High-voltage circuit breaker driving motor parameter identification method and system based on least square method, medium and processor

Abstract

The invention discloses a high-voltage circuit breaker driving motor parameter identification method and system based on a least square method, a medium and a processor, and relates to the technical field of motor driving systems for high-voltage circuit breakers. According to the method, data such as current, voltage, rotor speed and position of a driving motor are collected, a parameter identification model including input, output and parameters is established, and a least square method with a forgetting factor is designed for parameter identification. The size of a forgetting factor in a recursive least square algorithm is dynamically adjusted according to an error of output values of an identification model and an actual model, so that the influence of measured value fluctuation on identification parameters under the working conditions of rotating speed or load sudden change and the like can be inhibited, and estimated values of motor parameters can be quickly converged to actual values under the steady-state working condition; and the accuracy of parameter identification is improved.

Description

Technical Field

[0001] This invention relates to the field of motor inductance parameter identification technology, and in particular to a method, system, medium and processor for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method. Background Technology

[0002] Permanent magnet synchronous motors (PMSMs) are used for contact movement control in high-voltage switchgear due to their high efficiency, high reliability, and high power density. In PMSM vector control systems, the dynamic decoupling of the right-angle and quadrature-axis currents is inextricably linked to the inductance of the motor windings. The inductance parameters of the PMSM undergo difficult-to-measure changes during high-voltage circuit breaker contact operation due to factors such as ambient temperature, affecting the accurate control of the circuit breaker contacts by the PMSM. Rapid and accurate identification of the PMSM inductance parameters significantly improves the control performance of the high-voltage circuit breaker drive system; therefore, accurate and rapid identification of the PMSM inductance parameters is crucial.

[0003] In recent years, a large number of scholars have proposed a number of inductance parameter identification strategies for PMSM parameters from different perspectives. However, the current strategies still have large errors in identification results under operating conditions such as sudden changes in speed or load.

[0004] Therefore, there is a need for a method, system, medium, and processor for identifying parameters of high-voltage circuit breaker drive motors based on the least squares method. Summary of the Invention

[0005] To address the problem of significant errors in identification results under conditions such as sudden changes in speed or load in existing technologies, this invention provides a method, system, medium, and processor for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method. This method addresses the issue of large errors between the output values ​​of the theoretical and actual models under conditions such as sudden changes in speed or load. In such cases, using a larger forgetting factor can suppress fluctuations in measured values. Under steady-state conditions, using a smaller forgetting factor allows the estimated motor parameters to converge quickly to the actual values. The specific technical solution is as follows: A method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method, comprising: S1: Construct a drive motor model that includes the parameters to be identified, input parameters, and output parameters; S2: Extract the parameters to be identified and rewrite the drive motor model into matrix form based on the voltage equation; S3: Convert the matrix-style drive motor model into an initial identification model consisting of three parts: parameters to be identified, input parameters, and output parameters; S4: Based on the initial identification model, a recursive least squares parameter identification model with a dynamic forgetting factor is constructed using the least squares method. S5: Use sensors to collect input and output data, and input the recursive least squares parameter identification model to identify the parameters of the drive motor.

[0006] Furthermore, in step S1, the drive motor model is as follows: ; in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This represents the angular velocity of the motor.

[0007] Furthermore, in step S3, the matrix-type drive motor model is as follows: ; in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This represents the angular velocity of the motor.

[0008] Furthermore, in step S3, the initial identification model is: ; ; ; ; ; ; ; in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This refers to the angular velocity of the motor. , The system output matrix; , Transpose the system input matrix; , The parameter matrix to be identified.

[0009] Furthermore, in step S4, the construction of a recursive least squares parameter identification model with a dynamic forgetting factor based on the initial identification model specifically includes the following steps: S41: Construct a basic identification model based on the least squares method based on the initial identification model; S42: Obtain the static least squares identification model based on the solution objective of the basic identification model; S43: The performance index is corrected by using a recursive least squares method with a forgetting factor to obtain a parameter identification model of recursive least squares method with dynamic forgetting factor.

[0010] Furthermore, in step S41, the basic identification model is as follows: ; In the formula, It is the system output matrix. It is the transpose of the system input matrix. It is the parameter matrix to be identified. It is a noise signal matrix.

[0011] Furthermore, in step S43, the recursive least squares parameter identification model with a dynamic forgetting factor is as follows: ; In the above formula, and Represent the gain matrix and covariance matrix; It is the identity matrix; Indicates the dynamic forgetting factor; Input matrix to the system; The parameter matrix to be identified; subscript and n This indicates the current sampling time and the last sampling time.

