Current transformer calibration method and device based on minimum mean square M estimation and medium

Abstract

The invention 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 comprises the steps that response signals are extracted from a to-be-calibrated transformer and a standard transformer, and response signal vectors are established; parameters of an N-order RVSSLMSM adaptive filter are initialized, the initial parameters and a to-be-calibrated mutual inductor response signal vector are substituted into the adaptive filter to obtain filter output, an error vector of an adaptive filter output signal vector and a standard mutual inductor response signal vector is calculated, and therefore a variance value is updated; and according to the variance variation, updating the parameters of the adaptive filter, substituting the response signal vector of the to-be-calibrated mutual inductor into the updated adaptive filter, and repeating the steps until the residual sum is converged or reaches a certain number of iterations, thereby obtaining the optimal coefficient of the filter and completing the calibration. And the calibration precision 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: A current transformer calibration method based on least mean square M estimation, comprising: Collecting response signals of a to-be-calibrated transformer and a standard transformer to the same sweep excitation signal to generate response signal vectors of the to-be-calibrated transformer and the standard transformer; Initializing an N-order robust variable step size least mean square M estimation adaptive filter parameter vector; the response signal vector of the to-be-calibrated mutual inductor is brought into the initialized adaptive filter to perform initial iteration calculation, and filter output signal values are obtained; errors between the filter output signal values and corresponding signal values in the response signal vector of the standard mutual inductor are calculated, and a variance value is updated; according to a size of a variance change amount, a filter parameter vector is updated; whether the residual error of the updated filter parameter vector and the filter parameter vector before updating converges or whether the iteration number reaches a preset value is judged; if yes, the updated filter parameter vector is output, the calibration of the current mutual inductor is completed, otherwise, the response signal vector of the to-be-calibrated mutual inductor is brought into the adaptive filter after the parameter is updated to perform next iteration calculation, filter output signal values are obtained, and the filter parameter vector updating step is returned.

[0006] 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, and the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor are generated, including: the excitation signal generating device is controlled to simultaneously apply a same sweep excitation signal to the to-be-calibrated mutual inductor and the standard mutual inductor; the response signal generated by the to-be-calibrated mutual inductor and the response signal generated by the standard mutual inductor with the same length are acquired, and the response signal vectors of the to-be-calibrated mutual inductor and the standard mutual inductor are generated.

[0007] In some embodiments, the N-order robust variable step-size minimum mean square M estimation adaptive filter parameter vector is initialized, including: a step-size parameter of the N-order robust variable step-size minimum mean square M estimation adaptive filter is initialized; a filter parameter vector of the N-order robust variable step-size minimum mean square M estimation adaptive filter is initialized, wherein a size of the filter parameter vector is ; an output signal vector of the N-order robust variable step-size minimum mean square M estimation adaptive filter is initialized.

[0008] 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: the last signal values in the response signal vector of the to-be-calibrated mutual inductor are taken as initial values of the output signal vector; wherein is a length of the response signal vector.

[0009] In some embodiments, the response signal vector of the to-be-calibrated mutual inductor is brought into the initialized adaptive filter to perform initial iteration calculation, including: Dividing the response signal vector of the mutual inductor to be calibrated according to the order of the adaptive filter; Substituting the initial filter parameter vector and the response signal vector of the transformer to be calibrated after the initial iteration into the filter iteration calculation formula to obtain the filter output signal value of the initial iteration; The filter iterative calculation formula is: the filter output signal value is equal to the product of the response signal vector of the mutual inductor to be calibrated and the transpose of the filter parameter vector.

[0010] In some implementations, updating the filter parameter vector includes: Performing a 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; According to the error value, updating the variance value of the filter; If the difference between the updated variance value and the variance value before the update is less than or equal to the threshold, the current error is considered to conform to the Gaussian distribution, and the first update formula is used to calculate the filter parameter vector for the next iteration; If the difference between the updated variance value and the variance value before the update is greater than the threshold, it is considered that the current error conforms to the non-Gaussian distribution, and the second update formula is used to calculate and update the filter parameter vector for the next iteration.

