Current transformer calibration method, device, and medium based on least squares m-estimation

Abstract

The application discloses a current transformer calibration method and device based on minimum mean square M estimation, and a medium, and belongs to the technical field of current transformer calibration. The method extracts response signals from a to-be-calibrated transformer and a standard transformer, and establishes a response signal vector. Parameters of an N-order RVSSLMSM adaptive filter are initialized, the initial parameters and the response signal vector of the to-be-calibrated transformer are brought into the adaptive filter to obtain filter output, an error vector of the adaptive filter output signal vector and the response signal vector of the standard transformer is calculated, and a variance value is updated. According to the size of the variance change amount, the adaptive filter parameters are updated, the response signal vector of the to-be-calibrated transformer is brought into the updated adaptive filter, and the cycle is repeated until the residual error converges or a certain number of iterations is reached, the optimal coefficient of the filter is obtained, the calibration is completed, and the calibration accuracy and the system robustness are effectively improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of current transformer calibration, and particularly relates to a current transformer calibration method, device and medium based on least mean square M estimation. BACKGROUND

[0002] The current transformer is a key sensor widely used in electrical measurement and is of great significance to the power monitoring and testing system. With the rapid development of the power system, the current transformer with high precision, high sensitivity and high resolution is widely used, which makes the calibration accuracy of the current transformer be also raised. However, in the actual calibration process of the current transformer, due to the difference in use time and manufacturing process of the transformer, the resonance point of the transformer will drift, so it is necessary to calibrate by sweeping. When using the sweep method for calibration, it is impossible to guarantee a completely quiet environment, and external electromagnetic interference or mechanical coupling interference will inevitably penetrate, thereby greatly affecting the output signal of the transformer, resulting in a certain uncertainty in the calibration process. These interferences cannot be completely removed, therefore, how to greatly suppress these interferences and improve the robustness of the calibration has become a key problem to be solved.

[0003] At present, in the field of current transformer calibration, a part of the calibration system uses the traditional comparison method for calibration, this method can only calibrate the static characteristics of the current transformer, and it is difficult to calibrate its dynamic characteristics. Although another part of the calibration system realizes the dynamic calibration of the current transformer by modeling the sensor system itself, but this kind of method needs a large amount of prior information, and the non-Gaussian noise interference cannot be effectively processed, the operation complexity is high and the system robustness is poor. SUMMARY

[0004] In order to overcome the deficiencies of the prior art, the application provides a current transformer calibration method, device and medium based on least mean square M estimation, which uses an improved normalized robust least mean square algorithm and an adaptive filtering algorithm combining M estimation theory to calibrate the dynamic characteristics of the current transformer, thereby effectively improving the calibration accuracy and system robustness.

[0005] The application is implemented by the following technical solutions:

[0006] A current transformer calibration method based on least mean square M estimation, comprising:

[0007] Collecting response signals of the same sweep excitation signal of the to-be-calibrated transformer and the standard transformer, and generating response signal vectors of the to-be-calibrated transformer and the standard transformer;

[0008] Initializing an N-order robust variable step size least mean square M estimation adaptive filter parameter vector;

[0009] bringing the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iteration calculation to obtain a filter output signal value;

[0010] calculating an error between the filter output signal value and a corresponding signal value in the response signal vector of the standard mutual inductor and updating a variance value, and updating the filter parameter vector according to the size of the variance change amount;

[0011] judging whether the residual error between the updated filter parameter vector and the filter parameter vector before updating converges or whether the iteration number reaches a preset value, and if so, outputting the updated filter parameter vector to complete the calibration of the current mutual inductor, otherwise bringing the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter after parameter updating to perform next iteration calculation to obtain a filter output signal value, and returning to the filter parameter vector updating step.

[0012] In some embodiments, the response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal are collected to generate the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor, including:

[0013] controlling the excitation signal generating device to simultaneously apply a same sweep excitation signal to the to-be-calibrated mutual inductor and the standard mutual inductor;

[0014] acquiring the response signals of the to-be-calibrated mutual inductor and the response signals of the standard mutual inductor with the same length to generate the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor.