[0012] A parameter identification system for a high-voltage circuit breaker drive motor based on the least squares method, applied to the aforementioned parameter identification method for a high-voltage circuit breaker drive motor based on the least squares method, includes: The first building module is used to build a drive motor model that includes parameters to be identified, input parameters, and output parameters; The rewriting module is used to extract the parameters to be identified and rewrite the drive motor model into matrix form based on the voltage equation. The conversion module is used to convert the matrix drive motor model into an initial identification model consisting of three parts: parameters to be identified, input parameters, and output parameters. The second building module is used to construct a recursive least squares parameter identification model with a dynamic forgetting factor based on the initial identification model and using the least squares method. The identification module is used to collect input and output data using sensors and input the recursive least squares parameter identification model to identify the parameters of the drive motor.

[0013] A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the above-described method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method.

[0014] A processor for running a program, wherein the program executes the above-described method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: 1. The least squares method with a forgetting factor is applied to motor parameter identification. The magnitude of the forgetting factor is dynamically adjusted based on the error between the output values ​​of the current theoretical model and the actual model, solving the problem of poor identification results for high-voltage circuit breaker drive motors under conditions such as sudden changes in speed or load. Under conditions such as sudden changes in speed or load, the error between the output values ​​of the theoretical model and the actual model of the high-voltage circuit breaker drive motor is large. In this case, using a larger forgetting factor can suppress the fluctuation of the measured values. Under steady-state conditions, using a smaller forgetting factor allows the estimated values ​​of motor parameters to converge quickly to the actual values. Attached Figure Description

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the accompanying drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0017] Figure 1 This is a flowchart illustrating the first method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method. Figure 2 This is a flowchart illustrating the second method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method. Figure 3This is a schematic diagram of a high-voltage circuit breaker drive motor parameter identification system based on the least squares method. Figure 4 This is a diagram showing the stator inductor identification and verification results in an implementation example of this application; Figure 5 This is a diagram showing the stator resistance identification and verification results in the implementation case of this application; Figure 6 This is a diagram showing the magnetic link identification verification results in the implementation case of this application. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] It should be understood that, when used in this application, the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components, and / or collections thereof.

[0020] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0021] It should also be further understood that the term “and / or” as used in this application refers to any combination of one or more of the associated listed items, as well as all possible combinations, and includes these combinations.

[0022] Example 1 like Figure 1 , Figure 2 This is a flowchart illustrating a method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method, including the following specific steps: S1: Construct a drive motor model that includes the parameters to be identified, input parameters, and output parameters.

[0023] Furthermore, the drive motor model is as follows: ; in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This represents the angular velocity of the motor.

[0024] S2: Parameters to be identified , , and Extract the data and rewrite the drive motor model in matrix form based on the voltage equation, as follows: ; Assuming the entire identification is performed under steady-state conditions, and ignoring the effect of the current rate of change on the identification, i.e. ; S3: Convert the matrix-style drive motor model into an initial identification model consisting of three parts: parameters to be identified, input parameters, and output parameters. The initial identification model is as follows: ; ; ; ; ; ; ; S4: Based on the initial identification model, construct a recursive least squares parameter identification model with a dynamic forgetting factor using the least squares method. The specific steps are as follows: S41: Construct a basic identification model based on the least squares method according to the initial identification model, as shown in the following formula: ; In the formula It is the system output matrix. It is the transpose of the system input matrix. It is the parameter matrix to be identified. It is a noise signal matrix.

[0025] S42: By minimizing the sum of squared errors The core objective is to solve for... Minimum of the parameter matrix to be identified .

[0026] Through the Taking the derivative and setting it to zero, we obtain the static least squares identification model as follows: ; In the above formula, where For the input data matrix, This is for outputting a data matrix.

[0027] The static least squares identification model has two problems: Data saturation: As the amount of data sampled (n) increases, the influence of early data and new data on parameter estimation becomes equal, resulting in the dilution of the correction effect of new data, i.e., data saturation occurs.

[0028] Low computational efficiency: Each new data entry requires recalculation. This involves large-scale matrix operations and is not suitable for real-time identification.

[0029] Therefore, it is necessary to transform the static least squares identification model into a recursive form, update the parameters iteratively, and introduce a "forgetting factor" to strengthen the weight of new data.

[0030] S43: The performance index is corrected by using a recursive least squares method with a forgetting factor to obtain a parameter identification model of recursive least squares method with dynamic forgetting factor.

[0031] 1. To address data saturation, historical data is assigned a decay weight, and the performance metric adjustment includes a forgetting factor: ; in Forgetting factor New data The weight is 1, and the weight of the old data follows... attenuation.

[0032] 2. Recursive calculation of parameter update amount Assuming it has already passed Obtain the parameter matrix from the set of data When the newest one is added Group data At that time, the parameter matrix needs to be updated to According to the least squares principle, the parameter update is proportional to the "prediction error": in, This is the gain matrix, used to adjust the correction magnitude.

