System-level Efficiency Optimization Method for Linear Induction Motor Considering Multi-line Wave Magnetic Field Characteristics

Abstract

The present invention provides a method for optimizing the system-level efficiency of a linear induction motor by taking into account the characteristics of multi-traveling-wave magnetic fields. An optimization model is defined: the system is divided into two levels, namely the motor level and the control level, for optimization design; based on the chain equivalent circuit analysis model of the linear induction motor, the influence laws of the winding arrangement and the motor structure parameters on the optimization objectives are analyzed, the main optimization variables are screened out; and the optimization variables are sorted hierarchically according to the coupling relationship of the variables. The genetic algorithm is used to optimize the variables layer by layer; the final electromagnetic scheme is selected; the model predictive control based on the efficiency optimization function is used as the basic framework for motor control to solve the optimal flux linkage with the minimum system loss under various working conditions; and taking the speed overshoot as the optimization objective, the differential evolution algorithm is used to iteratively optimize the PI control parameters to seek better dynamic performance. The present invention can significantly improve the efficiency and power factor of the linear induction motor drive system and effectively ensure the dynamic response ability of the system.

Description

Technical Field

[0001] The present invention belongs to the field of linear motor optimization, and more specifically, relates to a method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of multi-traveling-wave magnetic fields. Background Art

[0002] A linear induction motor can directly generate linear motion without a transmission mechanism such as a gearbox, and has broad application prospects in urban rail transit drive systems such as subways. Compared with a rotary induction motor-driven rail transit system, a linear induction motor-driven system has advantages such as a strong climbing ability, a small turning radius, and a small cross-sectional area. However, due to special structures such as open-ended primary iron cores and semi-filled slots in a linear induction motor, it faces serious end effects and rich spatial harmonics, resulting in problems such as low efficiency and poor dynamic performance in a linear induction motor-driven system.

[0003] In response to the above problems, researchers have carried out a large amount of work on optimizing linear induction motor-driven systems. However, most of the work is to separately optimize and design the main components such as linear induction motors, converters, and controllers first, and then simply combine them to form a linear induction motor-driven system, without optimizing and designing the drive system as a whole. However, due to the influence of many factors such as the time-varying cross-coupling of multiple parameters caused by end effects, the complexity of operating conditions, and the non-linear delay of converters and controllers, the key indicators such as the efficiency of a linear induction motor-driven system deviate greatly from the theoretical values in actual operation, and the overall drive performance is difficult to reach the optimal. Moreover, the traditional motor optimization model does not consider the rich spatial harmonics of a linear induction motor, resulting in a large deviation between the optimized scheme and the actual motor performance, further deteriorating the optimization effect of the drive system.

[0004] Therefore, it is necessary to propose an accurate and efficient method for optimizing the system-level efficiency of a linear induction motor from the system level to improve the operating performance of a linear induction motor-driven system. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the purpose of the present invention is to provide a method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of multi-traveling-wave magnetic fields, aiming to solve the problems of poor effects and incomplete consideration factors of existing traditional device-level optimization methods.

[0006] To achieve the above object, a method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of multi-traveling-wave magnetic fields includes the following steps:

[0007] S1: According to the design requirements of the drive system, define an optimization model: set optimization objectives and constraints, and optimize and design the drive system by dividing it into two levels: the motor level and the control level;

[0008] S2: Analyze the influence rules of winding arrangement and motor structure parameters on the optimization objectives based on the chain equivalent circuit analysis model of the linear induction motor, screen out the main optimization variables, and determine the optimization range;

[0009] S3: Based on the optimization objectives, analyze the internal relationships between the optimization variables, and hierarchically sort the optimization variables according to the coupling relationship of the variables;

[0010] S4: Use the improved non-dominated sorting genetic algorithm to optimize the variables layer by layer, select the final electromagnetic scheme from the optimized solution set according to appropriate selection principles, and output the corresponding parameters of the electromagnetic scheme to the control system;

[0011] S5: Take the model predictive control based on the efficiency optimization function as the basic framework of motor control, solve the optimal flux linkage with the minimum system loss under each working condition, and achieve the tracking control of the optimal flux linkage through model predictive control;

[0012] S6: And take the speed overshoot as the optimization objective, use the differential evolution algorithm to iteratively optimize the PI control parameters to seek better dynamic performance;

[0013] S7: Finally, at the system level, judge whether the performance of the optimized drive system can meet the requirements.

[0014] The optimization model in the step S1 includes two main parts: the motor layer and the control layer. The optimization objectives are the efficiency of the linear induction motor, the drive system, the power factor, and the dynamic response ability. The constraints include: the motor size, power, slot fill factor, the upper limit value of the primary current density constrained by the heat dissipation condition, and the speed overshoot.

[0015] The optimization objective of the motor layer is:

[0016]

[0017] Among them, η m is the motor efficiency, is the motor power factor, x m is the optimization variable of the motor layer.

[0018] The constraints of the motor layer are:

[0019]

[0020] Among them, P opt is the rated power of the motor after optimization, P rated is the rated power of the motor, P design is the allowable power error value before and after motor optimization; τ is the pole pitch of the motor, λ s is the primary width of the motor, k s is the design experience coefficient; Js is the primary armature winding current density, J limit is the primary armature winding current density limit value; L s is the primary length of the motor, L max is the maximum length of the motor; F n is the normal force, F em is the electromagnetic thrust, k F is the design experience coefficient; f c is the slot fill factor, f ideal is the ideal slot fill factor, E f is the allowable slot fill factor error value.

[0021] The optimization objective of the control layer is:

[0022] max: f2(x c ) = η system

[0023] where η system is the motor system efficiency, x c is the control system parameter.

[0024] The constraint conditions of the control layer are:

[0025] s.t.: {ω os ≤ ω limit

[0026] where ω os is the speed overshoot, ω limit is the overshoot limit value.

