Motor parameter identification method and device based on observability analysis and related equipment

Abstract

The embodiment of the invention provides a motor parameter identification method and device based on observability analysis and related equipment, the method is used for performing parameter identification on a permanent magnet synchronous motor, and the method comprises the following steps: firstly, obtaining a motor parameter and a target identification parameter of the permanent magnet synchronous motor; substituting the motor parameters and the target identification parameters into a voltage equation corresponding to the permanent magnet synchronous motor, and establishing a target function; determining a Hessian matrix corresponding to the target function as a target matrix; calculating a target condition number corresponding to the target matrix according to the target matrix; when the target matrix and the target condition number meet a preset observation condition, determining that the target identification parameter is an observable parameter; solving the target function based on the target matrix to obtain a first identification result; and updating the value of the target identification parameter in the permanent magnet synchronous motor as the first identification result. According to the method, the parameter observable conditions can be systematically analyzed, so that the accuracy and reliability of parameter identification are improved.

Description

Technical Field

[0001] The present application relates to the field of permanent magnet synchronous motors, and in particular to a motor parameter identification method, device and related equipment based on energy observability analysis. Background Art

[0002] Permanent Magnet Synchronous Motors (PMSMs) are synchronous motors that use permanent magnet materials to generate magnetic fields. They are widely used due to their high efficiency, wide speed range, and good dynamic response characteristics. In order to achieve high-performance control of permanent magnet synchronous motors, accurate motor parameters are required. However, changing operating conditions (and environment) can cause motor parameters to fluctuate around standard values. Therefore, obtaining the dynamic changes of motor parameters is essential for precise control.

[0003] In the related art, many parameter estimation methods have been proposed, including numerical methods, observer-based methods and artificial intelligence-based methods. However, these methods all have the problem of insufficient analysis of the observable conditions of the parameters, which affects the accuracy and reliability of parameter identification. Summary of the invention

[0004] The embodiments of the present application provide a motor parameter identification method, apparatus and related equipment based on observability analysis, which systematically analyzes the observability conditions of parameters, thereby improving the accuracy and reliability of parameter identification.

[0005] To achieve the above-mentioned purpose, a first aspect of an embodiment of the present application proposes a motor parameter identification method based on observability analysis, the method is used to perform parameter identification on a permanent magnet synchronous motor, the method comprising:

[0006] Acquiring motor parameters and target identification parameters of the permanent magnet synchronous motor;

[0007] Substituting the motor parameters and the target identification parameters into the voltage equation corresponding to the permanent magnet synchronous motor to establish an objective function;

[0008] Determine the Hessian matrix corresponding to the objective function as the target matrix;

[0009] Calculate the target condition number corresponding to the target matrix according to the target matrix;

[0010] When the target matrix and the target condition number meet a preset observation condition, determining that the target identification parameter is an observable parameter;

[0011] Solving the objective function based on the objective matrix to obtain a first identification result;

[0012] The value of the target identification parameter in the permanent magnet synchronous motor is updated to the first identification result.

[0013] In some embodiments, calculating the target condition number corresponding to the target matrix according to the target matrix includes:

[0014] Calculate the maximum eigenvalue and the minimum eigenvalue of the target matrix;

[0015] The target condition number is calculated according to the maximum eigenvalue and the minimum eigenvalue.

[0016] In some embodiments, the preset observation condition includes an observable threshold, and when the target matrix and the target condition number meet the preset observation condition, determining that the target identification parameter is an observable parameter includes:

[0017] Calculate the determinant of the target matrix;

[0018] When the target condition number is less than the observable threshold and the determinant is not equal to zero, it is determined that the target matrix and the target condition number satisfy the observation condition, and the target identification parameter is determined to be an observable parameter.

[0019] In some embodiments, solving the objective function based on the objective matrix to obtain a first identification result includes:

[0020] Initializing the target recognition parameters;

[0021] Calculating the first-order derivative of the objective function with respect to the target identification parameter to obtain a Jacobian matrix;

[0022] Calculate a target inverse matrix of the target matrix;

[0023] Calculate the parameter correction amount of the current iteration round according to the target inverse matrix and the Jacobian matrix;

[0024] updating the target identification parameter based on the parameter correction amount;

[0025] In response to the updated target identification parameters not satisfying the convergence condition, returning to calculate the first-order derivative of the target function with respect to the target identification parameters according to the updated target identification parameters until the target identification parameters satisfy a preset convergence condition;

[0026] In response to the updated target identification parameters satisfying the convergence condition, the updated target identification parameters are determined as the first identification result.

[0027] In some embodiments, the motor parameter of the permanent magnet synchronous motor includes a direct axis current, and after calculating the target condition number corresponding to the target matrix according to the target matrix, the method further includes:

[0028] When the target condition number does not satisfy the preset observation condition, adjusting the direct axis current, and updating the objective function and the target condition number based on the adjusted direct axis current, so that the target condition number satisfies the preset observation condition;

[0029] Solving the updated objective function based on Newton's iteration method to obtain a second identification result of the target identification parameter;

[0030] The value of the target identification parameter in the permanent magnet synchronous motor is updated to the second identification result.

[0031] In some embodiments, after substituting the target matrix and the target condition number into the voltage equation corresponding to the permanent magnet synchronous motor to establish the target function, the method further includes:

[0032] Acquiring steady-state operation data of the permanent magnet synchronous motor;

[0033] Based on the voltage equation of the permanent magnet synchronous motor, the objective function is corrected according to the steady-state operation data and the target identification parameters to update the objective function.

