Error space extraction optimization method, device, electronic device and readable storage medium

Abstract

The present application discloses an error space extraction optimization method, device, electronic device and readable storage medium, which are applied to the technical field of power transmission and transformation equipment. The error space extraction optimization method comprises: obtaining signal measurement data of a capacitive voltage transformer, judging whether the data volume of the signal measurement data meets a preset data volume threshold; if so, performing singular value decomposition on the signal measurement data to obtain a characteristic decomposition result of the signal measurement data; if not, performing covariance matrix decomposition on the signal measurement data to obtain a characteristic decomposition result of the signal measurement data; determining the principal component space and the error space in the characteristic decomposition result. The present application solves the technical problem of low error space extraction efficiency in the prior art.

Description

Technical Field

[0001] The present application relates to the technical field of power transmission and transformation equipment, and in particular to an error space extraction optimization method, device, electronic device and readable storage medium. Background Art

[0002] With the rapid development of science and technology, the technology of power transmission and transformation equipment has become more and more mature. At present, capacitor voltage transformers use covariance matrix decomposition to extract error space. However, the available memory space for covariance matrix decomposition to process data is small. When the amount of data processed is too large, it is easy to have excessive memory usage, resulting in slow operation or failure to run, resulting in low efficiency in error space extraction. Summary of the invention

[0003] The main purpose of this application is to provide an error space extraction optimization method, device, electronic device and readable storage medium, aiming to solve the technical problem of low error space extraction efficiency in the prior art.

[0004] To achieve the above objectives, the present application provides an error space extraction optimization method, which is applied to an error space extraction optimization device. The error space extraction optimization method includes:

[0005] Acquire signal measurement data of the capacitor voltage transformer, and determine whether the data volume of the signal measurement data meets a preset data volume threshold;

[0006] If yes, then obtaining a characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data;

[0007] If not, obtaining a characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data;

[0008] A principal component space and an error space are determined in the eigendecomposition result.

[0009] Optionally, the eigendecomposition result includes an eigenvector matrix and an eigenvalue matrix, and the step of determining a principal component space and an error space in the eigendecomposition result includes:

[0010] Determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix;

[0011] According to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, a principal element space and an error space are determined in the eigenvector matrix.

[0012] Optionally, the step of determining the principal element space and the error space in the eigenvector matrix according to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer comprises:

[0013] Determine, according to the number of principal components and the number of signal channels, first position information of the principal component space in the eigenvector matrix and second position information of the error space in the eigenvector matrix;

[0014] The principal component space is selected in the eigenvector matrix according to the first position information, and the error space is selected in the eigenvector matrix according to the second position information.

[0015] Optionally, the step of determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix includes:

[0016] Determining the correlation between the signal channels according to the eigenvalue matrix;

[0017] According to the correlation, each of the signal channels in the principal component space is classified to obtain a principal component classification group, and the number of groups in the principal component classification group is used as the number of principal components.

[0018] Optionally, the step of determining the correlation between the signal channels according to the eigenvalue matrix includes:

[0019] According to the eigenvalue matrix, the correlation coefficient between every two signal channels in each of the signal channels is calculated, and the correlation coefficient is used as the correlation between the signal channels.

[0020] Optionally, the step of classifying the signal channels in the principal component space according to the correlation to obtain principal component classification groups includes:

[0021] Selecting a target correlation coefficient having a strong correlation among the correlation coefficients;

[0022] The target signal channel pairs corresponding to the target correlation coefficients are divided into principal component classification groups.

[0023] Optionally, the step of selecting a target signal channel corresponding to a correlation coefficient having a strong correlation among the correlation coefficients includes:

[0024] Determining whether the correlation coefficient meets a preset correlation condition;

[0025] If so, it is determined that the target signal channel pair corresponding to the correlation coefficient has a strong correlation;

[0026] If not, it is determined that there is no strong correlation between the target signal channel pair corresponding to the correlation coefficient.

