Multi-parameter identification method and system for permanent magnet synchronous motor based on improved whale optimization algorithm

Abstract

The present disclosure belongs to the technical field of motor parameter identification, and provides a permanent magnet synchronous motor multi-parameter identification method and system based on an improved whale algorithm, including the following steps: obtaining a state equation of a permanent magnet synchronous motor in a d-q coordinate system; performing discretization processing on the obtained state equation, respectively calculating discrete voltage equations when the d-axis current exists or not, and constructing a motor full-rank equation set; calculating a fitness function of the constructed motor full-rank equation set; performing optimization solving on the fitness function based on the improved whale algorithm, and obtaining optimal identified multi-parameters.

Description

Technical Field

[0001] This disclosure belongs to the field of motor parameter identification technology, specifically relating to a method and system for multi-parameter identification of permanent magnet synchronous motors based on an improved whale algorithm. Background Technology

[0002] The statements in this section are merely background information relating to this disclosure and do not necessarily constitute prior art.

[0003] Permanent magnet synchronous motors (PMSMs) are simple in structure, reliable in operation, and highly efficient in energy conversion, making them widely used in automotive, aerospace, and military fields. To achieve high-performance control of PMSMs, a combination of vector control and sensorless control is typically employed, while motor parameters (e.g., stator resistance R) are also considered. s quadrature axis inductance L d Direct-axis inductor L q Permanent magnet flux ψ f The accurate acquisition of motor parameters (such as stator current, magnetic saturation effect, and load disturbance) directly determines whether the motor can operate at high performance. However, in actual operation, motor parameters are easily affected by factors such as the operating environment, stator current, magnetic saturation effect, and load disturbances (for example, different frequencies and temperature changes will cause changes in stator resistance, and magnetic saturation effect will affect inductance parameters, etc.), leading to changes in the system's operating state and thus degrading the performance of the control system. Therefore, the accurate identification of motor parameters has broad prospects and important theoretical and engineering significance.

[0004] According to the inventor, most current research on motor parameter identification is based on artificial intelligence optimization algorithms for permanent magnet synchronous motor parameter identification. However, traditional algorithms (such as particle swarm optimization and genetic algorithms) cannot achieve multi-parameter identification and also suffer from problems such as large workload, long solution time, and low efficiency. High solution accuracy and high efficiency are prerequisites for parameter identification and high-performance motor operation.

[0005] The Whale Optimization Algorithm (WOA) is a metaheuristic optimization algorithm that has emerged in recent years. It is simple in principle, easy to implement, and offers high solution accuracy, and has been applied in fields such as reactive power optimization in power systems and integrated optimization of distribution networks. However, traditional Whale algorithms suffer from several drawbacks in motor parameter identification. These include: the pseudo-random number generation strategy cannot ensure a uniform distribution of individual whales in the solution space; the linearly decreasing convergence factor 'a' may lead to local optima and a decline in global search capability; the linearly decreasing weight coefficient 'ω' reduces the global search speed in the early stages of the algorithm; and the decrease in whale population diversity. These issues ultimately reduce the accuracy and efficiency of parameter identification. Summary of the Invention

[0006] To address the aforementioned issues, this disclosure proposes a multi-parameter identification method and system for permanent magnet synchronous motors based on an improved whale algorithm. This disclosure uses SOBO sequences to generate ultra-uniformly distributed particles to improve the diversity of the initial whale population, solving problems such as uneven sample distribution, low computational efficiency, and local optima caused by the variability and inhomogeneity of traditional pseudo-random number algorithms. It also improves the convergence factor 'a', sets nonlinear adjustment of the weight coefficient 'ω', and considers boundary issues to enhance the accuracy of multi-parameter identification.

[0007] According to some embodiments, the first solution of this disclosure provides a multi-parameter identification method for permanent magnet synchronous motors based on an improved whale algorithm, which adopts the following technical solution:

[0008] A multi-parameter identification method for permanent magnet synchronous motors based on an improved whale algorithm includes the following steps:

[0009] Obtain the state equation of the permanent magnet synchronous motor in the dq coordinate system;

[0010] The obtained state equations are discretized, and the discrete voltage equations for the existence of d-axis currents are calculated to construct a full-rank equation system for the motor.

[0011] Based on the constructed full-rank equations of the motor, the fitness function of the permanent magnet synchronous motor is calculated.