[0011] In some implementations, the first update formula is: ;

[0012] And / or, the second update formula is: ;

[0013] in, is the updated filter parameter vector, is the filter parameter vector before updating, is the step size parameter of the filter, is the variance value 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 transformer to be calibrated in the current iteration, is the number of mixed Laplace components, 、 All are The parameters of the mixed Laplace components, is the error value of the current iteration, is a symbolic function, for The transpose of .

[0014] In some embodiments, the response signal vector of the to-be-calibrated mutual inductor is brought into the parameter-updated adaptive filter for 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 to obtain the filter output signal value of the next iteration.

[0015] In a third aspect, the present application provides an electronic device including a memory and a processor, the memory storing a computer program, and the processor implementing any of the above current transformer calibration methods when executing the computer program.

[0016] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, and the computer program is executed by a processor to implement any of the above current transformer calibration methods.

[0017] 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. The mathematical model adopted by the filter is greatly simplified, so the computational complexity is smaller. In addition, in the filter parameter update iteration process, the present application uses 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.

[0018] 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

[0019] The accompanying drawings described herein are used to provide further understanding of the embodiments of the present application, and form a part of the present application, and do not constitute a limitation to the embodiments of the present application. In the drawings: Figure 1 The current transformer calibration method flowchart proposed by the embodiments of the present application; Figure 2 The principle block diagram of the current transformer calibration device proposed by the embodiments of the present application; Figure 3 The current transformer calibration system architecture schematic diagram proposed by the embodiments of the present application; Figure 4 The electronic device schematic diagram proposed by the embodiments of the present application; Figure 5 The computer-readable storage medium schematic diagram proposed by the embodiments of the present application; Figure 6 The to-be-calibrated signal waveform diagram generated for the to-be-calibrated mutual inductor; Figure 7 The standard signal waveform diagram generated for the standard mutual inductor; Figure 8 To correct the signal waveform of the to-be-calibrated signal shown in FIG. 1; Figure 6 To correct the signal waveform of the to-be-calibrated signal shown in FIG. 1; Figure 9 To correct the signal waveform of the to-be-calibrated signal shown in FIG. 1;

[0020] Reference signs and corresponding names of parts: 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

[0021] 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. Also, as used in various embodiments of the present application, the terms "include", "have", and their conjugates merely indicate the presence of specific features, numbers, steps, operations, elements, components, or combinations thereof, 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.

[0022] In various embodiments of the present application, the expression "or" or "at least one of A or / and B" includes any combination of the listed terms or all the combinations thereof. 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.

[0023] The expressions (such as "first", "second", etc.) used in various embodiments of the present application 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 other elements. 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 called a second element, and likewise, a second element can be called a first element without departing from the scope of various embodiments of the present application.

[0024] It should be noted that if a description connects one component element to another component element, the first component element can be directly connected to the second component element, and a third component element can be "connected" between the first component element and the second component element. Conversely, when one component element is "directly connected" to another component element, it can be understood that there is no third component element between the first component element and the second component element.

[0025] The terms used in the various embodiments of the present application are only for the purpose of describing specific embodiments and are not intended to limit the 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 (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the various embodiments of the present application belong. The terms (such as those defined in commonly used dictionaries) will be interpreted as having a meaning that is the same as the contextual meaning in the relevant technical field and will not be interpreted as having an idealized or overly formal meaning, unless clearly defined in the various embodiments of the present application.

[0026] In order to make the purposes, technical solutions and advantages of the present application clearer, further detailed description of the present application is made below in combination with embodiments and drawings, the illustrative embodiments of the present application and the description thereof are only for the purpose of explaining the present application and do not limit the present application.

[0027] Embodiment 1: The present embodiment proposes a current transformer calibration method based on minimum mean square M estimation, which extracts response signals from the to-be-calibrated transformer and the standard transformer, and establishes a response signal vector. Then, the parameters of the 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 above adaptive filter to obtain the filter output, and the error vector of the adaptive filter output signal vector and the standard transformer response signal vector is calculated, so as to update the variance value. Then, according to the size of the variance change, the adaptive filter parameters are updated, and then the to-be-calibrated transformer response signal vector is input 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, and the calibration is completed.