[0027] To achieve the above-mentioned purpose, the present application further provides an error space extraction and optimization device, which is applied to an error space extraction and optimization device, and the error space extraction and optimization device comprises:

[0028] A judgment module, used to obtain signal measurement data of the capacitive voltage transformer, and judge whether the data volume of the signal measurement data meets a preset data volume threshold;

[0029] a singular value decomposition module, configured to obtain a characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data;

[0030] a covariance decomposition module, for, if not, obtaining a characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data;

[0031] A determination module is used to determine the principal component space and the error space in the eigendecomposition result.

[0032] Optionally, the eigendecomposition result includes an eigenvector matrix and an eigenvalue matrix, and in the step of determining the principal component space and the error space in the eigendecomposition result, the determination module is further used for:

[0033] Determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix;

[0034] According to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, a principal element space and an error space are determined in the eigenvector matrix.

[0035] Optionally, in the step of determining the principal element space and the error space in the eigenvector matrix according to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, the determination module is further used for:

[0036] Determine, according to the number of principal components and the number of signal channels, first position information of the principal component space in the eigenvector matrix and second position information of the error space in the eigenvector matrix;

[0037] The principal component space is selected in the eigenvector matrix according to the first position information, and the error space is selected in the eigenvector matrix according to the second position information.

[0038] Optionally, in the step of determining the number of principal elements of the capacitor voltage transformer according to the eigenvalue matrix, the determining module is further used to:

[0039] Determining the correlation between the signal channels according to the eigenvalue matrix;

[0040] According to the correlation, each of the signal channels in the principal component space is classified to obtain a principal component classification group, and the number of groups in the principal component classification group is used as the number of principal components.

[0041] Optionally, in the step of determining the correlation between the signal channels according to the eigenvalue matrix, the determining module is further used to:

[0042] According to the eigenvalue matrix, the correlation coefficient between every two signal channels in each of the signal channels is calculated, and the correlation coefficient is used as the correlation between the signal channels.

[0043] Optionally, in the step of classifying the signal channels in the principal component space according to the correlation to obtain a principal component classification group, the determination module is further used to:

[0044] Selecting a target correlation coefficient having a strong correlation among the correlation coefficients;

[0045] The target signal channel pairs corresponding to the target correlation coefficients are divided into principal component classification groups.

[0046] Optionally, in the step of selecting a target signal channel corresponding to a correlation coefficient having a strong correlation among the correlation coefficients, the determination module is further used to:

[0047] Determining whether the correlation coefficient meets a preset correlation condition;

[0048] If so, it is determined that the target signal channel pair corresponding to the correlation coefficient has a strong correlation;

[0049] If not, it is determined that there is no strong correlation between the target signal channel pair corresponding to the correlation coefficient.

[0050] The present application also provides an electronic device, which includes: a memory, a processor, and a program of the error space extraction optimization method stored in the memory and executable on the processor. When the program of the error space extraction optimization method is executed by the processor, the steps of the error space extraction optimization method as described above can be implemented.

[0051] The present application also provides a computer-readable storage medium, on which is stored a program for implementing the error space extraction optimization method. When the program of the error space extraction optimization method is executed by a processor, the steps of the error space extraction optimization method as described above are implemented.

[0052] The present application also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the error space extraction optimization method as described above are implemented.

[0053] The present application provides an error space extraction optimization method, device, electronic device and readable storage medium. Compared with the prior art method of extracting error space by only using covariance matrix decomposition method, the present application obtains signal measurement data of a capacitive voltage transformer to determine whether the data volume of the signal measurement data meets a preset data volume threshold; if so, obtains a characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data; if not, obtains a characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data; determines the principal component space and the error space in the characteristic decomposition result, and obtains the characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data; obtains the principal component space and the error space by performing covariance matrix decomposition on the signal measurement data ... Since the complexity of the covariance matrix decomposition method is lower than that of the singular value decomposition method, when the data volume is small, the covariance matrix decomposition method is selected to improve the efficiency of error space extraction. When the data volume is large, the singular value decomposition method is selected so that the error space extraction can run normally. The decomposition method can be flexibly selected according to the size of the data, so that the capacitive voltage transformer can still extract the error space while ensuring normal operation in the face of complex heterogeneous application environments. The technical defect of using only the covariance matrix decomposition method to extract the error space is avoided, which is prone to slow operation or inability to run due to excessive memory usage due to processing too much data volume, thereby improving the efficiency of error space extraction. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0056] Figure 1 This is a flow chart of the first embodiment of the error space extraction optimization method of the present application;

[0057] Figure 2 Schematic diagram of the device structure of the hardware operating environment involved in the error space extraction optimization method in the embodiment of the present application.