[0012] The fitness function is optimized based on the improved whale algorithm to obtain the optimal identification parameters.

[0013] As a further technical limitation, the state parameters of the permanent magnet motor in the two-phase rotating dq coordinate system are collected, and the state equation of the permanent magnet synchronous motor in the dq coordinate system is established based on the collected state parameters.

[0014] Furthermore, the state parameters include d-axis current component, q-axis current component, d-axis voltage component, q-axis voltage component, d-axis inductance, q-axis inductance, stator resistance, and permanent magnet flux linkage.

[0015] As a further technical limitation, the discretization process employs the Euler method or the zero-order preservation method.

[0016] As a further technical limitation, the fitness function is:

[0017] Among them, u d and u q These are the dq-axis stator voltages of the ideal PMSM model. For the actual PMSM model, τ1, τ2, τ3, and τ4 are the weighting coefficients of the fitness function. The larger the value of τ, the more important a certain component is in the fitness function. d0 (k), u q0 (k), ω e (k), i q0 (k) is used for i d =0 control strategy, the kth sampled data; u d1 (k), u q1 (k), i d1 (k), i q1 (k) represents the kth sampled data when the disturbance current is injected.

[0018] As a further technical limitation, the specific process of optimizing the fitness function based on the improved whale algorithm is as follows: obtain the initial whale pod; calculate the individual fitness value of the whales based on the fitness function; update the historical global best individual of the whale pod and the historical best individual of each whale individual, so that the fitness function value of the next generation of whale individuals is less than that of the previous generation, until the fitness function value no longer decreases or reaches the maximum number of iterations, and output the whale individuals, thus completing the optimization solution and outputting the global best individual of the pod.

[0019] As a further technical limitation, the obtained motor state information is combined with the individual whale solution, and the identification model voltage of each individual whale is obtained through the full-rank equation system of the motor. The fitness function value of each individual whale is calculated based on the difference between the obtained identification model voltage and the preset reference voltage.

[0020] According to some embodiments, the second solution of this disclosure provides a multi-parameter identification system for permanent magnet synchronous motors based on an improved whale algorithm, which adopts the following technical solution:

[0021] A multi-parameter identification system for permanent magnet synchronous motors based on an improved whale algorithm includes:

[0022] The acquisition module is configured to acquire the state equations of the permanent magnet synchronous motor in the dq coordinate system;

[0023] The module is configured to discretize the acquired state equations, calculate the discrete voltage equations when there is a d-axis current, and construct a full-rank equation set for the motor.

[0024] The calculation module is configured to calculate the fitness function of the permanent magnet synchronous motor based on the constructed full-rank equations of the motor.

[0025] The identification module is configured to optimize the fitness function based on the improved whale algorithm to obtain the optimal identification parameters.

[0026] According to some embodiments, a third aspect of this disclosure provides a computer-readable storage medium, employing the following technical solution:

[0027] A computer-readable storage medium having a program stored thereon that, when executed by a processor, implements the steps of the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in the first aspect of this disclosure.

[0028] According to some embodiments, the fourth solution of this disclosure provides an electronic device that adopts the following technical solution:

[0029] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in the first aspect of this disclosure.

[0030] Compared with the prior art, the beneficial effects of this disclosure are as follows:

[0031] This disclosure uses SOBO sequences to generate ultra-uniformly distributed particles to improve the diversity of the initial whale population. It solves the problems of uneven sample distribution, low computational efficiency, and local optima caused by the differences and inhomogeneities of traditional pseudo-random number algorithms. It improves the convergence factor 'a', sets the weight coefficient 'ω' for nonlinear adjustment, and considers boundary problems to realize the four-parameter identification of permanent magnet synchronous motors, thereby improving the operating accuracy and performance of the permanent magnet synchronous motor control system. Attached Figure Description

[0032] The accompanying drawings, which form part of this disclosure, are used to provide a further understanding of this disclosure. The illustrative embodiments of this disclosure and their descriptions are used to explain this disclosure and do not constitute an undue limitation of this disclosure.