[0034] In some embodiments, the target identification parameter is the direct axis inductance And quadrature inductance When , the objective function is a quadratic objective function, and the objective function is defined as:

[0035]

[0036] Among them, u d is the direct axis voltage, u q is the quadrature axis voltage, i d is the direct axis current, i q is the quadrature axis current, R is the resistance, is the flux linkage parameter, p is the derivative operator, ω e is the motor speed.

[0037] To achieve the above purpose, a second aspect of an embodiment of the present application proposes a motor parameter identification system based on observability analysis, comprising:

[0038] An acquisition unit, used to acquire motor parameters and target identification parameters of the permanent magnet synchronous motor;

[0039] An establishing unit, configured to establish an objective function based on a voltage equation of the permanent magnet synchronous motor and according to the motor parameters and the target identification parameters;

[0040] A first determining unit is used to determine the Hessian matrix corresponding to the objective function as a target matrix;

[0041] A calculation unit, used for calculating a target condition number corresponding to the target matrix according to the target matrix;

[0042] a second determination unit, configured to determine that the target identification parameter is an observable parameter when the target matrix and the target condition number satisfy a preset observation condition;

[0043] A solving unit, solving the objective function based on the objective matrix to obtain a first identification result;

[0044] An updating unit is used to update the value of the target identification parameter in the permanent magnet synchronous motor to the first identification result.

[0045] To achieve the above-mentioned purpose, the third aspect of an embodiment of the present application proposes an electronic device, which includes a memory and a processor, the memory stores a computer program, and when the processor executes the computer program, it implements the motor parameter identification method based on observability analysis as described in the first aspect.

[0046] To achieve the above-mentioned purpose, the fourth aspect of an embodiment of the present application proposes a storage medium, which is a computer-readable storage medium, and the storage medium stores a computer program. When the computer program is executed by a processor, it implements the motor parameter identification method based on energy observability analysis as described in the first aspect.

[0047] The embodiments of the present application propose a motor parameter identification method, device and related equipment based on observability analysis. The method is used to identify the parameters of a permanent magnet synchronous motor. The method first obtains the motor parameters and target identification parameters of the permanent magnet synchronous motor; substitutes the motor parameters and target identification parameters into the voltage equation corresponding to the permanent magnet synchronous motor to establish an objective function; determines the Hessian matrix corresponding to the objective function as the target matrix; calculates the target condition number corresponding to the target matrix according to the target matrix; when the target matrix and the target condition number meet the preset observation conditions, determines that the target identification parameters are observable parameters; solves the objective function based on the target matrix to obtain a first identification result; and updates the numerical value of the target identification parameter in the permanent magnet synchronous motor to the first identification result.

[0048] After establishing the objective function, the embodiment of the present application calculates the Hessian matrix corresponding to the objective function, and determines the Hessian matrix as the target matrix. By analyzing the characteristics of the target matrix and the condition number, it is judged whether the target identification parameters meet the observation conditions under the current conditions. When the observation conditions are met, the objective function is solved based on the target matrix to obtain the first identification result. This identification result is an accurate and reliable identification result verified by the observation conditions. According to the identification result, the corresponding numerical value in the permanent magnet synchronous motor is updated. Through the above process, the observable conditions of the parameters can be systematically analyzed, thereby improving the accuracy and reliability of parameter identification.

[0049] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A flow chart of a motor parameter identification method based on observability analysis provided in one embodiment of the present application;

[0051] Figure 2 An embodiment of the present application provides Figure 1 Flow chart of step 104;

[0052] Figure 3 An embodiment of the present application provides Figure 1 Flow chart of step 105;

[0053] Figure 4 An embodiment of the present application provides Figure 1 Flowchart of step 106;

[0054] Figure 5 An embodiment of the present application provides Figure 1 The flowchart after step 104;

[0055] Figure 6 An embodiment of the present application provides Figure 1 The flowchart after step 102

[0056] Figure 7 A schematic diagram of a motor parameter identification device based on observability analysis provided in one embodiment of the present application;

[0057] Figure 8 A schematic diagram of a motor parameter identification system based on observability analysis provided in one embodiment of the present application;

[0058] Fig. 9A schematic diagram of the hardware structure of an electronic device provided in yet another embodiment of the present application. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] It should be noted that although the functional modules are divided in the device schematic and the logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] First, some nouns involved in this application are analyzed:

[0063] Permanent Magnet Synchronous Motor (PMSM): A permanent magnet synchronous motor is a synchronous motor that uses permanent magnets to generate a magnetic field. Its main features include high efficiency, good speed control performance, and a wide speed operating range. In a PMSM, permanent magnets are usually mounted on the rotor, while the stator contains the windings. When current passes through the stator windings, a rotating magnetic field is generated, which interacts with the permanent magnets to generate torque and drive the motor to rotate.

[0064] Hessian matrix: In the calculus of multivariable functions, the Hessian matrix (or second-order derivative matrix) is a square matrix whose elements are the second-order partial derivatives of the original function. For a function f(x1, x2, ..., x n ), whose Hessian matrix H is defined as:

[0065]

[0066] Permanent magnet synchronous motors (PMSMs) are widely used in industrial and commercial applications due to their high efficiency, wide speed range, and excellent dynamic performance. These motors are designed to achieve high power conversion efficiency, reduce energy losses, and adapt to a wide range of operating conditions from low speed to high speed, showing fast response capabilities and precise speed and position control. However, a key factor for PMSM to achieve its excellent control performance is to obtain accurate motor parameters. Motor parameters, including resistance, inductance, speed, etc., directly affect the dynamic response of the motor and the effectiveness of the control strategy.