[0058] The purpose, features and advantages of this application will be further described in conjunction with the embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0059] In order to make the above-mentioned purposes, features and advantages of the present application more obvious and understandable, the technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work belong to the scope of protection of this application.

[0060] Embodiment 1

[0061] The present application embodiment provides an error space extraction optimization method. In the first embodiment of the error space extraction optimization method of the present application, refer to Figure 1 , the error space extraction optimization method comprises:

[0062] Step S10, obtaining signal measurement data of the capacitor voltage transformer, and determining whether the data volume of the signal measurement data meets a preset data volume threshold;

[0063] Step S20: If yes, then obtain the characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data;

[0064] Step S30: if not, then performing covariance matrix decomposition on the signal measurement data to obtain a characteristic decomposition result of the signal measurement data;

[0065] Step S40, determining a principal component space and an error space in the eigendecomposition result.

[0066] In this embodiment, it should be noted that the preset data volume threshold may be a preset critical value of the system available memory when using the covariance matrix decomposition method, or may be a preset maximum value of the data volume when using the covariance matrix decomposition method.

[0067] In this embodiment, it should be noted that when the singular value decomposition method is used, the system has a large available memory space, which can be used to process signal measurement data with a large amount of data; when the covariance matrix decomposition method is used, the system has a small available memory space, which can be used to process signal measurement data with a small amount of data.

[0068] Exemplarily, steps S10 to S40 include: according to the number of signal channels corresponding to the capacitive voltage transformer, obtaining the signal measurement data of the secondary side of the capacitive voltage transformer in each signal channel corresponding to the number of signal channels, obtaining the data volume of the signal measurement data, and judging whether the data volume meets the preset data volume threshold; if the data volume meets the preset data volume threshold, obtaining the time domain sampling eigenvalues ​​of each signal channel, determining the data matrix composed of each signal measurement data according to the number of signal channels and the time domain sampling eigenvalues, and obtaining the characteristic decomposition result of the signal measurement data by performing singular value decomposition on the data matrix; if the data volume does not meet the preset data volume threshold, obtaining the time domain sampling eigenvalues ​​of each signal channel, determining the data matrix composed of each signal measurement data according to the number of signal channels and the time domain sampling eigenvalues, and obtaining the characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the data matrix; determining the principal component space and the error space in the characteristic decomposition result, wherein the time domain sampling eigenvalues ​​can be the number of time domain sampling points, the time domain sampling amplitude, or the time domain sampling phase value.

[0069] Furthermore, the specific process of the singular value decomposition is as follows:

[0070] T NxM =U N×N ∑ N×M V M×M

[0071] Where, T N×M is the data matrix, U N×N is the decomposition matrix of the data matrix, ∑ N×M is the eigenvalue matrix of the data matrix, V M×M is the eigenvector matrix of the data matrix.

[0072] Furthermore, the specific process of the covariance matrix decomposition is as follows:

[0073]

[0074] Where, T N×M is the data matrix, is the transpose of the data matrix, U N×N is the eigenvector matrix of the data matrix, ∑ N×M is the eigenvalue matrix of the data matrix, is the transpose of the eigenvalue matrix, is the transpose of the eigenvector matrix.

[0075] The singular value decomposition is performed by dividing the data matrix into the decomposition matrix, the eigenvalue matrix and the eigenvector matrix, and the covariance matrix decomposition is performed by dividing the data matrix into the eigenvector matrix and the eigenvalue matrix. Therefore, the covariance matrix decomposition method is less complex than the singular value decomposition method.