[0015] In some embodiments, the N-order robust variable step-size minimum mean square M estimation adaptive filter parameter vector is initialized, including:

[0016] initializing the step-size parameter of the N-order robust variable step-size minimum mean square M estimation adaptive filter;

[0017] initializing the filter parameter vector of the N-order robust variable step-size minimum mean square M estimation adaptive filter, wherein the size of the filter parameter vector is ;

[0018] initializing the output signal vector of the N-order robust variable step-size minimum mean square M estimation adaptive filter.

[0019] In some embodiments, the output signal vector of the N-order robust variable step-size minimum mean square M estimation adaptive filter is initialized, specifically:

[0020] taking the last a signal value as an initial value of the output signal vector; wherein is a length of the response signal vector.

[0021] In some embodiments, the initial iteration calculation of the response signal vector of the to-be-calibrated mutual inductor in the initialized adaptive filter includes:

[0022] dividing the response signal vector of the to-be-calibrated mutual inductor according to the order of the adaptive filter;

[0023] bringing the initial filter parameter vector and the divided initial iteration into a filter iteration calculation formula with the response signal vector of the to-be-calibrated mutual inductor to obtain a filter output signal value of the initial iteration;

[0024] wherein the filter iteration calculation formula is that the filter output signal value is equal to the product of the response signal vector of the to-be-calibrated mutual inductor and the transpose of the filter parameter vector.

[0025] In some embodiments, the updating of the filter parameter vector includes:

[0026] performing difference operation on the filter output signal value and the corresponding signal value in the response signal vector of the standard mutual inductor to obtain an error value;

[0027] updating the variance value of the filter according to the error value;

[0028] if the difference between the updated variance value and the variance value before updating is less than or equal to a threshold value, it is considered that the current error conforms to Gaussian distribution, and a first updating formula is used to calculate the filter parameter vector of the next iteration;

[0029] if the difference between the updated variance value and the variance value before updating is greater than the threshold value, it is considered that the current error conforms to non-Gaussian distribution, and a second updating formula is used to calculate the filter parameter vector of the next iteration.

[0030] In some embodiments, the first updating formula is:

[0031] ;

[0032] and / or, the second updating formula is:

[0033] ;

[0034] wherein, is the updated filter parameter vector, is the filter parameter vector before updating, is a step parameter of the filter, is the variance value before updating, a corresponding signal value in the response signal vector of the standard transformer for the current iteration, a response signal vector of the transformer to be calibrated for the current iteration, a number of mixed Laplace components, , parameters of the m-th mixed Laplace component, an error value of the current iteration, a sign function, a transpose of the updated filter parameter vector.

[0035] In some embodiments, the response signal vector of the transformer to be calibrated is brought into the parameter-updated adaptive filter for next iteration calculation, specifically:

[0036] the product of the response signal vector of the transformer to be calibrated for next iteration and the transpose of the updated filter parameter vector is calculated to obtain the filter output signal value of next iteration.

[0037] In a third aspect, the present application provides an electronic device, including a memory and a processor, the memory stores a computer program, and the processor implements any of the embodiments of the current transformer calibration method when executing the computer program.

[0038] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement any of the embodiments of the current transformer calibration method.

[0039] The present application provides a current transformer calibration method based on least mean square M estimation, which adopts a robust variable step size least mean square M estimation adaptive filter, and the mathematical model adopted is greatly simplified, so that the operation complexity is smaller. In addition, in the filter parameter updating iteration process, the present application adopts a Laplace noise model to fit the impulse noise, which significantly improves the robustness of the system and improves the processing accuracy of the noise.