[0067] However, motors encounter constantly changing operating conditions in actual applications, such as changes in load and speed, which can cause fluctuations in motor parameters. In addition, environmental factors such as temperature changes can also affect the properties of motor materials, which in turn affect motor parameters. These parameter fluctuations make it difficult to accurately measure and control them. In order to achieve precise control, it is necessary to be able to monitor and adapt to the dynamic changes of motor parameters in real time, which is a major challenge for traditional parameter estimation methods.

[0068] Existing technologies can be divided into three categories: numerical methods, observer-based methods, and artificial intelligence (AI)-based methods. Numerical methods rely on motor models and numerical calculations, and usually use the numerical values ​​of motor parameters directly in the control algorithm; observer-based methods use the input and output data of the motor to estimate the internal state and parameters. This method is particularly important in motor control because it can provide real-time parameter estimation; and AI-based methods, such as neural networks, can predict motor parameters by learning a large amount of data, with strong adaptability, especially for complex nonlinear systems. Although these methods can successfully obtain accurate parameter values ​​in specific cases, they are insufficient in the analysis of observable conditions for parameter identification. Observability refers to whether the internal state and parameters of the system can be uniquely determined by external measurable data. If the observable conditions are not analyzed, the accuracy of parameter estimation will be reduced. Some researchers have tried to estimate resistance and inductance using only the operating data of the motor without introducing additional signals, and have also used high-frequency injection technology to simultaneously estimate resistance and inductance through a full-state observer. This method can improve the accuracy of parameter estimation to a certain extent, but it also increases the complexity and difficulty of implementation of the system.

[0069] The above methods often produce uncertain results in the analysis of the consistency of observable conditions and the observability and degree of motor parameters. This means that in different studies, there may be differences in the observability evaluation of the same parameter set, which poses a challenge to the accuracy and reliability of motor parameter identification. In addition, existing technologies rarely consider the impact of noise and interference in actual systems, which may lead to instability and inaccuracy in parameter estimation in practical applications.

[0070] In order to overcome these limitations, the present invention proposes a permanent magnet synchronous motor parameter identification method based on observability analysis. This method not only considers the observability of motor parameters, but also takes into account the robustness of parameter estimation under actual operating conditions, including noise and interference factors. By constructing the Hessian matrix of the objective function and calculating its condition number, the present invention can evaluate the observable conditions of parameter identification and ensure that stable and reliable parameter estimation results can be obtained under various operating conditions.

[0071] Reference Figure 1 , is an optional flow chart of a motor parameter identification method based on observability analysis provided in an embodiment of the present application, Figure 1 The method may include but is not limited to steps 101 to 107. It can be understood that this embodiment Figure 1 The method may include but is not limited to steps 101 to 107, and the order is not specifically limited. The order of the steps may be adjusted or some steps may be reduced or added according to actual needs.

[0072] Step 101: Obtain motor parameters and target identification parameters of a permanent magnet synchronous motor.

[0073] Step 101 is described in detail below.

[0074] In some embodiments, relevant motor parameter data is collected from a permanent magnet synchronous motor (PMSM). This data may include, but is not limited to, the motor's voltage, current, speed, and other operating parameters that may affect the motor's performance and control. After collecting the necessary motor parameter data, it is then necessary to determine which parameters are the target parameters of this identification process. The target parameters may include the motor's resistance, inductance, flux linkage parameters, etc.

[0075] In some embodiments, before starting the identification process, the target parameter may be initialized to set an initial estimated value, which may be based on rated data of the motor or previous experience.

[0076] Step 102: Substitute the motor parameters and the target identification parameters into the voltage equation corresponding to the permanent magnet synchronous motor to establish the target function.

[0077] Step 102 is described in detail below.

[0078] In some embodiments, after the motor parameters and target identification parameters of the permanent magnet synchronous motor are obtained in step 101, first, the voltage equation of the permanent magnet synchronous motor is used as follows:

[0079]

[0080] Among them, u dis the direct axis voltage, u q is the quadrature axis voltage, i d is the direct axis current, i q is the quadrature axis current, R is the resistance, is the flux linkage parameter, p is the derivative operator, ω e is the motor speed.

[0081] Substitute the motor parameters and target identification parameters collected in step 101 into the voltage equation to establish a quadratic objective function. The following takes the direct-axis inductance and quadrature-axis inductance as the identification targets as an example. Other parameter combinations can be analyzed using the same method. The quadratic objective function is defined as:

[0082]

[0083] in,

[0084]

[0085] As can be seen from the above formula, ideally, if the actual values ​​of all parameters of the motor can be accurately known and the observed current and voltage data are not affected by any noise or interference, then the value of this objective function should be zero. This is because all predicted voltages will match the actual observed voltages exactly and the error terms will be eliminated. However, in reality, due to the influence of measurement noise, system uncertainty and other factors, the value of the objective function may not be zero. The value of the objective function is greater than zero, reflecting the deviation between the predicted value and the actual observed value. Therefore, the convergence of the objective function is a key indicator for evaluating the observability of the parameters. If the objective function can converge to a smaller value during the optimization process, this indicates that the parameters of the motor can be accurately estimated through the current observation data and operating conditions, that is, the parameters are observable. On the contrary, if the objective function cannot converge to the minimum value, or the convergence speed is very slow, this indicates that some parameters are unobservable or difficult to accurately estimate under given operating conditions. This may be due to a variety of reasons, including poor quality of measurement data, high system noise level, strong coupling effects between parameters, or the selected operating conditions do not provide enough information to uniquely determine the parameter value.

[0086] Step 103: Determine the Hessian matrix corresponding to the objective function as the target matrix.

[0087] Step 103 is described in detail below.