[0076] Wherein, in step S40, the eigendecomposition result includes an eigenvector matrix and an eigenvalue matrix, and the step of determining the principal component space and the error space in the eigendecomposition result includes:

[0077] Step S41, determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix;

[0078] Step S42: determining a principal component space and an error space in the eigenvector matrix according to the number of principal components and the number of signal channels corresponding to the capacitive voltage transformer.

[0079] Exemplarily, steps S41 to S42 include: determining the number of principal components of the capacitor voltage transformer based on the eigenvalue matrix; determining the number of signal channels of the capacitor voltage transformer based on the ratio difference and angle difference of the capacitor voltage transformer, and determining the principal component space and error space in the eigenvector matrix based on the number of principal components and the number of signal channels, wherein the ratio difference is a ratio error, which refers to the difference between the actual secondary current (voltage) of the capacitor voltage transformer multiplied by the rated transformation ratio and the actual primary current (voltage), and the percentage of the actual primary current (voltage); the angle difference is a phase angle error, which refers to the phase difference between the secondary current (voltage) phasor of the capacitor voltage transformer and the primary current (voltage) phasor after being rotated 180° counterclockwise.

[0080] Wherein, in step S42, the step of determining the principal element space and the error space in the eigenvector matrix according to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer includes:

[0081] Step A10, determining first position information of the principal element space in the eigenvector matrix and second position information of the error space in the eigenvector matrix according to the number of principal elements and the number of signal channels;

[0082] Step A20: selecting the principal component space in the eigenvector matrix according to the first position information, and selecting the error space in the eigenvector matrix according to the second position information.

[0083] In this embodiment, it should be noted that the first position information is the row and column position of the principal element space in the eigenvector matrix; the second position information is the row and column position of the error space in the eigenvector matrix.

[0084] Exemplarily, steps A10 to A20 include: determining first position information of the principal element space in the eigenvector matrix based on the number of principal elements, and determining second position information of the error space in the eigenvector matrix based on the number of signal channels and the number of principal elements; selecting the principal element space in the eigenvector matrix based on the first position information, and selecting the error space in the eigenvector matrix based on the second position information. For example, when the number of principal elements is 2 and the number of signal channels is 4, the first two columns in the eigenvector matrix are used as the principal element space, and the third to fourth columns in the eigenvector matrix are used as the error space.

[0085] The embodiment of the present application provides an error space extraction optimization method. Compared with the prior art method of extracting error space by only using the covariance matrix decomposition method, the embodiment of the present application obtains the signal measurement data of the capacitive voltage transformer to determine whether the data volume of the signal measurement data meets the preset data volume threshold; if so, the characteristic decomposition result of the signal measurement data is obtained by performing singular value decomposition on the signal measurement data; if not, the characteristic decomposition result of the signal measurement data is obtained by performing covariance matrix decomposition on the signal measurement data; the principal component space and the error space are determined in the characteristic decomposition result. The matrix decomposition method is less complex than the singular value decomposition method. When the data volume is small, the covariance matrix decomposition method is selected to improve the efficiency of error space extraction. When the data volume is large, the singular value decomposition method is selected so that the error space extraction can run normally. The decomposition method can be flexibly selected according to the size of the data, so that the capacitive voltage transformer can still extract the error space while ensuring normal operation in the face of complex heterogeneous application environments. The technical defect of using only the covariance matrix decomposition method to extract the error space is avoided, which is prone to slow operation or inability to run due to excessive memory usage due to processing too much data volume, thereby improving the efficiency of error space extraction.

[0086] Embodiment 2

[0087] Further, based on the first embodiment of the present application, in another embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. On this basis, in step S41, the step of determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix includes:

[0088] Step B10, determining the correlation between the signal channels according to the eigenvalue matrix;

[0089] Step B20: classify the signal channels in the principal component space according to the correlation to obtain principal component classification groups, and use the number of groups in the principal component classification groups as the number of principal components.

[0090] In this embodiment, it should be noted that the prior art determines the number of principal components of the capacitive voltage transformer through a principal component determination formula.