[0040] Correspondingly, the electronic device and the computer readable storage medium proposed by the present application also have the above technical effects. BRIEF DESCRIPTION OF DRAWINGS

[0041] The accompanying drawings used to provide a further understanding of the embodiments of the present application, constitute a part of the present application, and do not constitute a limitation to the embodiments of the present application. In the drawings:

[0042] Figure 1 a flow chart of the current transformer calibration method proposed by the embodiments of the present application; ​​

[0043] Figure 2 A principle block diagram of a current transformer calibration device proposed by an embodiment of the present application;

[0044] Figure 3 A system architecture diagram of a current transformer calibration system proposed by an embodiment of the present application;

[0045] Figure 4 A schematic diagram of an electronic device proposed by an embodiment of the present application;

[0046] Figure 5 A schematic diagram of a computer readable storage medium proposed by an embodiment of the present application;

[0047] Figure 6 A to-be-calibrated signal waveform diagram generated for a to-be-calibrated transformer;

[0048] Figure 7 A standard signal waveform diagram generated for a standard transformer;

[0049] Figure 8 A to Figure 6 A signal waveform diagram after correction of the to-be-calibrated signal shown in FIG. 8;

[0050] Figure 9 An error analysis result diagram.

[0051] Corresponding component names:

[0052] 200 - current transformer calibration device, 201 - signal acquisition unit, 202 - parameter initialization unit, 203 - iterative calculation unit, 204 - parameter updating unit, 205 - iterative loop unit, 206 - output unit, 300 - current transformer calibration system, 301 - input device, 302 - output device, 303 - processor A, 304 - memory A, 400 - electronic device, 410 - memory B, 420 - processor B, 411 - computer program A, 500 - computer readable storage medium, 511 - computer program B. DETAILED DESCRIPTION

[0053] Hereinafter, the term "include" or "may include" used in various embodiments of the present application indicates the existence of the invented function, operation, or element, and does not limit addition of one or more functions, operations, or elements. In addition, as used in various embodiments of the present application, the terms "include", "have", and their conjugates merely indicate that specific features, numbers, steps, operations, elements, components, or combinations thereof are present, and should not be construed as excluding the presence or addition of one or more other features, numbers, steps, operations, elements, components, or combinations thereof in advance.

[0054] In various embodiments of the present application, the expression "or" or "at least one of A or / and B" includes any and all combinations of the listed terms. For example, the expression "A or B" or "at least one of A or / and B" can include A, can include B, or can include both A and B.

[0055] The expressions used in various embodiments of the present application, such as "first", "second", and the like, can modify various constituent elements in various embodiments, but can not limit the corresponding constituent elements. For example, the above expressions do not limit the order and / or importance of the elements. The above expressions are used only for the purpose of distinguishing one element from another element. For example, the first user device and the second user device indicate different user devices, although both are user devices. For example, a first element can be referred to as a second element, and likewise, a second element can be referred to as a first element, without departing from the scope of various embodiments of the present application.

[0056] It should be noted that if a constituent element is described as being "connected" to another constituent element, the first constituent element can be directly connected to the second constituent element, and a third constituent element can be "connected" between the first constituent element and the second constituent element. Conversely, when a constituent element is "directly connected" to another constituent element, it can be understood that there is no third constituent element between the first constituent element and the second constituent element.

[0057] The terms used in various embodiments of the present application are used only for the purpose of describing particular embodiments and are not intended to limit various embodiments of the present application. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Unless otherwise defined, all terms used herein, including technical terms and scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments of the present application belong. The terms such as those defined in a generally used dictionary will be interpreted to have the same meaning as the contextual meaning in the relevant technical field, and will not be interpreted to have ideal or excessively formal meanings, unless clearly defined in various embodiments of the present application.

[0058] In order to make the purposes, technical solutions, and advantages of the present application more clear, further detailed explanations of the present application are made below in conjunction with embodiments and drawings, the illustrative embodiments of the present application and the explanations thereof are used only for explaining the present application, and do not limit the present application.