[0088] The Hessian matrix is ​​a second-order partial derivative matrix of a multivariable function, which describes the local curvature of the function at a specific point. For the target function, its Hessian matrix contains the second-order partial derivatives of the target function with respect to the target identification parameter. For example, when the target identification parameter is the direct-axis inductance And quadrature inductance When , the Hessian matrix corresponding to the objective function is expressed as:

[0089]

[0090] in,

[0091]

[0092] Among them, the Hessian matrix indicates whether the function can converge to the optimal value quickly. The eigenvalues ​​of the Hessian matrix can provide important information about the curvature of the function, thereby affecting the convergence performance of the optimization algorithm. If the Hessian matrix is ​​positive definite (all eigenvalues ​​are positive), the point is a local minimum; if it is negative definite (all eigenvalues ​​are negative), the point is a local maximum. If the Hessian matrix has both positive and negative eigenvalues, the point is a saddle point, which means that the function value increases in some directions and decreases in other directions. For parameter estimation problems, a well-conditioned Hessian matrix means that the estimation process is more likely to converge to the true parameter value. Therefore, when designing a parameter estimation algorithm, it is very important to ensure the good conditioning of the Hessian matrix.

[0093] Step 104: Calculate the target condition number corresponding to the target matrix according to the target matrix.

[0094] Step 104 is described in detail below.

[0095] First, the condition number is a measure of the sensitivity of a problem to changes in the input data. A high condition number makes the system sensitive to parameter changes, where small noise can lead to significant changes in the results. Therefore, information matrices with high condition numbers are often considered unstable or ill-conditioned. Mathematically, the condition number of a matrix A is defined as:

[0096]

[0097] Among them, ||A|| is the L2 norm of matrix A. From the definition of the condition number, it can be seen that when the information matrix is ​​complex, it is a complicated process to determine whether it is an ill-conditioned matrix. However, if the matrix A is a symmetric matrix, the definition of its condition number will become simple, and its form is:

[0098]

[0099] Among them, λ(A) is the eigenvalue of matrix A, max() is the maximum eigenvalue, and min() is the minimum eigenvalue.

[0100] Since the Hessian matrix is ​​symmetric, there is no need to consider the transpose of the Hessian matrix when calculating the condition number, which greatly reduces the complexity of the calculation. A key property of symmetric matrices is that their eigenvalues ​​are all real numbers, which simplifies many mathematical processes, including the calculation of the condition number.

[0101] Therefore, refer to Figure 2 In some embodiments, step 104 includes but is not limited to steps 201 to 202.

[0102] Step 201: Calculate the maximum eigenvalue and the minimum eigenvalue of the target matrix.

[0103] Step 202: Calculate the target condition number according to the maximum eigenvalue and the minimum eigenvalue.

[0104] Step 201 to step 202 are described in detail below.

[0105] Among them, step 201 involves calculating the maximum and minimum eigenvalues ​​of the target matrix. Since the Hessian matrix is ​​symmetric, these eigenvalues ​​can be found by numerical methods, including but not limited to power iteration. After finding the maximum and minimum eigenvalues, in step 202, the target condition number can be calculated based on the maximum and minimum eigenvalues ​​obtained in step 201. The calculation formula of the condition number is the maximum eigenvalue divided by the minimum eigenvalue. This ratio provides a quantitative measure that describes the sensitivity of the matrix to input changes. If the condition number is low, this usually indicates that the target matrix is ​​stable and the parameter identification process may be more reliable. On the contrary, if the condition number is high, measures may need to be taken to improve the condition of the matrix, such as by regularization or changing the parameterization, to improve the stability and accuracy of parameter identification.

[0106] Step 105: When the target matrix and the target condition number meet the preset observation conditions, the target identification parameters are determined to be observable parameters.

[0107] Step 105 is described in detail below.

[0108] Among them, before performing parameter identification, it is necessary to set some observation conditions, which can be based on experience, theoretical analysis or previous research, and can include but are not limited to the threshold of the target condition number, the rank of the target matrix, etc. The target condition number calculated in step 104 will be compared with the preset condition number threshold. If the condition number is lower than this threshold, it indicates that the target matrix is ​​"good" enough, that is, it is not too sensitive to changes in input data. If the condition number of the target matrix meets the preset observation conditions, this means that under the current measurement and operating conditions, the target identification parameters can be uniquely determined by the measurement data, that is, the parameters are observable.

[0109] Reference Figure 3 In some embodiments, the preset observation condition includes an observable threshold, and step 105 includes but is not limited to steps 301 to 302.

[0110] Step 301: Calculate the determinant of the target matrix.

[0111] Step 302: When the target condition number is less than the observable threshold and the determinant is not equal to zero, it is determined that the target matrix and the target condition number meet the observation condition, and the target identification parameters are determined to be observable parameters.

[0112] Step 301 - step 302 are described in detail below.

[0113] In order to avoid the target matrix being ill-conditioned, the following observable conditions should be met:

[0114]

[0115] Among them, rank(H) indicates that the target quadratic matrix is ​​a full rank matrix, and Cond(H) indicates that the condition number of the target matrix is ​​less than the preset condition number threshold M, where M is a constant and its value may vary slightly in different systems. Since the Hessian matrix is ​​symmetric, it is not necessary to consider the transpose of the Hessian matrix when calculating the condition number, which greatly reduces the complexity of the calculation. A key property of symmetric matrices is that their eigenvalues ​​are all real numbers, which simplifies many mathematical processes, including the calculation of the condition number. Therefore, the observable condition can be optimized as:

[0116]

[0117] That is, the determinant det(H) of the target matrix is ​​not 0, and the condition number is less than or equal to M.