[0091] Specifically, the formula for determining the principal component is as follows:

[0092]

[0093] In the formula, m is the number of the principal components, δ i is the value of the diagonal in the eigenvalue matrix, M is the number of signal channels, and a is the principal component control threshold.

[0094] The setting of the principal component control threshold is related to the actual measurement environment. For different principal component control threshold conditions, the signal-to-noise ratio of the monitoring error signal of the error channel is different, which has different effects on error detection. Therefore, when a fixed principal component control threshold is used to determine the number of principal components, when the signal measurement data changes, it is easy to cause an error space extraction error due to the low signal-to-noise ratio of the monitoring error signal and an unclear coupling law of the setting error, thereby causing error spillover, or an over-correction error spillover due to the high signal-to-noise ratio of the monitoring error signal. The embodiment of the present application determines the number of principal components by the correlation between the signal channels. Even if the signal measurement data changes, a suitable number of principal components can still be adapted in real time, thereby avoiding the technical defects that when a fixed principal component control threshold is used to determine the number of principal components, due to changes in the signal measurement data, an error space extraction error due to the low signal-to-noise ratio of the monitoring error signal and an unclear coupling law of the setting error, thereby causing error spillover, or an over-correction error spillover due to the high signal-to-noise ratio of the monitoring error signal, thereby improving the accuracy of error space extraction and further improving the accuracy of error coupling.

[0095] Wherein, in step B10, the step of determining the correlation between the signal channels according to the eigenvalue matrix includes:

[0096] Step B11, calculating the correlation coefficient between every two signal channels in each of the signal channels according to the eigenvalue matrix, and taking the correlation coefficient as the correlation between the signal channels.

[0097] Exemplarily, step B11 includes: calculating the correlation between every two signal channels in each of the signal channels based on the eigenvalue matrix to obtain a correlation matrix, obtaining a correlation coefficient column of the correlation matrix, obtaining the correlation coefficient between every two signal channels, and using the correlation coefficient as the correlation between each of the signal channels, wherein the correlation matrix includes the numbers of the two signal channels and a correlation coefficient column between the two signal channels.

[0098] Furthermore, the specific process of obtaining the correlation coefficient between each two signal channels is as follows:

[0099]

[0100] In the formula, C i，j is the correlation coefficient between signal channel i and signal channel j, T(i) is the data matrix of signal channel i, is the data matrix transpose of the i signal channel, T(j) is the data matrix of the j signal channel, Transpose the data matrix of the j signal channel.

[0101] The step of classifying the signal channels in the principal component space according to the correlation to obtain principal component classification groups includes:

[0102] Step B21, selecting a target correlation coefficient having a strong correlation from the correlation coefficients;

[0103] Step B22, dividing the target signal channel pairs corresponding to the target correlation coefficients into principal component classification groups.

[0104] Exemplarily, step B21 to step B22 include: based on each of the correlation coefficients, selecting a target correlation coefficient with a strong correlation among the correlation coefficients; obtaining a target signal channel corresponding to the target correlation coefficient, and using each of the target signal channels as a principal component classification group, for example, using channels 1, 3, and 4 with a strong correlation as a principal component classification group, and using channels 2, 5, and 6 with a strong correlation as another principal component classification group.

[0105] Among them, in step B21, the step of selecting the target signal channel corresponding to the correlation coefficient having a strong correlation among the correlation coefficients includes:

[0106] Step C10, determining whether the correlation coefficient meets a preset correlation condition;

[0107] Step C20: If yes, it is determined that the target signal channel pair corresponding to the correlation coefficient has a strong correlation;

[0108] Step C30: If not, it is determined that there is no strong correlation between the target signal channel pair corresponding to the correlation coefficient.

[0109] In this embodiment, it should be noted that the preset correlation condition is a preset condition for determining whether the correlation coefficient is characterized as a strong correlation, and the preset correlation condition may be that the absolute value of the correlation coefficient is greater than a preset value.