[0033] 3. Derive the iterative formulas for the gain matrix G and the covariance matrix P. covariance matrix Defined as The recursive form, i.e. .

[0034] Using the matrix inversion lemma, we can derive: ; ; The above derivations are all based on the matrix operation rules of the static model, the core of which is to... The updates are converted into incremental forms to avoid redundant calculations.

[0035] 4. Eventually, as the amount of data processed increases, the so-called data saturation phenomenon will occur. To prevent this phenomenon, a recursive least squares method with a forgetting factor is used to modify the performance index. The iterative formula of the parameter identification model is as follows: ; subscript in formula and n Indicates the current sampling time and the last sampling time; It is the identity matrix; Indicates the forgetting factor; and This represents the gain matrix and covariance matrix. Because... G From P It is calculated from the middle, therefore it needs to be given P Set the initial vector.

[0036] The initial values ​​for the identification parameter matrix and covariance matrix are set as follows: ; 5. Forgetting Factor This forgetting factor is typically set as a fixed parameter. While a smaller value can improve the algorithm's convergence speed, it significantly reduces its ability to suppress measurement noise; conversely, a larger value will have the opposite effect. Therefore, the selection of the forgetting factor directly affects the parameter identification accuracy of the recursive least squares algorithm. To reconcile this inherent contradiction, a dynamic forgetting factor is introduced. Based on the difference between the output values ​​of the current theoretical model and the actual model To dynamically adjust the size of the forgetting factor: when the error is less than the required control precision When the model error is greater than 1, the forgetting factor is equal to 1; otherwise, it is due to model error. Decide.

[0037] ; The expression for the dynamic forgetting factor is: In the formula, It is an adjustable coefficient. , It refers to control precision.

[0038] The recursive least squares parameter identification model with a dynamic forgetting factor is as follows: ; S5: Use sensors to collect input and output data, and input the recursive least squares parameter identification model to identify the drive motor parameters. .

[0039] The method of collecting input and output data using sensors includes the following steps: S51: Acquires the three-phase current output from the inverter circuit via a current sensor. , and ; S52: Collects motor rotor position information via Hall position sensor; S53: Converts the three-phase current output from the inverter circuit. , and The stator d-axis (direct axis) current component is obtained after abc / dq coordinate transformation. and q-axis (cross-axis) component current ; S54: Transform the motor rotor position information collected by the Hall sensor to obtain the motor speed and rotor position.

[0040] The parameters of the drive motor That is, it includes four parameters. , , and .

[0041] S6: Input the identification results into the PMSM current loop PI controller to update the controller parameters, as shown in the following formula: ; in, For the current loop bandwidth, 、 for dq coordinate system d axis, q Shaft proportionality factor, 、 for dq coordinate system d axis, q Axis integral coefficient.

[0042] That is, the model parameter information of the drive motor in the circuit breaker is obtained by using the recursive least squares method with forgetting factor, and this information is provided to the current controller of the drive motor in the circuit breaker to adjust the parameters of the motor current controller.

[0043] S7: Perform effect verification. Under no-load conditions, the four motor parameters of the permanent magnet synchronous motor are identified using the above recursive least squares parameter identification algorithm. The identified parameter results are then compared with the actual parameter values, and the results are as follows. Figures 4-6 As shown. Four parameters. , , and The identification results are relatively accurate with relatively low error rates, achieving reliable identification accuracy for motor operation control. This parameter identification method can be used to track changes in motor parameters.

[0044] Example 2 like Figure 3 As shown, a parameter identification system for a high-voltage circuit breaker drive motor based on the least squares method is applied to the aforementioned parameter identification method for a high-voltage circuit breaker drive motor based on the least squares method, including: The first building module is used to build a drive motor model that includes parameters to be identified, input parameters, and output parameters; The rewriting module is used to extract the parameters to be identified and rewrite the drive motor model into matrix form based on the voltage equation. The conversion module is used to convert the matrix drive motor model into an initial identification model consisting of three parts: parameters to be identified, input parameters, and output parameters. The second building module is used to construct a recursive least squares parameter identification model with a dynamic forgetting factor based on the initial identification model and using the least squares method. The identification module is used to collect input and output data using sensors and input the recursive least squares parameter identification model to identify the parameters of the drive motor.

[0045] Example 3 A computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device where the computer-readable storage medium is located to execute the above-described method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method.

[0046] Example 4 A processor for running a program, wherein the program executes the above-described method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method.