[0118] The identifiable conditions obtained by calculation are:

[0119]

[0120] in,

[0121]

[0122]

[0123] Step 106: Solve the objective function based on the objective matrix to obtain a first identification result.

[0124] Step 106 is described in detail below.

[0125] Based on the above observable conditions, the convergence of the parameters to be identified under the corresponding operating conditions is guaranteed. Therefore, parameter identification can be performed based on the Newton iteration method, which can ensure the rapid convergence of the parameters to be identified. The Newton iteration method is an efficient optimization algorithm, which is particularly suitable for the case where the objective function is quadratic. The first-order derivative (gradient) and second-order derivative (Hessian matrix) of the objective function can be used to find the local minimum.

[0126] Reference Figure 4 In some embodiments, step 106 may include but is not limited to steps 401 to 407 .

[0127] Step 401: Initialize target recognition parameters.

[0128] Before starting parameter identification, it is necessary to select an initial estimate for the motor parameters to be identified. These parameters may include resistance, inductance, flux linkage, etc. Their initial values ​​can be determined based on the rated data of the motor, information provided by the manufacturer, or data from previous experiments. Once the initial parameter vector is determined and verified, it will serve as the starting point of the algorithm for the next iterative calculation in order to find the optimal solution.

[0129] Step 402: Calculate the first-order derivative of the objective function with respect to the target identification parameter to obtain the Jacobian matrix.

[0130] Among them, the first-order derivative of the objective function provides information about the trend of the function in the parameter space. These derivatives are the basis for constructing the Jacobian matrix, which is further used to guide the update direction and amplitude of the parameters. Before calculating the first-order derivative, the specific form of the objective function needs to be clarified. In motor parameter identification, the objective function is a measure of the difference between the predicted output of the motor model and the actual observed data, such as the least squares error function, and the first-order partial derivative is calculated for each parameter in the objective function. These partial derivatives describe the rate of change of the objective function in each parameter direction. Arrange all the calculated first-order partial derivatives in the order of parameters to form a Jacobian matrix as follows:

[0131]

[0132] Step 403: Calculate the target inverse matrix of the target matrix.

[0133] Step 404: Calculate the parameter correction amount of the current iteration round according to the target inverse matrix and the Jacobian matrix.

[0134] Among them, in step 403-step 404, the target inverse matrix of the target matrix needs to be calculated first. Since the target matrix is ​​symmetric and positive definite, its inverse matrix can be obtained by numerical methods such as Cholesky decomposition. Then, the Jacobian matrix (the first-order derivative matrix of the target function) and the inverse matrix of the Hessian matrix are used to calculate the parameter correction Δθ, that is, the transpose of the Jacobian matrix is ​​multiplied by the inverse matrix of the Hessian matrix:

[0135] Δθ=-H -1 J

[0136] Step 405: Update the target identification parameters based on the parameter correction amount.

[0137] The parameter correction Δθ calculated in step 404 is obtained based on the inverse matrix of the Jacobian matrix and the Hessian matrix of the current iteration round. This correction indicates the direction and magnitude of adjustment from the current parameter estimate to a better parameter estimate. The parameter correction Δθ is added to the current parameter estimate θ using the following formula: old to generate a new parameter estimate Δθ new :

[0138] θ new =θ old +Δθ

[0139] Updated parameter value Δθ new Need to be recorded, these values ​​will be used as the starting point for the next round of iterations to calculate the Jacobian matrix and Hessian matrix required for the next round of iterations.

[0140] Step 406: In response to the updated target identification parameters not satisfying the convergence condition, return to executing the calculation of the first-order derivative of the target function with respect to the target identification parameters according to the updated target identification parameters until the target identification parameters satisfy the preset convergence condition.

[0141] Among them, step 406 involves checking the convergence conditions. In this step, first, it is possible to evaluate whether the current iteration is close enough to the optimal solution according to a preset criterion. This usually involves comparing the amplitude of the parameter correction amount with a pre-set threshold, and checking whether the objective function value has further significantly decreased. If the parameter correction amount is small enough and the improvement of the objective function is also lower than the preset standard, it can be considered that the algorithm has converged. However, if these conditions are not met, the algorithm needs to return to step 402, recalculate the Jacobian matrix, and continue to iterate in the hope of further optimizing the parameter estimation.

[0142] In some embodiments, an upper limit on the number of iterations may also be set to ensure that the current number of iterations does not exceed the upper limit on the number of iterations set for algorithm stability. This upper limit is intended to prevent the algorithm from running indefinitely in some cases even if the parameter correction amount and the degree of improvement in the objective function value do not reach the convergence condition. If the parameter correction amount is still large or the degree of improvement in the objective function value is still significant when the upper limit on the number of iterations is reached, this may indicate that the current iteration is not sufficiently close to the optimal solution, or that the initial parameter estimate is not accurate enough, and the algorithm parameters or initial conditions need to be reconsidered. If the parameter correction amount and the degree of improvement in the objective function value do not reach the convergence condition, but because the number of iterations has reached the upper limit, it can be decided whether to accept the current parameter estimate as the final result, or to adjust the algorithm parameters or initial estimate according to the current iteration result, and restart the iteration process.

[0143] Step 407: In response to the updated target identification parameters satisfying the convergence condition, the updated target identification parameters are determined as the first identification result.

[0144] Among them, step 407 is to confirm the current parameter estimate as the first identification result after the convergence condition is met, which means that the local minimum of the objective function has been found and the parameter estimate is close enough to the true value. This result will be used to update the motor model to improve the control strategy and performance of the motor. At this time, the Newton iteration process can be ended and the obtained parameter estimates can be verified to ensure that they are physically reasonable and consistent with the actual measurement data. If the verification is successful, these parameter estimates will be used in subsequent motor control and performance analysis.