[0059] Embodiment 1: The embodiment of the present application proposes a current transformer calibration method based on minimum mean square M estimation. The method extracts response signals from a to-be-calibrated transformer and a standard transformer, and establishes a response signal vector. Then, the parameters of an N-order robust variable step size least mean square M estimation (RVSSLMSM) adaptive filter are initialized, and the initial parameters and the to-be-calibrated transformer response signal vector are input into the adaptive filter to obtain a filter output. An error vector of the adaptive filter output signal vector and the standard transformer response signal vector is calculated, and a variance value is updated. Then, the adaptive filter parameters are updated according to the size of the variance change, and the to-be-calibrated transformer response signal vector is input into the updated adaptive filter. This cycle is repeated until the residual error converges or a certain number of iterations is reached, the optimal filter coefficients are obtained, and the calibration is completed.

[0060] As shown in Figure 1 The current transformer calibration method proposed by the embodiment of the present application includes the following steps:

[0061] Step 1: Collect the response signals of the to-be-calibrated transformer and the standard transformer to the same sweep excitation signal, and generate the response signal vectors of the to-be-calibrated transformer and the standard transformer.

[0062] Step 2: Initialize the N-order robust variable step size least mean square M estimation adaptive filter parameter vector; N is the filter order.

[0063] Step 3: Input the response signal vector of the to-be-calibrated transformer into the initialized adaptive filter for initial iteration calculation, and obtain the filter output signal value.

[0064] Step 4: Calculate the error between the filter output signal value and the corresponding signal value in the response signal vector of the standard transformer, update the variance value, and update the filter parameter vector according to the size of the variance change.

[0065] Step 5: Determine whether the residual error of the filter parameter vector before and after updating converges or the number of iterations reaches a preset value. If yes, output the updated filter parameter vector, complete the calibration of the current transformer, otherwise input the response signal vector of the to-be-calibrated transformer into the adaptive filter with updated parameters for next iteration calculation, obtain the filter output signal value, and return to step 4.

[0066] Further, in step 1 of the embodiment of the present application, the response signal acquisition process of the to-be-calibrated mutual inductor and the standard mutual inductor includes: controlling the excitation signal generating device to simultaneously apply a same sweep excitation signal to the to-be-calibrated mutual inductor and the standard mutual inductor; acquiring the response signal generated by the to-be-calibrated mutual inductor and the response signal generated by the standard mutual inductor, to generate the response signal vector of the to-be-calibrated mutual inductor and the standard mutual inductor. The excitation signal generating device can simulate the transmission signal of the power line in the power system, to ensure the real scene simulation in the calibration process. The excitation signal generating device simultaneously applies a same sweep excitation signal to the to-be-calibrated mutual inductor and the standard mutual inductor, and the signal acquisition and processing module acquires the response signal generated by the to-be-calibrated mutual inductor, i.e., the to-be-calibrated signal , and the response signal generated by the standard mutual inductor, i.e., the standard signal .

[0067] .

[0068] Further, in step 2 of the embodiment of the present application, the parameter vector initialization process is as follows:

[0069] The step size parameter of the N-order robust variable step length minimum mean square M estimation adaptive filter is (preferably 0.001), to control the convergence speed and stability. The initial filter parameter vector of the RVSSLMSM adaptive filter is:

[0070] ;

[0071] The size of the filter parameter vector is .

[0072] The initial variance of the filter noise estimation variance is . The initial filter output signal vector is:

[0073] ;

[0074] The initial value of each iteration output in the filter output signal vector is , and is the signal value taken from the end of the to-be-calibrated signal as the initial value of each iteration output in the filter output signal vector.

[0075] Further, in step 3 of the embodiment of the present application, the initial iteration calculation process is as follows:

[0076] First, the response signal vector of the to-be-calibrated mutual inductor is divided according to the filter order, which can be expressed as: ​​

[0077] ;

[0078] wherein, is the number of iterations, .

[0079] The initial filter parameter vector and the response signal vector of the mutual inductor to be calibrated are brought into the following formula to obtain the output signal value of the initial iteration . .