[0033] Figure 1 This is a flowchart of the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm in Embodiment 1 of this disclosure;

[0034] Figure 2 This is a flowchart of the improved whale optimization algorithm in Embodiment 1 of this disclosure;

[0035] Figure 3 This is a super-uniform sample sequence distribution map based on SOBOL sampling in Embodiment 1 of this disclosure;

[0036] Figure 4 This is a graph showing the relationship between the improved convergence factor 'a' and the number of iterations in Embodiment 1 of this disclosure;

[0037] Figure 5 This is a graph showing the relationship between the improved weight coefficient ω and the number of iterations in Embodiment 1 of this disclosure;

[0038] Figure 6 This is a schematic diagram of the parameter identification principle of PMSM in Embodiment 1 of this disclosure;

[0039] Figure 7 This is a structural block diagram of the multi-parameter identification system for permanent magnet synchronous motors based on the improved whale algorithm in Embodiment 2 of this disclosure. Detailed Implementation

[0040] The present disclosure will be further described below with reference to the accompanying drawings and embodiments.

[0041] It should be noted that the following detailed descriptions are illustrative and intended to provide further explanation of this disclosure. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure pertains.

[0042] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the exemplary embodiments according to this disclosure. As used herein, the singular form is intended to include the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms “comprising” and / or “including” are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0043] Where there is no conflict, the embodiments and features described herein can be combined with each other.

[0044] Example 1

[0045] Embodiment 1 of this disclosure introduces a multi-parameter identification method for permanent magnet synchronous motors based on an improved whale algorithm.

[0046] like Figure 1 The method for multi-parameter identification of permanent magnet synchronous motors based on an improved whale algorithm, as shown, includes the following steps:

[0047] Obtain the state equation of the permanent magnet synchronous motor in the dq coordinate system;

[0048] The obtained state equations are discretized, and the discrete voltage equations for the existence of d-axis currents are calculated to construct a full-rank equation system for the motor.

[0049] Based on the constructed full-rank equations of the motor, the fitness function of the permanent magnet synchronous motor is calculated.

[0050] The fitness function is optimized based on the improved whale algorithm to obtain the optimal identification parameters.

[0051] like Figure 2The specific process of the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm shown includes:

[0052] a. Establish the state equations of PMSM in the two-phase rotating dq coordinate system and set the set of parameters to be identified;

[0053] b. Discretize the voltage equation obtained in step a, and then... d =0 and i d By collecting the same amount of data in both cases ≠0, a full-rank equation system for the motor is obtained.

[0054] c. Improve the traditional whale optimization algorithm, including whale pod initialization based on SOBO sampling sequence, adopting an improved convergence factor a, nonlinearly adjusting the weight coefficient ω, and considering boundary problems, etc.

[0055] d. Set the fitness function, and based on the MARS principle, use the improved WOA algorithm to perform multi-parameter identification of PMSM.

[0056] As one or more implementation methods, in step a, the state equation is:

[0057]

[0058] In the formula, i d i q For the current component along the dq axis, u d u q L represents the voltage component along the dq axis. d L q R is the inductance on the dq axis. s For the stator resistance, ψ f Let λ be a permanent magnet flux linkage. s ,L d ,L q ,ψ f} represents the set of parameters that need to be identified simultaneously.

[0059] For PMSM vector control, i is generally adopted. d A control strategy of 0 is employed to improve the system power factor. When the motor is running stably, the change in the dq-axis current is relatively small, and its differential term can be approximated as 0, resulting in the simplified voltage equation:

[0060]

[0061] In step b, the discrete voltage equation is:

[0062]

[0063] By injecting a current disturbance signal into the d-axis of the motor stator for a short period of time, a full-rank state equation system of the motor is constructed, thereby achieving multi-parameter identification. The obtained i d The dq-axis model of a second-order motor with ≠0 is as follows:

[0064]

[0065] in i d =0 and i d By collecting the same amount of data in both cases ≠0, the following fourth-order full-rank equations for the motor are obtained:

[0066]

[0067] In the formula u d0 (k), u q0 (k), ω e (k), i q0 (k) is used for i d =0 control strategy, the kth sampled data; u d1 (k), u q1 (k), i d1 (k), i q1 (k) represents the kth sampled data when the disturbance current is injected.

[0068] In step c of this embodiment, regarding the generation of the initial whale population, the conventional approach is to use a pseudo-random number generation algorithm. However, due to the variability and non-uniformity of pseudo-random algorithms, individual whales cannot be evenly distributed within the solution space. To improve computational efficiency and avoid local optima, a SOBO sequence is used to generate a super-uniform sample, improving population diversity. Figure 3 As shown. Regarding the balance between global search capability and local optimization capability, the conventional approach is to use a linearly decreasing convergence factor 'a' and a linearly decreasing weight coefficient 'ω'. This paper improves 'a' so that it decreases non-linearly with the number of iterations, and improves 'ω' so that it decreases non-linearly with the number of iterations, thus balancing the algorithm's global search and local optimization capabilities, as shown. Figure 4 and Figure 5 As shown. Boundary issues are also considered to preserve population diversity.