[0145] Step 107: Update the value of the target identification parameter in the permanent magnet synchronous motor to the first identification result.

[0146] Step 107 is described in detail below.

[0147] In 106, after multiple iterations and convergence checks, parameter estimates that meet the convergence conditions are obtained, and these values ​​constitute the first identification results. These identified parameter values ​​are updated to the mathematical model of the permanent magnet synchronous motor, so that the resistance, inductance and other parameters in the motor model will be replaced by these new estimated values. After the parameters are updated, the validity of the new parameters can be verified by actual operating data, for example, by comparing the performance of the motor before and after the update, such as speed response, torque output, etc., to evaluate the effect of the parameter update.

[0148] Reference Figure 5 In some embodiments, after step 104, steps 501 to 503 may also be included.

[0149] Step 501: When the target condition number does not meet the preset observation condition, the direct axis current is adjusted, and the objective function and the target condition number are updated based on the adjusted direct axis current so that the target condition number meets the preset observation condition.

[0150] Step 501 begins by identifying the problem that the target condition number does not meet the requirements and taking measures to adjust the direct axis current. This adjustment is intended to change the operating state of the motor in the hope of improving the target condition number to achieve the preset observation condition. After the adjustment, based on the new operating conditions, the objective function and the target condition number need to be recalculated to evaluate whether the adjustment is effective. If the condition number is improved, it indicates that the adjustment measure is successful; if not, further adjustment may be required.

[0151] It is understandable that in step 501, the decision to adjust the direct-axis current is based on a deep understanding of the motor control characteristics, because the direct-axis current is related to the excitation of the motor in the permanent magnet motor control, while the quadrature-axis current is related to the control power. Since the quadrature-axis current is directly related to the output power and torque of the motor, its adjustment will have an immediate and significant impact on the performance and load response of the motor. Therefore, during the operation of the motor, it is usually necessary to maintain the stability of the quadrature-axis current to ensure that the motor can provide the required power and maintain the accuracy of control. In contrast, the adjustment of the direct-axis current has little effect on the instantaneous power output of the motor, but plays an important role in improving the observability of the motor parameters and the stability of the identification process. Therefore, by appropriately adjusting the direct-axis current, the operating point of the motor can be changed, thereby possibly improving the target condition number, making it easier to accurately identify the motor parameters. This adjustment helps to optimize the parameter identification process without interfering with the normal power control of the motor.

[0152] Step 502: Solve the updated objective function based on the Newton iteration method to obtain a second identification result of the target identification parameters.

[0153] Among them, when the target condition number meets the preset conditions, the Newton iteration method is used to solve the updated objective function. This step involves recalculating the Jacobian matrix and the Hessian matrix, and using these matrices to solve the linear equations to obtain the parameter correction. Through this iterative process, the second identification result of the target identification parameters can be obtained, which is the optimal parameter estimate after the objective function is updated.

[0154] Step 503: Update the value of the target identification parameter in the permanent magnet synchronous motor to the second identification result.

[0155] Finally, step 503 involves applying the second identification result to the motor model of the permanent magnet synchronous motor, using the second identification result as the final estimate of the motor parameters, and updating these values ​​to the mathematical model of the motor. These updates are implemented in the control system to ensure that the motor operates according to the latest parameter estimates. After the update, it is necessary to verify whether the performance of the motor is improved and evaluate the impact of the parameter update on the accuracy and stability of the motor control.

[0156] These three steps form a complete process, from identifying the problem, adjusting and recalculating, to applying the new parameter estimates, ensuring the robustness and accuracy of the motor parameter identification process and obtaining reliable results even when the initial conditions are not ideal.

[0157] Reference Figure 6 In some embodiments, after step 102, steps 601 to 602 may also be included.

[0158] Step 601: Acquire steady-state operation data of the permanent magnet synchronous motor.

[0159] Among them, in the actual operation of the motor, the steady-state operation data refers to the data when the various performance parameters of the motor reach a stable state without transient changes or dynamic disturbances. When executing step 601, it is first necessary to ensure that the motor operates under steady-state conditions, which usually means that the parameters such as the load, voltage and speed of the motor remain constant. In this stable state, the dynamic response of the motor has decayed, so the collected data can truly reflect the static performance of the motor. The data collection process may include the following aspects: measuring the three-phase current and voltage of the motor, recording the speed of the motor, temperature data, etc., a set of high-quality steady-state operation data, providing a solid foundation for subsequent parameter identification and correction of the objective function. Accurate steady-state data helps to improve the prediction accuracy of the motor model, optimize the motor control strategy, and provide important information for motor maintenance and fault diagnosis.

[0160] Step 602: Based on the voltage equation of the permanent magnet synchronous motor, the objective function is corrected according to the steady-state operation data and the target identification parameters to update the objective function.

[0161] The purpose of step 602 is to use the acquired steady-state operation data of the permanent magnet synchronous motor to correct the objective function. The objective function is updated according to the voltage equation and the steady-state data. By introducing the steady-state operation data, the objective function can be adjusted to make it closer to the performance of the motor under actual steady-state conditions.

[0162] Through step 601 - step 602 , it can be ensured that the motor parameter identification process is more accurate and reliable. This step not only improves the performance of the motor control strategy, but also provides important data support for the long-term stable operation and maintenance of the motor.

[0163] See also Figure 7 The embodiment of the present application further provides a motor parameter identification device 700 based on observability analysis, which can implement the above-mentioned motor parameter identification method based on observability analysis, including:

[0164] The acquisition unit 701 is used to acquire motor parameters and target identification parameters of the permanent magnet synchronous motor.