[0028] As shown in Figure 1 The current transformer calibration method proposed in the present embodiment includes the following steps: Step 1, collecting response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal, and generating a response signal vector of the to-be-calibrated mutual inductor and the standard mutual inductor; Step 2, initializing a parameter vector of an N-order robust variable step size least mean square (RVSSLMS) adaptive filter; N is an order of the filter; Step 3, inputting 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; Step 4, 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 parameter vector of the filter according to a size of the variance variation; Step 5, judging whether the residual error of the parameter vector of the filter before and after updating converges or whether the iteration number reaches a preset value, if yes, outputting the updated parameter vector of the filter, completing the calibration of the current mutual inductor, otherwise inputting the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter with the updated parameter to perform next iteration calculation, obtaining the filter output signal value, and returning to Step 4.

[0029] Further, in Step 1 of the embodiment of the application, the response signal collection process of the to-be-calibrated mutual inductor and the standard mutual inductor includes: 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 a response signal generated by the to-be-calibrated mutual inductor and a response signal generated by the standard mutual inductor, and generating a response signal vector of the to-be-calibrated mutual inductor and the standard mutual inductor. The excitation signal generating device can simulate a transmission signal of a power line in a power system, to ensure 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 obtains a response signal generated by the to-be-calibrated mutual inductor, i.e., a to-be-calibrated signal , and a response signal generated by the standard mutual inductor, i.e., a standard signal .

[0030] .

[0031] Further, in Step 2 of the embodiment of the application, the parameter vector initialization process is as follows: The step size parameter of the N-order RVSSLMS adaptive filter is , preferably 0.001, to control the convergence speed and stability, and the initial filter parameter vector of the RVSSLMS adaptive filter is : ;

[0032] Wherein, the filter parameter vector size is .

[0033] The initial variance of the filter noise estimation variance is The initial filter output signal vector is : ;

[0034] Wherein, is the initial value of each iteration output in the filter output signal vector, is the signal value taken from the end of the signal to be calibrated as the initial value of each iteration output in the filter output signal vector.

[0035] Further, in step 3 of the embodiment of the application, the initial iteration calculation process is as follows: First, the response signal vector of the mutual inductor to be calibrated is divided according to the filter order, which can be expressed as: ;

[0036] Wherein, is the number of cycles (iteration times), .

[0037] 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 .

[0038] ;

[0039] Wherein, is the response signal vector of the mutual inductor to be calibrated input by the initial iteration, is the transpose of the initial filter parameter vector .

[0040] Further, in step 4 of the embodiment of the application, the filter parameter vector updating process is as follows: 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 : ;

[0041] Based on the error , the variance value is updated, and the variance value updating formula is: ;

[0042] At this time, the updated variance value the difference between the variance value after the update and the threshold value , wherein the threshold value is set empirically. In the embodiment of the present application, .

[0043] When , it is considered that the current error conforms to the Gaussian distribution, and the filter parameter vector for the next iteration is calculated: ;

[0044] When , it is considered that the current error conforms to the non-Gaussian distribution, and the filter parameter vector for the next iteration is calculated: ;

[0045] wherein, is the updated filter parameter vector, is the filter parameter vector before the update, 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, which outputs 1 when the value is positive, and outputs -1 when the value is negative. The parameter update formula of the mixed Laplace distribution is: ;

[0046] wherein, is the Hadamard product, , are the parameter vectors of the mixed Laplace distribution before the update, , is the parameter vector of the mixed Laplace distribution after the update, is the expectation vector of the mixed Laplace distribution, and the size of the mixed Laplace parameter vector and the expectation vector is . The initial value of the mixed Laplace parameter vector , and the expectation vector are represented as: ;

[0047] wherein, is the number of mixed Laplace components, which is closely related to the filter performance, The larger the value, the better the filter performance, but the higher the computational complexity; The smaller the value, the worse the filter performance and the lower the computational complexity, which is determined according to the accuracy requirements of the calibration; the expected vector Can be generated directly, need to ensure and In the embodiments of this application, .

[0048] Then we have: .

[0049] Furthermore, in step 5 of the embodiment of the present application, the residual sum is calculated upon completion of each cycle (iteration), and the convergence of the residual sum is determined: .

[0050] Residual and for: ; in, After the update filter coefficients, Before the update filter coefficients.