[0069] Specifically, the initial whale pod is generated based on the SOBO sampling sequence as follows:

[0070] x i =σ(x max -x min )+x min (6)

[0071] In the formula, x i Let x be the position of the i-th whale. maxThe upper limit of the whale's position, x min Let σ be the lower bound of the whale's position, and σ = random(0,1). sobol .

[0072] The improved convergence factor a is:

[0073]

[0074] In the formula, α and β are adjustment coefficients; e is the natural constant (e≈2.718); t is the current iteration number; T max This represents the maximum number of iterations. While the convergence factor of this improvement exhibits a non-linear variation in the number of iterations, it balances global search with local optimization.

[0075] The weighting coefficient ω is non-linearly adjusted as follows:

[0076]

[0077] In the formula, ω max ω min These are the upper and lower limits of ω, respectively; T max ω represents the maximum number of iterations; t represents the current iteration number; and P represents the time coefficient. Here, the improved weight coefficient ω decreases non-linearly with the number of iterations, accelerating the global search speed in the early stages of the algorithm and achieving a balance between global search and local optimization capabilities.

[0078] In step c, when the whale's position x i ≥x max or x i ≤x min At that time, the location is updated as follows:

[0079] x i '=x i ×M (9)

[0080] In the formula, x i ' represents the individual whale after boundary processing; M is the position update parameter, and M = H × random(-0) . (5, 0.5), where H represents the Euclidean metric between the individual with the smallest fitness function value in the current space and the individual closest to it.

[0081] In step d, the fitness function is:

[0082]

[0083] In the formula, u d u q For the dq-axis stator voltage of an ideal PMSM model, For the actual PMSM model, τ1, τ2, τ3, and τ4 are the weighting coefficients of the fitness function. The larger the value of τ, the more important a certain component is in the fitness function. d0 (k), u q0 (k), ω e (k), i q0 (k) is used for i d =0 control strategy, the kth sampled data; u d1 (k), u q1 (k), i d1 (k), i q1 (k) represents the k-th sampled data when the disturbance current is injected. d u q i d i q ω e The voltage, current, and speed are obtained from voltage, current, and speed sensors, respectively, and the dq-axis voltage and current are obtained through coordinate transformation.

[0084] like Figure 2 and Figure 6 As shown, using the improved WOA algorithm, the obtained motor state information is compared with the individual solutions of this generation of whales. By combining these methods, the identification model voltage for each individual whale is obtained through a system of full-rank equations for the electric motor. And based on the reference voltage (u) d0 ,u q0 ,u d1 ,u q1 The fitness function value of each individual whale is calculated by taking the difference between the two values. The algorithm continuously updates the global best individual in the history of the whale population and the historical best individual of each individual whale. At the same time, the fitness function value of the next generation of whales is made smaller than that of the previous generation. This process is repeated until the fitness function value no longer decreases or the maximum number of iterations is reached. The algorithm then terminates and the output whale is the global best individual in the population.

[0085] This embodiment uses SOBO sequences to generate ultra-uniformly distributed particles to improve the diversity of the initial whale population. It solves the problems of uneven sample distribution, low computational efficiency, and local optima caused by the differences and inhomogeneities of traditional pseudo-random number algorithms. It improves the convergence factor 'a', sets the weight coefficient 'ω' for nonlinear adjustment, and considers boundary problems to achieve four-parameter identification of permanent magnet synchronous motors, thereby improving the operating accuracy and performance of the permanent magnet synchronous motor control system.

[0086] Example 2

[0087] Embodiment 2 of this disclosure introduces a multi-parameter identification system for permanent magnet synchronous motors based on an improved whale algorithm.

[0088] like Figure 7The multi-parameter identification system for permanent magnet synchronous motors based on the improved whale algorithm shown includes:

[0089] The acquisition module is configured to acquire the state equations of the permanent magnet synchronous motor in the dq coordinate system;

[0090] The module is configured to discretize the acquired state equations, calculate the discrete voltage equations when there is a d-axis current, and construct a full-rank equation set for the motor.