[0047] This application discloses a method and system for identifying parameters of a high-voltage circuit breaker drive motor based on least squares, relating to the technical field of motor drive systems for high-voltage circuit breakers. The method includes collecting data such as current, voltage, rotor speed, and position of the drive motor in the circuit breaker; establishing a parameter identification model for the high-voltage circuit breaker drive motor comprising three parts: input, output, and parameters; and designing a least squares method with a forgetting factor to identify the parameters of the high-voltage circuit breaker drive motor. This method dynamically adjusts the size of the forgetting factor in the recursive least squares algorithm based on the error between the output values ​​of the identification model and the actual model. On the one hand, this can suppress the influence of large fluctuations in measured values ​​such as current and voltage of the high-voltage circuit breaker drive motor under operating conditions such as sudden changes in speed or load on the identified parameters. On the other hand, using a smaller forgetting factor under steady-state conditions allows the estimated values ​​of the motor parameters to converge quickly to the actual values, improving the accuracy of parameter identification.

[0048] Those skilled in the art will recognize that the units of the various examples described in connection with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the invention.

[0049] In the embodiments provided by the present invention, it should be understood that the division of units is only a logical functional division. In actual implementation, there may be other division methods, such as multiple units can be combined into one unit, one unit can be split into multiple units, or some features can be ignored.

[0050] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0051] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0052] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of this application.

Claims

1. A method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method, characterized in that, include: S1: Construct a drive motor model that includes the parameters to be identified, input parameters, and output parameters; S2: Extract the parameters to be identified and rewrite the drive motor model into matrix form based on the voltage equation; S3: Convert the matrix-style drive motor model into an initial identification model consisting of three parts: parameters to be identified, input parameters, and output parameters; S4: Based on the initial identification model, a recursive least squares parameter identification model with a dynamic forgetting factor is constructed using the least squares method. S5: Use sensors to collect input and output data, and input the recursive least squares parameter identification model to identify the parameters of the drive motor.

2. The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method according to claim 1, characterized in that, In step S1, the drive motor model is as follows: ； in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This represents the angular velocity of the motor.

3. The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method according to claim 1, characterized in that, In step S3, the matrix-type drive motor model is as follows: ； in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This represents the angular velocity of the motor.

4. The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method according to claim 1, characterized in that, In step S3, the initial identification model is: ； ； ； ； ； ； ； in, , These are the d-axis and q-axis components of the stator voltage, respectively. , These are the d-axis and q-axis components of the stator current, respectively. The resistance of the stator; , These are the equivalent inductances for the d-axis and q-axis components, respectively. For permanent magnet flux linkage; This refers to the angular velocity of the motor. , The system output matrix; , Transpose the system input matrix; , The parameter matrix to be identified.

5. The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method according to claim 1, characterized in that, In step S4, the step of constructing a recursive least squares parameter identification model with a dynamic forgetting factor based on the initial identification model specifically includes the following steps: S41: Construct a basic identification model based on the least squares method according to the initial identification model; S42: Obtain the static least squares identification model based on the solution objective of the basic identification model; S43: The performance index is corrected by using a recursive least squares method with a forgetting factor to obtain a parameter identification model of recursive least squares method with dynamic forgetting factor.

6. The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method according to claim 5, characterized in that, In step S41, the basic identification model is as follows: ； In the formula, It is the system output matrix. It is the transpose of the system input matrix. It is the parameter matrix to be identified. It is a noise signal matrix.

7. The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method according to claim 6, characterized in that, In step S43, the recursive least squares parameter identification model with a dynamic forgetting factor is as follows: ； In the above formula, and Represent the gain matrix and covariance matrix; It is the identity matrix; Indicates the dynamic forgetting factor; Input matrix to the system; The parameter matrix to be identified; subscript and n This indicates the current sampling time and the last sampling time.

8. A parameter identification system for a high-voltage circuit breaker drive motor based on the least squares method, characterized in that, The method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method as described in any one of claims 1 to 7 includes: The first building module is used to build a drive motor model that includes parameters to be identified, input parameters, and output parameters; The rewriting module is used to extract the parameters to be identified and rewrite the drive motor model into matrix form based on the voltage equation. The conversion module is used to convert the matrix drive motor model into an initial identification model consisting of three parts: parameters to be identified, input parameters, and output parameters. The second building module is used to construct a recursive least squares parameter identification model with a dynamic forgetting factor based on the initial identification model and using the least squares method. The identification module is used to collect input and output data using sensors and input the recursive least squares parameter identification model to identify the parameters of the drive motor.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored program, wherein, when the program is executed, it controls the device containing the computer-readable storage medium to perform the low-squares method for identifying parameters of a high-voltage circuit breaker drive motor as described in any one of claims 1 to 7.

10. A processor, characterized in that, The processor is used to run a program, wherein the program executes the method for identifying parameters of a high-voltage circuit breaker drive motor based on the least squares method as described in any one of claims 1 to 7.

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