[0080] ;

[0081] wherein, is the response signal vector of the mutual inductor to be calibrated input in the initial iteration, is the transpose of the initial filter parameter vector .

[0082] Further, in step 4 of the embodiment of the present application, the filter parameter vector updating process is as follows:

[0083] The filter output signal value is subtracted from the corresponding signal value in the response signal vector of the standard mutual inductor to obtain the error :

[0084] ;

[0085] The variance value is updated based on the error , and the variance value updating formula is:

[0086] ;

[0087] At this time, the difference between the updated variance value and the variance value before updating is compared with the size of the threshold value , wherein the threshold value is set according to experience. In the embodiment of the present application, .

[0088] When , it is considered that the current error conforms to the Gaussian distribution, and the filter parameter vector of the next iteration is calculated:

[0089] ;

[0090] When , it is considered that the current error conforms to the non-Gaussian distribution, and the filter parameter vector of the next iteration is calculated:

[0091] ;

[0092] wherein, is the updated filter parameter vector, is the filter parameter vector before updating, is the corresponding signal value in the response signal vector of the standard mutual inductor in the current iteration, is the response signal vector of the to-be-calibrated mutual inductor in the current iteration, is the transpose of, , are parameters of the first mixed Laplace component, is the number of mixed Laplace components, is a sign function, outputting 1 when the value is positive, and outputting -1 when the value is negative. The parameter updating formula of the mixed Laplace distribution is:

[0093] ;

[0094] wherein, is the Hadamard product, , are the parameter vectors of the mixed Laplace distribution before updating, , is the parameter vector of the mixed Laplace distribution after updating, is the expectation vector of the mixed Laplace distribution, the size of the mixed Laplace parameter vector and the expectation vector are . The initial value of the mixed Laplace parameter vector is , and the expectation vector is expressed as:

[0095] ;

[0096] wherein, is the number of mixed Laplace components, which is closely related to the filter performance, the larger the value is, the better the filter performance is, and the higher the calculation complexity is; the smaller the value is, the worse the filter performance is, and the lower the calculation complexity is, and the value is determined according to the accuracy requirement of calibration; the expectation vector can be directly generated, and it is required that and . In the embodiment of the application, .

[0097] then:

[0098] .

[0099] Further, in step 5 of the embodiment of the application, the residual sum is calculated when each round of iteration is completed, and the convergence of the residual sum is determined:

[0100] .

[0101] The residual sum is :

[0102] ;

[0103] Wherein, the updated filter coefficient of the i th is , the filter coefficient of the i th before updating is . When the residual sum has not converged or the number of iterations has not reached

[0104] , the response signal vector of the to-be-calibrated mutual inductor is brought into the adaptive filter with updated parameters for the next iteration, and the filter output signal value is calculated:

[0105] ;

[0106] Wherein, the response signal vector of the to-be-calibrated mutual inductor input for the next iteration is , the updated filter parameter vector is the filter parameter vector for the next iteration, and the transpose of is .

[0107] After obtaining the new filter output signal value, return to step 4 to update the filter parameter vector.

[0108] When the residual sum converges or the number of iterations reaches , the iteration is ended, and the updated filter parameter vector is output as the final robust variable step size least mean square M-estimation adaptive filter coefficient for the calibration of the current transformer.

[0109] Based on the same technical concept, the embodiment of the application also proposes a current transformer calibration device based on least mean square M-estimation, as shown in Figure 2 , the current transformer calibration device 200 comprises:

[0110] A signal acquisition unit 201 is configured to acquire the response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal, and generate the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor. The specific mode is as described in step 1 above, and will not be described here. ​​​

[0111] The parameter initialization unit 202 is configured to initialize an N-order robust variable step-size minimum mean square M-estimation adaptive filter parameter vector; N is a filter order. The specific initialization process is described in step 2 above, and will not be repeated here.