[0110] Exemplarily, steps C10 to C30 include: determining whether the correlation coefficient satisfies a preset correlation condition; if the correlation coefficient satisfies the preset correlation condition, determining that there is a strong correlation between the target signal channel pair corresponding to the correlation coefficient; if the correlation coefficient does not satisfy the preset correlation condition, determining that there is no strong correlation between the target signal channel pair corresponding to the correlation coefficient.

[0111] The embodiment of the present application provides an error space extraction optimization method. Compared with the prior art method of extracting error space by only using the covariance matrix decomposition method, the embodiment of the present application obtains the signal measurement data of the capacitive voltage transformer to determine whether the data volume of the signal measurement data meets the preset data volume threshold; if so, the characteristic decomposition result of the signal measurement data is obtained by performing singular value decomposition on the signal measurement data; if not, the characteristic decomposition result of the signal measurement data is obtained by performing covariance matrix decomposition on the signal measurement data; the principal component space and the error space are determined in the characteristic decomposition result. The matrix decomposition method is less complex than the singular value decomposition method. When the data volume is small, the covariance matrix decomposition method is selected to improve the efficiency of error space extraction. When the data volume is large, the singular value decomposition method is selected so that the error space extraction can run normally. The decomposition method can be flexibly selected according to the size of the data, so that the capacitive voltage transformer can still extract the error space while ensuring normal operation in the face of complex heterogeneous application environments. The technical defect of using only the covariance matrix decomposition method to extract the error space is avoided, which is prone to slow operation or inability to run due to excessive memory usage due to processing too much data volume, thereby improving the efficiency of error space extraction.

[0112] Embodiment 3

[0113] The embodiment of the present application further provides an error space extraction and optimization device, which is applied to an error space extraction and optimization device, and the error space extraction and optimization device includes:

[0114] A judgment module, used to obtain signal measurement data of the capacitive voltage transformer, and judge whether the data volume of the signal measurement data meets a preset data volume threshold;

[0115] a singular value decomposition module, configured to obtain a characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data;

[0116] a covariance decomposition module, for, if not, obtaining a characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data;

[0117] A determination module is used to determine the principal component space and the error space in the eigendecomposition result.

[0118] Optionally, the eigendecomposition result includes an eigenvector matrix and an eigenvalue matrix, and in the step of determining the principal component space and the error space in the eigendecomposition result, the determination module is further used for:

[0119] Determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix;

[0120] According to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, a principal element space and an error space are determined in the eigenvector matrix.

[0121] Optionally, in the step of determining the principal element space and the error space in the eigenvector matrix according to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, the determination module is further used for:

[0122] Determine, according to the number of principal components and the number of signal channels, first position information of the principal component space in the eigenvector matrix and second position information of the error space in the eigenvector matrix;

[0123] The principal component space is selected in the eigenvector matrix according to the first position information, and the error space is selected in the eigenvector matrix according to the second position information.

[0124] Optionally, in the step of determining the number of principal elements of the capacitor voltage transformer according to the eigenvalue matrix, the determining module is further used to:

[0125] Determining the correlation between the signal channels according to the eigenvalue matrix;

[0126] According to the correlation, each of the signal channels in the principal component space is classified to obtain a principal component classification group, and the number of groups in the principal component classification group is used as the number of principal components.

[0127] Optionally, in the step of determining the correlation between the signal channels according to the eigenvalue matrix, the determining module is further used to:

[0128] According to the eigenvalue matrix, the correlation coefficient between every two signal channels in each of the signal channels is calculated, and the correlation coefficient is used as the correlation between the signal channels.

[0129] Optionally, in the step of classifying the signal channels in the principal component space according to the correlation to obtain a principal component classification group, the determination module is further used to:

[0130] Selecting a target correlation coefficient having a strong correlation among the correlation coefficients;

[0131] The target signal channel pairs corresponding to the target correlation coefficients are divided into principal component classification groups.

[0132] Optionally, in the step of selecting a target signal channel corresponding to a correlation coefficient having a strong correlation among the correlation coefficients, the determination module is further used to:

[0133] Determining whether the correlation coefficient meets a preset correlation condition;

[0134] If so, it is determined that the target signal channel pair corresponding to the correlation coefficient has a strong correlation;

[0135] If not, it is determined that there is no strong correlation between the target signal channel pair corresponding to the correlation coefficient.