[0051] When the residual and No convergence or the number of cycles has not been reached , then the response signal vector of the mutual inductor to be calibrated is brought into the adaptive filter after parameter update for the next iteration, and the filter output signal value is calculated : ;

[0052] in, The response signal vector of the transformer to be calibrated is input for the next iteration. is the updated filter parameter vector, that is, the filter parameter vector for the next iteration, for The transpose of .

[0053] After obtaining the new filter output signal value, return to step 4 to update the filter parameter vector.

[0054] When the residual and Convergence or number of cycles reached , end the loop, and output the updated filter parameter vector as the final robust variable step size least mean square M estimation adaptive filter coefficient to calibrate the current transformer.

[0055] Based on the same technical concept, the application further provides a current transformer calibration device based on minimum mean square M estimation, as shown in the figure, the current transformer calibration device 200 comprises: Figure 2 The signal acquisition unit 201 is configured to acquire response signals of the to-be-calibrated current transformer and the standard current transformer to the same sweep excitation signal, and generate a response signal vector of the to-be-calibrated current transformer and the standard current transformer. The specific mode is as described in step 1 above, and will not be repeated here.

[0056] 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 the filter order. The specific initialization process is as described in step 2 above, and will not be repeated here.

[0057] The iteration calculation unit 203 is configured to bring the response signal vector of the to-be-calibrated current transformer into the initialized adaptive filter to perform initial iteration calculation, and obtain a filter output signal value. The specific initial iteration calculation process is as described in step 3 above, and will not be repeated here.

[0058] 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 current transformer, update a variance value, and update the filter parameter vector according to the size of the variance change. The specific filter parameter vector updating process is as described in step 4 above, and will not be repeated here.

[0059] In addition, the iteration loop unit 205 is configured to judge whether the residual error of the filter parameter vector before and after updating converges or the iteration number reaches a preset value, and if so, drive the output unit 206 to output the updated filter parameter vector, complete the calibration of the current transformer, otherwise drive the iteration calculation unit 203 to bring the response signal vector of the to-be-calibrated current transformer into the adaptive filter after parameter updating, and perform next iteration calculation to obtain a filter output signal value. The specific evaluation process is as described in step 5 above, and will not be repeated here.

[0060] Based on the same technical concept, the application further provides a current transformer calibration system based on minimum mean square M estimation, as shown in the figure, the current transformer calibration system 300 comprises: Figure 3 The input device 301, the output device 302, the processor A 303 and the memory A 304; wherein the number of the processor A 303 and the memory A 304 can be one or more, Figure 3 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 The bus connection is taken as an example.

[0061] The processor A 303 is configured to execute the following steps by invoking the operation instructions stored in the memory A 304: 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; Initializing an N-order robust variable step-size least mean square M-estimate adaptive filter parameter vector; N is a filter order; 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; 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; 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 to complete the calibration of the current mutual inductor, or otherwise bringing the response signal vector of the to-be-calibrated mutual inductor into the adaptive filter with the updated parameters to perform next iteration calculation to obtain the filter output signal value and returning to the filter parameter vector updating step.

[0062] Optionally, the processor A 303 is further configured to execute any one of the embodiments in the above current mutual inductor calibration method by invoking the operation instructions stored in the memory A 304.

[0063] Based on the same technical concept, the embodiments of the present application further provide an electronic device, as shown in the figure. Figure 4 The electronic device 400 includes 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: 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; Initializing an N-order robust variable step-size least mean square M-estimate adaptive filter parameter vector; N is a filter order; 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; 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; If yes, the updated filter parameter vector is outputted, and 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, and the filter output signal value is obtained through next iteration calculation, and the filter parameter vector updating step is returned.

[0064] Optionally, the processor B 420, when executing the computer program A 411, can implement any of the implementation manners in the corresponding embodiments of the current transformer calibration method.

[0065] 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 manners 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.

[0066] Based on the same technical concept, the embodiments of the present application also propose a computer readable storage medium, as shown in the figure, the computer readable storage medium 500 stores a computer program B 511, and the computer program B 511 is executed by a processor to implement the following steps: Figure 5 The response signal vectors of the to-be-calibrated current transformer and the standard current transformer are generated by collecting the response signals of the to-be-calibrated current transformer and the standard current transformer to the same sweep excitation signal; The N-order robust variable step-size M-estimate least mean square adaptive filter parameter vector is initialized; N is the filter order; The response signal vector of the to-be-calibrated current transformer is brought into the initialized adaptive filter to perform initial iteration calculation, and the filter output signal value is obtained; 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; and the filter parameter vector is updated according to the size of the variance change; 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; and the filter parameter vector is updated according to the size of the variance change; If yes, the updated filter parameter vector is outputted, and 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, and the filter output signal value is obtained through next iteration calculation, and the filter parameter vector updating step is returned.

