which is the number of columns of the original matrix, After expansion by Kalman filter, a 150×360 expansion matrix is formed.

Table 1 High-dimensional matrix scale

(3) The matrix D _std is obtained by normalizing the matrix D ₃ by using the formula (4).

(4) Use formula (6) to formula (8) to obtain the influence quantity correlation evaluation matrix Z ₂ .

(5) Use the ratio difference data of the air-core coil current transformer to form the error state matrix, use the temperature parameter data to form the environmental parameter matrix, and combine the error state matrix and the environmental parameter matrix into the influence quantity correlation evaluation matrix D _ev1 , and the matrix size is 40 ×144; using the ratio difference data of the air-core coil current transformer and the humidity parameter data to form the influence quantity correlation evaluation matrix _Dev2 , and the matrix size is also 40×144. The sliding time window is selected as 1800s. Figures 3 and 4 are the eigenvalue distributions of Dev1 and _{Dev2 respectively} . Comparing Figures 3 and 4, it can be seen that the eigenvalue distribution of the singular value equivalent matrix of _Dev1 is relatively scattered. The eigenvalues exceed the limit of the ring; the eigenvalue distribution of the singular value equivalent matrix of _Dev2 is more concentrated and basically distributed within a ring.

The correlation evaluation index is calculated according to formula (9) to formula (10). Figure 5 and Figure 6 are the change trend diagrams of the correlation evaluation index obtained according to Dev1 and _{Dev2 respectively} . It can be seen that for the evaluation matrix _Dev1 , The maximum value of the evaluation index d _MSR rises to around 0.35, and the I _MSR reaches 273.15; for the evaluation matrix _Dev2 , the evaluation index d _MSR always remains around 0, and the I _MSR is 43.8, which is much smaller than the I _MSR of the matrix _Dev1 . It shows that the relative difference of the air-core coil current transformer has a strong correlation with temperature, and a weak correlation with humidity.

As will be appreciated by those skilled in the art, the embodiments of the present application may be provided as a method, a system, or a computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It will be understood that each flow and/or block in the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to the processor of a general purpose computer, special purpose computer, embedded processor or other programmable data processing device to produce a machine such that the instructions executed by the processor of the computer or other programmable data processing device produce Means for implementing the functions specified in a flow or flow of a flowchart and/or a block or blocks of a block diagram.

These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory result in an article of manufacture comprising instruction means, the instructions The apparatus implements the functions specified in the flow or flow of the flowcharts and/or the block or blocks of the block diagrams.

These computer program instructions can also be loaded on a computer or other programmable data processing device to cause a series of operational steps to be performed on the computer or other programmable device to produce a computer-implemented process such that The instructions provide steps for implementing the functions specified in the flow or blocks of the flowcharts and/or the block or blocks of the block diagrams. The above are only the preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, without departing from the technical principle of the present invention, several improvements and modifications can also be made. These improvements and modifications It should also be regarded as the protection scope of the present invention.

Claims (13)