[0136] The error space extraction optimization device provided by the present application adopts the error space extraction optimization method in the above embodiment to solve the technical problem of low error space extraction efficiency. Compared with the prior art, the beneficial effects of the error space extraction optimization device provided by the embodiment of the present application are the same as the beneficial effects of the error space extraction optimization method provided by the above embodiment, and other technical features in the error space extraction optimization device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0137] Embodiment 4

[0138] An embodiment of the present application provides an electronic device, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the error space extraction optimization method in the above embodiment.

[0139] Reference below Figure 2, which shows a schematic diagram of the structure of an electronic device suitable for implementing the embodiments of the present disclosure. The electronic device in the embodiments of the present disclosure may include, but is not limited to, mobile terminals such as mobile phones, notebook computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 2 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0140] like Figure 2 As shown, the electronic device may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). In the RAM, various programs and data required for the operation of the electronic device are also stored. The processing device, ROM, and RAM are connected to each other via a bus. An input / output (I / O) interface is also connected to the bus.

[0141] Typically, the following systems can be connected to the I / O interface: input devices including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices including, for example, a magnetic tape, a hard disk, etc.; and communication devices. The communication device can allow the electronic device to communicate with other devices wirelessly or by wire to exchange data. Although the figure shows an electronic device with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have alternatively.

[0142] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device, or installed from a ROM. When the computer program is executed by a processing device, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0143] The electronic device provided by the present application adopts the error space extraction optimization method in the above embodiment to solve the technical problem of low error space extraction efficiency. Compared with the prior art, the beneficial effects of the electronic device provided by the embodiment of the present application are the same as the beneficial effects of the error space extraction optimization method provided by the above embodiment, and other technical features in the electronic device are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0144] It should be understood that the various parts of the present disclosure can be implemented with hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0145] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0146] Embodiment 5

[0147] This embodiment provides a computer-readable storage medium having computer-readable program instructions stored thereon, and the computer-readable program instructions are used to execute the error space extraction optimization method in the above embodiment.

[0148] The computer-readable storage medium provided in the embodiment of the present application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In the present embodiment, the computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, a system or a device or used in combination with it. The program code contained on the computer-readable storage medium can be transmitted with any appropriate medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0149] The computer-readable storage medium may be included in the electronic device, or may exist independently without being installed in the electronic device.

[0150] The above-mentioned computer-readable storage medium carries one or more programs. When the above-mentioned one or more programs are executed by an electronic device, the electronic device: obtains signal measurement data of the capacitive voltage transformer, and determines whether the data amount of the signal measurement data meets a preset data amount threshold; if so, obtains a characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data; if not, obtains a characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data; and determines a principal component space and an error space in the characteristic decomposition result.

[0151] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages, or a combination thereof, including object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0152] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present application. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0153] The modules involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of the module does not limit the unit itself in some cases.

[0154] The computer-readable storage medium provided by the present application stores computer-readable program instructions for executing the above-mentioned error space extraction optimization method, which solves the technical problem of low error space extraction efficiency. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the embodiment of the present application are the same as the beneficial effects of the error space extraction optimization method provided by the above-mentioned implementation, and will not be repeated here.

[0155] Embodiment 6

[0156] The present application also provides a computer program product, comprising a computer program, wherein when the computer program is executed by a processor, the steps of the error space extraction optimization method as described above are implemented.

[0157] The computer program product provided by the present application solves the technical problem of low error space extraction efficiency. Compared with the prior art, the beneficial effects of the computer program product provided by the embodiment of the present application are the same as the beneficial effects of the error space extraction optimization method provided by the above embodiment, which will not be repeated here.

[0158] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent processing scope of the present application.