[0112] The iterative calculation unit 203 is configured to bring the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iterative calculation, and obtain a filter output signal value. The specific initial iterative calculation process is described in step 3 above, and will not be repeated here.

[0113] The parameter updating unit 204 is configured to calculate an error between the filter output signal value and a corresponding signal value in the response signal vector of the standard mutual inductor, and update a variance value. According to the size of the variance change amount, the filter parameter vector is updated. The specific filter parameter vector updating process is described in step 4 above, and will not be repeated here.

[0114] In addition, the iterative loop unit 205 is configured to judge whether the residual error of the filter parameter vector before and after updating converges or whether the number of iterations reaches a preset value. If yes, the driving output unit 206 outputs the updated filter parameter vector, and the calibration of the current transformer is completed. Otherwise, the driving iterative calculation unit 203 brings the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter after parameter updating, and performs next iterative calculation to obtain a filter output signal value. The specific evaluation process is described in step 5 above, and will not be repeated here.

[0115] Based on the same technical concept, the embodiment of the present application also proposes a current transformer calibration system based on minimum mean square M-estimation, as shown in the following figure. Figure 3 The current transformer calibration system 300 proposed by the embodiment of the present application comprises:

[0116] an input device 301, an output device 302, a processor A 303 and a memory A 304; wherein the number of the processor A 303 and the memory A 304 can be one or more, Figure 3 for example, one processor A 303 and one memory A 304 are taken as an example for description. The input device 301, the output device 302, the processor A 303 and the memory A 304 can be connected through a bus or other means, Figure 3 for example, a connection through a bus is taken as an example.

[0117] The processor A 303 is configured to execute the following steps by calling the operation instructions stored in the memory A 304.

[0118] collecting response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal, and generating response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor;

[0119] initializing an adaptive filter parameter vector of an Nth order robust variable step size minimum mean square M estimation; N is a filter order;

[0120] bringing the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iteration calculation, to obtain a filter output signal value;

[0121] calculating an error between the filter output signal value and a corresponding signal value in the response signal vector of the standard mutual inductor and updating a variance value, and updating the filter parameter vector according to a size of the variance change amount;

[0122] judging whether the residual errors of the filter parameter vectors before and after the update converge or whether the iteration times reach a preset value, and if so, outputting the updated filter parameter vector, completing the calibration of the current mutual inductor, otherwise, bringing the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter after the parameter update, performing next iteration calculation to obtain a filter output signal value, and returning to the filter parameter vector updating step.

[0123] Optionally, the processor A 303 is further configured to execute any one of the embodiments in the current mutual inductor calibration method by invoking the operation instructions stored in the memory A 304.

[0124] Based on the same technical concept, the embodiments of the present application further provide an electronic device, as shown in the figure, the electronic device 400 comprises a memory B 410, a processor B 420 and a computer program A 411 stored in the memory B 410 and executable on the processor B 420, and the processor B 420 implements the following steps when executing the computer program A 411: Figure 4 collecting response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal to generate response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor;

[0125] initializing an adaptive filter parameter vector of an Nth order robust variable step size minimum mean square M estimation; N is a filter order;

[0126] bringing the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iteration calculation, to obtain a filter output signal value;

[0127] calculating an error between the filter output signal value and a corresponding signal value in the response signal vector of the standard mutual inductor and updating a variance value, and updating the filter parameter vector according to a size of the variance change amount;

[0128] judging whether the residual errors of the filter parameter vectors before and after the update converge or whether the iteration times reach a preset value, and if so, outputting the updated filter parameter vector, completing the calibration of the current mutual inductor, otherwise, bringing the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter after the parameter update, performing next iteration calculation to obtain a filter output signal value, and returning to the filter parameter vector updating step.

[0129] If yes, the updated filter parameter vector is outputted, the calibration of the current transformer is completed, otherwise, the response signal vector of the to-be-calibrated current transformer is brought into the adaptive filter with the updated parameters, the filter output signal value is obtained through next iteration calculation, and the filter parameter vector updating step is returned.