1. an air-core coil current transformer error environment correlation analysis method, is characterized in that, comprises the steps: S1: Collect environmental parameter data and air-core coil current transformer error data, and construct an original matrix according to the collected environmental parameter data and error data within the evaluation time window; S2: Expand the original matrix based on the Kalman filter to establish a high-dimensional random matrix; S3: Standardize the high-dimensional random matrix to convert it into a non-Hermitian matrix with a row vector mean of 0 and a variance of 1; S4: obtaining the influence quantity correlation evaluation matrix according to the non-Hermitian matrix; S5: Obtain the air-core coil current transformer error environment correlation evaluation index according to the influence amount correlation evaluation matrix, and determine the air-core coil current transformer error and environment according to the influence amount correlation evaluation matrix and the correlation evaluation index. The correlation between the parameters is evaluated. 2 . The method for analyzing the error environment correlation of air-core coil current transformers according to claim 1 , wherein, in step S1 , an environment parameter matrix is constructed by the collected environment parameter data. 3 . Among them, the element P _ij represents the measured value of the i-th measurable environmental parameter at time j, i is the serial number of the measurable environmental parameter, i=1, 2, ... M, M is the number of environmental parameters, and j is the measured environmental parameter. Serial number, j=1, 2,...T, T is the number of measurements; build the error state matrix by the collected error data Among them, the element Q _ij represents the measured value of the ith transformer error parameter at time j, i is the serial number of the transformer error parameter, i=1, 2, ... N, N is the number of transformer error parameters, and j is the The serial number of the measurement, j=1, 2,...T, the original matrix constructed is where k=M+N. 3. The method for analyzing the error environment correlation of air-core coil current transformers according to claim 1, wherein in step S2, the high-dimensional random matrix obtained after expansion is k' is the number of expanded state parameters, the value range of N' satisfies k'/T∈(0,1], and T is the number of measurements. 4. The method for analyzing air-core coil current transformer error environment correlation according to claim 1, wherein in step S3, the non-Hermitian matrix is in Represents the mean value of the sample x _i , σ(x _ij ) represents the standard deviation of the sample x _i , x _i is the row vector of the high-dimensional random matrix D ₃ , x _i =(x _i1 ,x _i2 ,...,x _iT ), 1≤i≤k', k' is the number of expanded state parameters, T is the number of measurements, y _ij is a new variable obtained by the standardization method of the variable x _ij in the high-dimensional random matrix. 5. The air-core coil current transformer error environment correlation analysis method according to claim 1, wherein the step S4 is specifically: Calculate the singular value equivalent matrix Du of the non _- Hermitian matrix; Calculate the matrix product Z according to the _singular value equivalent matrix Du; An error state evaluation matrix Z ₂ is obtained from the matrix product Z. 6. The method for analyzing the error environment correlation of air-core coil current transformers according to claim 5, wherein the matrix product The error state evaluation matrix in, z _i is the row vector of matrix Z, z _i =(z _i1 ,z _i2 ,...,z _iT ), 1≤i≤k', is the row vector of matrix Z ₂ , σ(z _i ) _represents the standard deviation of zi, k' is the number of state parameters after expansion, and T is the number of measurements. 7. The method for analyzing air-core coil current transformer error environment correlation according to claim 1, wherein in step S5, the correlation evaluation index comprises d _MSR and I _MSR , wherein d _MSR =ε _ev − ε _ref , the integral of d _MSR over time is I _MSR : Among them, t ₁ and t ₂ represent the start time and end time of the evaluation, _{λi is the eigenvalue of the corresponding original matrix, λwi is} the eigenvalue of the corresponding reference matrix, n and _n2 are the number of eigenvalues of the corresponding original matrix and reference matrix, respectively, E() represents the expected eigenvalue sample, so the reference matrix The reference matrix is composed of an air-core coil error state matrix and a Gaussian white noise matrix, where D ₁ is the environmental parameter matrix; D _N is the noise matrix, whose dimensions are the same as the environmental parameter matrix, and the elements are random variables obeying standard normal distribution. The value is the same as the magnitude of the white Gaussian noise superimposed in the matrix expansion. 8. An air-core coil current transformer error state monitoring system, characterized in that it comprises an original matrix building module, a high-dimensional random matrix building module, a standardized processing module, an influence quantity correlation evaluation matrix module and a correlation evaluation module; The original matrix building module is used to collect environmental parameter data and air-core coil current transformer error data, and build an original matrix according to the collected environmental parameter data and error data within the evaluation time window; The high-dimensional random matrix building module is used to expand the original matrix based on the Kalman filter to establish a high-dimensional random matrix; The standardization processing module is used to standardize the high-dimensional random matrix to convert it into a non-Hermitian matrix with a row vector mean of 0 and a variance of 1; The influence quantity correlation assessment matrix module is used to obtain the influence quantity correlation assessment matrix according to the non-Hermitian matrix; The correlation evaluation module is used to obtain the air-core coil current transformer error environment correlation evaluation index according to the influence amount correlation evaluation matrix, and to evaluate the air-core coil according to the influence amount correlation evaluation matrix and the correlation evaluation index. The correlation between current transformer errors and environmental parameters is evaluated. 9 . The air-core coil current transformer error state monitoring system according to claim 8 , wherein the original matrix building module is used to build an environmental parameter matrix by collecting environmental parameter data. 10 . Among them, the element P _ij represents the measured value of the i-th measurable environmental parameter at time j, i is the serial number of the measurable environmental parameter, i=1, 2, ... M, M is the number of environmental parameters, and j is the measured environmental parameter. Serial number, j=1, 2,...T, T is the number of measurements; build the error state matrix by the collected error data Among them, the element Q _ij represents the measured value of the ith transformer error parameter at time j, i is the serial number of the transformer error parameter, i=1, 2, ... N, N is the number of transformer error parameters, and j is the The serial number of the measurement, j=1, 2,...T, the original matrix constructed is where k=M+N. 10. The air-core coil current transformer error state monitoring system according to claim 8, wherein the high-dimensional random matrix established by the high-dimensional random matrix building module is: k' is the number of expanded state parameters, the value range of N' satisfies k'/T∈(0,1], and T is the number of measurements. 11. The air-core coil current transformer error state monitoring system according to claim 8, wherein the non-Hermitian matrix obtained by the standardized processing module is that the non-Hermitian matrix is: in Represents the mean value of the sample x _i , σ(x _ij ) represents the standard deviation of the sample x _i , x _i is the row vector of the high-dimensional random matrix D ₃ , x _i =(x _i1 ,x _i2 ,...,x _iT ), 1≤i≤k', k' is the number of expanded state parameters, T is the number of measurements, and yij is a new variable obtained by the standardization method for the variable x _ij in the high-dimensional random matrix. 12. The air-core coil current transformer error state monitoring system according to claim 8, wherein the influence quantity correlation evaluation matrix module is used to calculate the singular value equivalent matrix D _u of the non-Hermitian matrix , the matrix product Z is calculated according to the _singular value equivalent matrix Du, and the error state evaluation matrix Z ₂ is obtained according to the matrix product Z; the matrix product The error state evaluation matrix in, z _i is the row vector of matrix Z, z _i =(z _i1 ,z _i2 ,...,z _iT ), 1≤i≤k', is the row vector of matrix Z ₂ , σ(z _i ) _represents the standard deviation of zi, k' is the number of state parameters after expansion, T is the number of measurements 13. The air-core coil current transformer error state monitoring system according to claim 8, wherein the correlation evaluation index comprises d _MSR and I _MSR , wherein d _MSR =ε _ev -ε _ref , d _MSR versus time The score is Among them, t ₁ and t ₂ represent the start time and end time of the evaluation, _{λi is the eigenvalue of the corresponding original matrix, λwi is} the eigenvalue of the corresponding reference matrix, n and _n2 are the number of eigenvalues of the corresponding original matrix and reference matrix, respectively, E() represents the expected eigenvalue sample, so the reference matrix The reference matrix is composed of an air-core coil error state matrix and a Gaussian white noise matrix, where D ₁ is the environmental parameter matrix; D _N is the noise matrix, whose dimensions are the same as the environmental parameter matrix, and the elements are random variables obeying standard normal distribution. The value is the same as the magnitude of the white Gaussian noise superimposed in the matrix expansion.