Claims

1. An error space extraction optimization method, characterized in that: The error space extraction optimization method comprises: Acquire signal measurement data of the capacitive voltage transformer, and determine whether the data volume of the signal measurement data meets a preset data volume threshold, wherein the preset data volume threshold is a preset critical value of the system available memory when using the covariance matrix decomposition method, or a preset maximum value of the data volume when using the covariance matrix decomposition method; If yes, then obtaining a characteristic decomposition result of the signal measurement data by performing singular value decomposition on the signal measurement data, so that the error space extraction can run normally; If not, performing covariance matrix decomposition on the signal measurement data to obtain a characteristic decomposition result of the signal measurement data to ensure error space extraction efficiency; Determining a principal component space and an error space in the eigendecomposition result, wherein the eigendecomposition result includes an eigenvector matrix and an eigenvalue matrix; The step of determining the principal component space and the error space in the eigendecomposition result comprises: Determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix; According to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, a principal element space and an error space are determined in the eigenvector matrix.

2. The error space extraction optimization method according to claim 1, characterized in that: The step of determining the principal element space and the error space in the eigenvector matrix according to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer comprises: Determine, according to the number of principal components and the number of signal channels, first position information of the principal component space in the eigenvector matrix and second position information of the error space in the eigenvector matrix; The principal component space is selected in the eigenvector matrix according to the first position information, and the error space is selected in the eigenvector matrix according to the second position information.

3. The error space extraction optimization method according to claim 1, characterized in that: The step of determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix comprises: Determining the correlation between the signal channels according to the eigenvalue matrix; According to the correlation, each of the signal channels in the principal component space is classified to obtain a principal component classification group, and the number of groups in the principal component classification group is used as the number of principal components.

4. The error space extraction optimization method according to claim 3, characterized in that: The step of determining the correlation between the signal channels according to the eigenvalue matrix comprises: According to the eigenvalue matrix, the correlation coefficient between every two signal channels in each of the signal channels is calculated, and the correlation coefficient is used as the correlation between the signal channels.

5. The error space extraction optimization method according to claim 4, characterized in that: The step of classifying the signal channels in the principal component space according to the correlation to obtain principal component classification groups comprises: Selecting a target correlation coefficient having a strong correlation among the correlation coefficients; The target signal channel pairs corresponding to the target correlation coefficients are divided into principal component classification groups.

6. The error space extraction optimization method according to claim 5, characterized in that: The step of selecting a target signal channel corresponding to a correlation coefficient having a strong correlation among the correlation coefficients comprises: Determining whether the correlation coefficient meets a preset correlation condition; If so, it is determined that the target signal channel pair corresponding to the correlation coefficient has a strong correlation; If not, it is determined that there is no strong correlation between the target signal channel pair corresponding to the correlation coefficient.

7. An error space extraction optimization device, characterized in that: The error space extraction optimization device comprises: A judgment module, used for obtaining signal measurement data of a capacitive voltage transformer, and judging whether the data volume of the signal measurement data meets a preset data volume threshold, wherein the preset data volume threshold is a preset critical value of the system available memory when using the covariance matrix decomposition method, or a preset maximum value of the data volume when using the covariance matrix decomposition method; A singular value decomposition module, for, if yes, performing singular value decomposition on the signal measurement data to obtain a characteristic decomposition result of the signal measurement data, so as to enable the error space extraction to operate normally; A covariance decomposition module, for, if not, obtaining a characteristic decomposition result of the signal measurement data by performing covariance matrix decomposition on the signal measurement data to ensure error space extraction efficiency; A determination module, used to determine a principal component space and an error space in the eigendecomposition result, wherein the eigendecomposition result includes an eigenvector matrix and an eigenvalue matrix; The step of determining the principal component space and the error space in the eigendecomposition result comprises: Determining the number of principal elements of the capacitive voltage transformer according to the eigenvalue matrix; According to the number of principal elements and the number of signal channels corresponding to the capacitive voltage transformer, a principal element space and an error space are determined in the eigenvector matrix.

8. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the steps of the error space extraction optimization method according to any one of claims 1 to 7.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for implementing the error space extraction optimization method, and the program for implementing the error space extraction optimization method is executed by a processor to implement the steps of the error space extraction optimization method as described in any one of claims 1 to 6.

Images