[0130] Optionally, the processor B 420 implements any one of the embodiments in the corresponding embodiments of the current transformer calibration method when executing the computer program A 411.

[0131] It should be noted that the electronic device proposed in the embodiments of the present application is a device used to implement the current transformer calibration method, and therefore based on the current transformer calibration method proposed in the embodiments of the present application, those skilled in the art can understand the specific implementation of the electronic device of the embodiments of the present application and various changes thereof, and therefore the specific implementation of the electronic device to implement the current transformer calibration method is not introduced in detail here, as long as the electronic device used to implement the current transformer calibration method is implemented by those skilled in the art, it belongs to the scope of protection of the present application.

[0132] Based on the same technical concept, the embodiments of the present application also propose a computer readable storage medium, as shown in the computer readable storage medium 500, the computer program B 511 is stored on the computer readable storage medium 500, and the computer program B 511 is executed by the processor to implement the following steps: Figure 5

[0133] The response signals of the to-be-calibrated current transformer and the standard current transformer to the same sweep excitation signal are collected to generate the response signal vectors of the to-be-calibrated current transformer and the standard current transformer;

[0134] The N-order robust variable step-size M-estimate least mean square adaptive filter parameter vector is initialized; N is the filter order;

[0135] The response signal vector of the to-be-calibrated current transformer is brought into the initialized adaptive filter to perform initial iteration calculation to obtain the filter output signal value;

[0136] The error between the filter output signal value and the corresponding signal value in the response signal vector of the standard current transformer is calculated, and the variance value is updated; the filter parameter vector is updated according to the size of the variance change;

[0137] ​The residual error of the filter parameter vector before and after updating is judged whether to converge or the iteration number reaches the preset value, if yes, the updated filter parameter vector is outputted, the calibration of the current transformer is completed, otherwise the response signal vector of the to-be-calibrated transformer is brought into the adaptive filter with updated parameters, the filter output signal value is obtained through next iteration calculation, and the filter parameter vector updating step is returned.

[0138] Optionally, the computer program B511, when executed by the processor, can implement any of the embodiments of the current transformer calibration method.

[0139] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0140] Embodiment 2: The current transformer calibration method proposed in Embodiment 1 is verified by a specific example. Wherein Figure 6 is the to-be-calibrated signal generated by the to-be-calibrated transformer , Figure 7 is the standard signal generated by the standard transformer , after setting the initial parameters of the RVSSLMSM adaptive filter, the iteration loop process of the above current transformer calibration method is processed to obtain the calibrated signal, as shown in Figure 8 , and the final filter coefficient, as shown in Table 1.

[0141] Table 1 final filter coefficient

[0142]

[0143] The error analysis of the calibrated signal is performed, and the error analysis result is shown in Figure 9 . As can be seen from Figure 9 , with the increase of the iteration number, the error gradually tends to zero. In addition, under the interference of pulse error input, the output error value after calibration remains stable without significant fluctuation.

[0144] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can adopt a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0145] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0146] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0147] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 one or more flow or blocks

[0148] The above detailed description merely describes a specific implementation of the application, and the only purpose of the above detailed description is to explain the principles of the application, technical solutions and advantages. It should be understood that the above detailed description is only a specific implementation of the application, and is not intended to limit the protection scope of the application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the application shall be included in the protection scope of the application.