[0091] The computation module is configured to compute the fitness function of the constructed full-rank equations of the motor.

[0092] The identification module is configured to optimize the fitness function based on the improved whale algorithm to obtain the optimal identification parameters.

[0093] The detailed steps are the same as those of the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm provided in Example 1, and will not be repeated here.

[0094] Example 3

[0095] Embodiment 3 of this disclosure provides a computer-readable storage medium.

[0096] A computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps in the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in Embodiment 1 of this disclosure.

[0097] The detailed steps are the same as those of the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm provided in Example 1, and will not be repeated here.

[0098] Example 4

[0099] Embodiment 4 of this disclosure provides an electronic device.

[0100] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in Embodiment 1 of this disclosure.

[0101] The detailed steps are the same as those of the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm provided in Example 1, and will not be repeated here.

[0102] The above description is merely a preferred embodiment of this disclosure and is not intended to limit this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of protection of this disclosure.

Claims

1. A multi-parameter identification method for permanent magnet synchronous motors based on an improved whale algorithm, characterized in that, Includes the following steps: Obtaining permanent magnet synchronous motors in d - q Equations of state in a coordinate system; The obtained state equations are discretized, and the existence of each equation is calculated. d Discrete voltage equations for shaft current are used to construct a full-rank equation system for the motor. Based on the constructed full-rank equations of the motor, the fitness function of the permanent magnet synchronous motor is calculated. The fitness function is optimized based on the improved whale algorithm to obtain the optimal identification parameters. Data collection of permanent magnet motors in two-phase rotation dq State parameters in the coordinate system are used to establish the permanent magnet synchronous motor in the collected state parameters. d - q The state equations in the coordinate system; the state parameters include d Axis current components, q Axis current components, d Axis voltage components, q Axis voltage components, d Shaft inductance, q Shaft inductance, stator resistance, and permanent magnet flux linkage; The fitness function is: ;in, u d and u q These are the dq-axis stator voltages of the ideal PMSM model. , For the actual PMSM model, τ1, τ2, τ3, and τ4 are the weighting coefficients of the fitness function. The larger the value of τ, the more important a certain component is in the fitness function. u d0 ( k ), u q0 ( k ), ω e ( k ), i q0 ( k ) for adoption i d Under the =0 control strategy, the first k Secondary sampling data; u d1 ( k ), u q1 ( k ), i d1 ( k ), i q1 ( k ) is the first when the disturbance current is injected. k Secondary sampling data; The obtained motor state information is combined with the individual whale solution, and the identification model voltage of each individual whale is obtained through the full-rank equation system of the motor. The fitness function value of each individual whale is calculated based on the difference between the obtained identification model voltage and the preset reference voltage.

2. The multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in claim 1, characterized in that, The discretization process employs either the Euler method or the zero-order preservation method.

3. The multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in claim 1, characterized in that, The specific process of optimizing the fitness function based on the improved whale algorithm is as follows: obtain the initial whale pod; calculate the individual fitness value of the whales based on the fitness function; update the historical global best individual of the whale pod and the historical best individual of each whale individual, so that the fitness function value of the next generation of whale individuals is less than that of the previous generation, until the fitness function value no longer decreases or reaches the maximum number of iterations, and output the whale individuals, thus completing the optimization solution and outputting the global best individual of the pod.

4. A multi-parameter identification system for permanent magnet synchronous motors based on an improved whale algorithm, employing the multi-parameter identification method for permanent magnet synchronous motors based on an improved whale algorithm as described in any one of claims 1-3, characterized in that, include: The acquisition module is configured to acquire information about the permanent magnet synchronous motor. d - q Equations of state in a coordinate system; The building module is configured to discretize the acquired state equations and calculate whether they exist. d Discrete voltage equations for shaft current are used to construct a full-rank equation system for the motor. The computation module is configured to compute the fitness function of the constructed full-rank equations of the motor. The identification module is configured to optimize the fitness function based on the improved whale algorithm to obtain the optimal identification parameters.

5. A computer-readable storage medium having a program stored thereon, characterized in that, When the program is executed by the processor, it implements the steps in the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in any one of claims 1-3.

6. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the multi-parameter identification method for permanent magnet synchronous motors based on the improved whale algorithm as described in any one of claims 1-3.

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