[0067] Optionally, the computer program B511, when executed by the processor, can implement any of the embodiments of the current transformer calibration method described above.

[0068] 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.

[0069] Embodiment 2: The current transformer calibration method proposed in Embodiment 1 is verified by a specific example in this embodiment. 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-mentioned current transformer calibration method is processed to obtain the calibrated signal, as shown in Figure 8 , and the final filter coefficients, as shown in Table 1.

[0070] Table 1 final filter coefficients

[0071] 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 number of iterations, 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.

[0072] 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 take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can take 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.

[0073] 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

[0074] 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

[0075] 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

[0076] The above detailed description merely describes preferred embodiments of the application, and is not intended to limit the scope of the application. The application is described with reference to the attached drawings, and is open to alterations and modifications. The detailed description is presented primarily for the purpose of enabling others skilled in the art to make and use the application, and is not intended to limit the scope of the application as set forth in the appended claims.

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 a response signal vector 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; if yes, outputting the updated filter parameter vector, completing 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.

2. The current transformer calibration method based on minimum mean square M estimation according to claim 1, characterized in that, The method of collecting the response signals of the to-be-calibrated mutual inductor and the standard mutual inductor to the same sweep excitation signal, and generating the response signal vector of the to-be-calibrated mutual inductor and the standard mutual inductor comprises the following steps: 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 a response signal generated by the to-be-calibrated mutual inductor and a response signal generated by the standard mutual inductor, and generating the response signal vector 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 method of initializing the N-order robust variable step-size least mean square (M-estimate) adaptive filter parameter vector comprises the following steps: initializing a step-size parameter of the N-order robust variable step-size least mean square (M-estimate) adaptive filter; Initialize the filter parameter vector of the N-order robust variable step size least mean square M-estimation adaptive filter, where the size of the filter parameter vector is ; 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 method of initializing the output signal vector of the N-order robust variable step-size least mean square (M-estimate) adaptive filter comprises the following steps: taking the last signal value of a response signal vector of the mutual inductor to be calibrated as an initial value of the output signal vector; wherein the length of the response signal vector is the length of the response signal vector is 5. The current transformer calibration method based on minimum mean square M estimation according to claim 1, characterized in that, The method of bringing the response signal vector of the to-be-calibrated mutual inductor into the initialized adaptive filter to perform initial iteration calculation comprises the following steps: dividing the response signal vector of the to-be-calibrated mutual inductor according to an order of the adaptive filter; bringing the initial filter parameter vector and the divided initial iteration into a filter iteration calculation formula using the response signal vector of the to-be-calibrated mutual inductor, to obtain an initial iteration filter output signal value; The filter iteration calculation formula is: the filter output signal value is equal to a product of the response signal vector of the to-be-calibrated mutual inductor and a 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 method of updating the filter parameter vector comprises the following steps: 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 variance value of the filter 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 the non-Gaussian distribution, and the filter parameter vector for next iteration is calculated by using the second updating formula.

7. The current transformer calibration method based on minimum mean square M estimation according to claim 6, characterized in that, The first updating formula is: ； And / or, the second updating formula is: ； in, is the updated filter parameter vector, is the filter parameter vector before updating, is the step size parameter of the filter, is the variance value 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 transformer to be calibrated in the current iteration, is the number of mixed Laplace components, 、 All are The parameters of the mixed Laplace components, is the error value of the current iteration, is a symbolic function, for The transpose of .

8. The current transformer calibration method based on minimum mean square M estimation according to claim 7, characterized in that, The response signal vector of the to-be-calibrated mutual inductor is brought into the parameter-updated adaptive filter for 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 to obtain the filter output signal value for next iteration.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: The processor executes the computer program to implement the current transformer calibration method in any one of claims 1-7.

10. 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 1-7.

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