Claims

1. A method for calibrating a current transformer based on minimum mean square M-estimation, characterized by, The method comprises the following steps: collecting response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal, and generating response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor; initializing an N-order robust variable step-size least mean square (M-estimate) adaptive filter parameter vector; bringing the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iteration calculation, and obtaining a filter output signal value; calculating an error between the filter output signal value and a corresponding signal value in the response signal vector of the standard mutual inductor and updating a variance value, and updating the filter parameter vector according to a size of the variance change amount; judging whether a residual error between the updated filter parameter vector and the filter parameter vector before updating converges or whether an iteration number reaches a preset value, and if so, outputting the updated filter parameter vector to complete calibration of the current mutual inductor, otherwise, bringing the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter after parameter updating to perform next iteration calculation, obtaining a filter output signal value, and returning to the filter parameter vector updating step; the updating of the filter parameter vector comprises: performing difference operation on the filter output signal value and a corresponding signal value in the response signal vector of the standard mutual inductor to obtain an error value; updating a filter variance value according to the error value; if a difference between the updated variance value and the variance value before updating is less than or equal to a threshold value, it is considered that the current error conforms to a Gaussian distribution, and a first updating formula is used to calculate the filter parameter vector for next iteration; if the difference between the updated variance value and the variance value before updating is greater than the threshold value, it is considered that the current error conforms to a non-Gaussian distribution, and a second updating formula is used to calculate the filter parameter vector for next iteration; the first updating formula is: ； and / or, the second updating formula is: ； wherein, is the updated filter parameter vector, is the filter parameter vector before update, is the step parameter of the filter, is the variance value before update, is the corresponding signal value in the response signal vector of the standard transformer at the current iteration, is the response signal vector of the transformer to be calibrated at the current iteration, is the number of mixed Laplacian components, , are parameters of the th mixed Laplacian component, is the error value at the current iteration, is the sign function, is the transpose of .

2. The current transformer calibration method based on minimum mean square M estimation according to claim 1, characterized in that, the collecting of the response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal, and the generation of the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor comprises: controlling an excitation signal generating device to simultaneously apply a same sweep excitation signal to the to-be-calibrated mutual inductor and the standard mutual inductor; obtaining response signals generated by the to-be-calibrated mutual inductor and the standard mutual inductor, and generating the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor.

3. The current transformer calibration method based on minimum mean square M estimation according to claim 1, characterized in that, the initialization of the N-order robust variable step-size least mean square (M-estimate) adaptive filter parameter vector comprises: initializing a step-size parameter of the N-order robust variable step-size least mean square (M-estimate) adaptive filter; initializing a filter parameter vector of an Nth order robust variable step-size minimum mean square M-estimation adaptive filter, wherein the filter parameter vector has a size of ​ initializing an output signal vector of the N-order robust variable step-size least mean square (M-estimate) adaptive filter.

4. The current transformer calibration method based on minimum mean square M estimation according to claim 3, characterized in that, the initialization of the output signal vector of the N-order robust variable step-size least mean square (M-estimate) adaptive filter is specifically: taking the last signal value of the response signal vector of the mutual inductor to be calibrated a signal value as an initial value of the output signal vector; wherein, is the length of the response signal vector.

5. The current transformer calibration method based on minimum mean square M estimation according to claim 1, characterized in that, the bringing of the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iteration calculation comprises: dividing the response signal vector of the to-be-calibrated mutual inductor according to an adaptive filter order. The initial filter parameter vector and the divided initial iteration are brought into a filter iteration calculation formula with the response signal vector of the to-be-calibrated mutual inductor to obtain a filter output signal value of the initial iteration; The filter iteration calculation formula is: the filter output signal value is equal to the product of the response signal vector of the to-be-calibrated mutual inductor and the transpose of the filter parameter vector.

6. A current transformer calibration method based on minimum mean square M-estimation according to any one of claims 1-5, characterized in that, The response signal vector of the to-be-calibrated mutual inductor is brought into the parameter-updated adaptive filter to perform next iteration calculation, specifically: The product of the response signal vector of the to-be-calibrated mutual inductor and the transpose of the updated filter parameter vector is calculated for next iteration to obtain a filter output signal value of next iteration. 7.An electronic device comprising a memory and a processor, the memory storing a computer program, wherein, The processor executes the computer program to implement the current transformer calibration method in any one of claims 1-6.

8. A computer-readable storage medium having stored thereon a computer program, characterized in that The computer program is executed by the processor to implement the current transformer calibration method in any one of claims 1-6.

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