[0165] The establishing unit 702 is used to establish an objective function based on the voltage equation of the permanent magnet synchronous motor according to the motor parameters and the target identification parameters.

[0166] The first determining unit 703 is used to determine the Hessian matrix corresponding to the objective function as the target matrix.

[0167] The calculation unit 704 is used to calculate the target condition number corresponding to the target matrix according to the target matrix.

[0168] The second determining unit 705 is used to determine that the target identification parameter is an observable parameter when the target matrix and the target condition number meet a preset observation condition.

[0169] The solving unit 706 solves the target function based on the target matrix to obtain a first identification result.

[0170] The updating unit 707 is used to update the value of the target identification parameter in the permanent magnet synchronous motor to the first identification result.

[0171] See also Figure 8 , Figure 8 The motor parameter identification system 800 based on observability analysis provided in the embodiment of the present application includes a load unit 801, a permanent magnet synchronous motor 802 and a controller 803;

[0172] A permanent magnet synchronous motor 802, the permanent magnet synchronous motor 802 is connected to the load unit 801, and the permanent magnet synchronous motor 802 is used to convert electrical energy into mechanical energy to drive the load unit 801;

[0173] The controller 803 is connected to the permanent magnet synchronous motor 802 . The controller 803 is used to generate a control signal and send it to the permanent magnet synchronous motor 802 . The controller 803 is also used to collect parameter data and motor parameters of the permanent magnet synchronous motor 802 .

[0174] The embodiments of the present application propose a motor parameter identification method, device and related equipment based on observability analysis. The method is used to identify the parameters of a permanent magnet synchronous motor. The method first obtains the motor parameters and target identification parameters of the permanent magnet synchronous motor; substitutes the motor parameters and target identification parameters into the voltage equation corresponding to the permanent magnet synchronous motor to establish an objective function; determines the Hessian matrix corresponding to the objective function as the target matrix; calculates the target condition number corresponding to the target matrix according to the target matrix; when the target matrix and the target condition number meet the preset observation conditions, determines that the target identification parameters are observable parameters; solves the objective function based on the target matrix to obtain a first identification result; and updates the numerical value of the target identification parameter in the permanent magnet synchronous motor to the first identification result.

[0175] After establishing the objective function, the embodiment of the present application calculates the Hessian matrix corresponding to the objective function, and determines the Hessian matrix as the target matrix. By analyzing the characteristics of the target matrix and the condition number, it is judged whether the target identification parameters meet the observation conditions under the current conditions. When the observation conditions are met, the objective function is solved based on the target matrix to obtain the first identification result. This identification result is an accurate and reliable identification result verified by the observation conditions. According to the identification result, the corresponding numerical value in the permanent magnet synchronous motor is updated. Through the above process, the observable conditions of the parameters can be systematically analyzed, thereby improving the accuracy and reliability of parameter identification.

[0176] The present application also provides an electronic device, including:

[0177] at least one memory;

[0178] at least one processor;

[0179] at least one program;

[0180] The program is stored in the memory, and the processor executes the at least one program to implement the motor parameter identification method based on energy observability analysis in the present application. The electronic device can be any intelligent terminal including a mobile phone, a tablet computer, a personal digital assistant (PDA), a car computer, etc.

[0181] See also Fig. 9 , Fig. 9 The hardware structure of an electronic device of another embodiment is illustrated, and the electronic device includes:

[0182] The processor 901 may be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of the present application;

[0183] The memory 902 can be implemented in the form of ROM (Read Only Memory), static storage device, dynamic storage device or RAM (Random Access Memory). The memory 902 can store operating systems and other applications. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 902, and the processor 901 calls and executes the motor parameter identification method based on energy observability analysis in the embodiments of this application;

[0184] Input / output interface 903, used to implement information input and output;

[0185] Communication interface 904, used to realize communication interaction between the device and other devices, which can be realized by wired mode (such as USB, network cable, etc.) or wireless mode (such as mobile network, WIFI, Bluetooth, etc.);

[0186] A bus 905 that transmits information between various components of the device (e.g., the processor 901, the memory 902, the input / output interface 903, and the communication interface 904);

[0187] The processor 901 , the memory 902 , the input / output interface 903 and the communication interface 904 are connected to each other in communication within the device via a bus 905 .

[0188] An embodiment of the present application further provides a storage medium, which is a computer-readable storage medium and stores a computer program. When the computer program is executed by a processor, the above-mentioned motor parameter identification method based on energy observability analysis is implemented.

[0189] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0190] The embodiments described in the embodiments of the present application are intended to more clearly illustrate the technical solutions of the embodiments of the present application, and do not constitute a limitation on the technical solutions provided in the embodiments of the present application. Those skilled in the art will appreciate that with the evolution of technology and the emergence of new application scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0191] Those skilled in the art will appreciate that the technical solutions shown in the figures do not constitute a limitation on the embodiments of the present application, and may include more or fewer steps than shown in the figures, or a combination of certain steps, or different steps.

[0192] The device embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0193] Those skilled in the art will appreciate that all or some of the steps in the methods disclosed above, and the functional modules / units in the systems and devices may be implemented as software, firmware, hardware, or a suitable combination thereof.

[0194] The terms "first", "second", "third", "fourth", etc. (if any) in the specification of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present application described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0195] It should be understood that in the present application, "at least one (item)" means one or more, and "plurality" means two or more. "And / or" is used to describe the association relationship of associated objects, indicating that three relationships may exist. For example, "A and / or B" can mean: only A exists, only B exists, and A and B exist at the same time, where A and B can be singular or plural. The character " / " generally indicates that the objects associated before and after are in an "or" relationship. "At least one of the following items" or similar expressions refers to any combination of these items, including any combination of single items or plural items. For example, at least one item (one) of a, b or c can mean: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, c can be single or multiple.

[0196] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the above units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. The mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0197] The units described above as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0198] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0199] If the integrated unit is implemented in the form of 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 application, 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, and the computer software product is stored in a storage medium, including multiple instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, referred to as ROM), random access memory (Random Access Memory, referred to as RAM), disk or optical disk and other media that can store programs.

[0200] The preferred embodiments of the present application are described above with reference to the accompanying drawings, but the scope of the rights of the present application is not limited thereto. Any modification, equivalent substitution and improvement made by those skilled in the art without departing from the scope and essence of the present application should be within the scope of the rights of the present application.

Claims

1. A motor parameter identification method based on observability analysis, characterized in that: The method is used to perform parameter identification on a permanent magnet synchronous motor, and the method comprises: Acquiring motor parameters and target identification parameters of the permanent magnet synchronous motor; Substituting the motor parameters and the target identification parameters into the voltage equation corresponding to the permanent magnet synchronous motor to establish an objective function; Determine the Hessian matrix corresponding to the objective function as the target matrix; Calculate the target condition number corresponding to the target matrix according to the target matrix; When the target matrix and the target condition number meet a preset observation condition, determining that the target identification parameter is an observable parameter; Solving the objective function based on the objective matrix to obtain a first identification result; The value of the target identification parameter in the permanent magnet synchronous motor is updated to the first identification result.

2. The motor parameter identification method based on observability analysis according to claim 1 is characterized in that: The step of calculating the target condition number corresponding to the target matrix according to the target matrix includes: Calculate the maximum eigenvalue and the minimum eigenvalue of the target matrix; The target condition number is calculated according to the maximum eigenvalue and the minimum eigenvalue.

3. The motor parameter identification method based on observability analysis according to claim 1 is characterized in that: The preset observation condition includes an observable threshold, and when the target matrix and the target condition number meet the preset observation condition, determining that the target identification parameter is an observable parameter includes: Calculate the determinant of the target matrix; When the target condition number is less than the observable threshold and the determinant is not equal to zero, it is determined that the target matrix and the target condition number satisfy the observation condition, and the target identification parameter is determined to be an observable parameter.

4. The motor parameter identification method based on observability analysis according to claim 1 is characterized in that: The step of solving the objective function based on the objective matrix to obtain a first identification result includes: Initializing the target recognition parameters; Calculating the first-order derivative of the objective function with respect to the target identification parameter to obtain a Jacobian matrix; Calculate a target inverse matrix of the target matrix; Calculate the parameter correction amount of the current iteration round according to the target inverse matrix and the Jacobian matrix; updating the target identification parameter based on the parameter correction amount; In response to the updated target identification parameters not satisfying the convergence condition, returning to calculate the first-order derivative of the target function with respect to the target identification parameters according to the updated target identification parameters until the target identification parameters satisfy a preset convergence condition; In response to the updated target identification parameters satisfying the convergence condition, the updated target identification parameters are determined as the first identification result.

5. The motor parameter identification method based on observability analysis according to claim 1 is characterized in that: The motor parameter of the permanent magnet synchronous motor includes a direct axis current. After calculating a target condition number corresponding to the target matrix according to the target matrix, the method further includes: When the target condition number does not satisfy the preset observation condition, adjusting the direct axis current, and updating the objective function and the target condition number based on the adjusted direct axis current, so that the target condition number satisfies the preset observation condition; Solving the updated objective function based on Newton's iteration method to obtain a second identification result of the target identification parameter; The value of the target identification parameter in the permanent magnet synchronous motor is updated to the second identification result.

6. The motor parameter identification method based on observability analysis according to claim 1 is characterized in that: After substituting the target matrix and the target condition number into the voltage equation corresponding to the permanent magnet synchronous motor to establish the target function, the method further includes: Acquiring steady-state operation data of the permanent magnet synchronous motor; Based on the voltage equation of the permanent magnet synchronous motor, the objective function is corrected according to the steady-state operation data and the target identification parameters to update the objective function.

7. The motor parameter identification method based on observability analysis according to claim 1 is characterized in that: When the target identification parameter is the direct-axis inductance And quadrature inductance When , the objective function is a quadratic objective function, and the objective function is defined as: Among them, u d is the direct axis voltage, u q is the quadrature axis voltage, i d is the direct axis current, i q is the quadrature axis current, R is the resistance, is the flux linkage parameter, p is the derivative operator, ω e is the motor speed.

8. A motor parameter identification device based on observability analysis, characterized in that: include: An acquisition unit, used to acquire motor parameters and target identification parameters of the permanent magnet synchronous motor; An establishing unit, configured to establish an objective function based on a voltage equation of the permanent magnet synchronous motor and according to the motor parameters and the target identification parameters; A first determining unit is used to determine the Hessian matrix corresponding to the objective function as a target matrix; A calculation unit, used for calculating a target condition number corresponding to the target matrix according to the target matrix; a second determination unit, configured to determine that the target identification parameter is an observable parameter when the target matrix and the target condition number satisfy a preset observation condition; A solving unit, solving the objective function based on the objective matrix to obtain a first identification result; An updating unit is used to update the value of the target identification parameter in the permanent magnet synchronous motor to the first identification result.

9. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program, and is characterized in that when the processor executes the computer program, the motor parameter identification method based on observability analysis as described in any one of claims 1 to 6 is implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the motor parameter identification method based on observability analysis described in any one of claims 1 to 6 is implemented.

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