[0027] The chain equivalent circuit analysis model of the linear induction motor in step S2 can accurately describe the operating performance of the linear induction motor under different working conditions, and analyze the influence law of the harmonic magnetic field and structural parameters of each pole pair of the linear induction motor on the motor performance. Based on the analysis model, the influence law of each optimization variable on the optimization objective is obtained, and the main optimization variables are selected according to the analysis results, and the optimization range is determined.

[0028] The electromagnetic steady-state thrust calculation formula is:

[0029]

[0030] where, B 3yv is the harmonic air-gap magnetic density of each pair of poles, j v is the harmonic current layer density of each pair of poles of the primary, and both are functions of the motor structure parameters.

[0031] Among the optimization variables include the winding arrangement method, the structural parameters of the motor, where the structural parameters of the motor are the primary tooth width b t 、slot width b s 、slot depth h t, secondary conductor plate thickness d, primary lateral width a1, secondary lateral width c1, etc.

[0032] The specific law of the influence of the harmonic magnetic field of each pole pair on the motor performance is as follows: the thrust provided by the harmonic magnetic field of each pole pair is different in nature, including both positive thrust and negative thrust. The motor performance can be enhanced by changing the winding arrangement, changing the composition of the harmonic magnetic field of each pole pair, and increasing the proportion of positive thrust.

[0033] If the motor winding arrangement is optimized alone, a motor structure that cannot be processed may be obtained. It is necessary to comprehensively consider the constraint relationship between the winding arrangement and the motor structure parameters to optimize the motor design.

[0034] In step S3, based on the optimization objective, the coupling relationship between the optimization variables and the degree of influence on the optimization objective are analyzed, and the optimization variables are sequentially stratified according to the analysis results. Then, based on the stratification results, the improved non-dominated sorting genetic algorithm is used to optimize the variables layer by layer.

[0035] The influence of optimization variables on optimization objectives is expressed as:

[0036]

[0037] Where r is the Pearson correlation coefficient, and S X For variable X n The sample mean and standard deviation of and S Y is the variable Y n The sample mean and standard deviation of , n1 is the sample size.

[0038] In steps S5 and S6, the model predictive control based on the efficiency optimization function can improve the efficiency of the linear drive system under steady and dynamic conditions and reduce thrust fluctuations during operation. The differential evolution algorithm is used to iteratively optimize the PI control parameters, which can improve the speed overshoot and response speed during the switching process of different working conditions, and further improve the dynamic response capability of the drive system.

[0039] In general, the above technical solution conceived by the present invention has the following beneficial effects compared with the prior art:

[0040] (1) The influence of the harmonic magnetic field of the linear induction motor is fully considered, the motor winding arrangement and structural parameters are optimized, and the efficiency and power factor of the motor are improved;

[0041] (2) Through model predictive control based on efficiency optimization function and PI parameter iterative optimization, the thrust fluctuation during operation is reduced, and the steady-state efficiency and dynamic response capability of the linear induction motor drive system are improved;

[0042] (3) Optimize the design of the drive system as a whole, solving the problem that the overall performance of the drive system may be poor due to the optimization of a single device. Description of the Drawings

[0043] Figure 1 is a flowchart of a method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of multi-traveling-wave magnetic fields provided by the present invention;

[0044] Figure 2 is a logic block diagram of the system-level optimization method provided by an embodiment of the present invention;

[0045] Figure 3 is the chain equivalent circuit of the linear induction motor provided by an embodiment of the present invention;

[0046] Figure 4 is a schematic structural diagram of the linear induction motor provided by an embodiment of the present invention;

[0047] Figure 5 is the model predictive control framework based on the efficiency optimization function provided by an embodiment of the present invention. Detailed Embodiments

[0048] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the following further details the present invention in conjunction with the accompanying drawings and a 3kW laboratory prototype as an example. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0049] A flowchart of a method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of multi-traveling-wave magnetic fields provided by the present invention, as Figure 1 shown, specifically includes the following steps:

[0050] S1: According to the design requirements of the drive system, define the optimization model: set the optimization objectives and constraints, and optimize the design of the drive system by dividing it into two levels: the motor level and the control level;

[0051] S2: Analyze the influence rules of the winding arrangement and the motor structure parameters on the optimization objectives based on the chain equivalent circuit analysis model of the linear induction motor, screen out the main optimization variables, and determine the optimization range;

[0052] S3: Based on the optimization objectives, analyze the internal relationships between the optimization variables, and hierarchically sort the optimization variables according to the coupling relationship of the variables;

[0053] S4: Use the improved non-dominated sorting genetic algorithm to optimize the variables layer by layer, select the final electromagnetic solution from the optimized solution set according to the appropriate selection principle, and output the parameters corresponding to the electromagnetic solution to the control system;

[0054] S5: Using model predictive control based on an efficiency optimization function as the basic framework for motor control, solve for the optimal flux linkage that minimizes system losses under various operating conditions, and achieve tracking control of the optimal flux linkage through model predictive control;

[0055] S6: Taking the speed overshoot as the optimization objective, use the differential evolution algorithm to iteratively optimize the PI control parameters to seek better dynamic performance;

[0056] S7: Finally, at the system level, determine whether the performance of the optimized drive system can meet the requirements.

[0057] The following will specifically describe the method of the present invention, and its logic block diagram is as Figure 2 shown

[0058] (1) According to the requirements of the linear induction motor drive system, divide the system into two parts, the motor layer and the control layer, for optimized design. Set the optimization objectives as the efficiency, power factor, and dynamic response ability of the linear induction motor. The constraint conditions are the motor size, power, the upper limit value of the primary current density restricted by the heat dissipation conditions, the speed overshoot, etc.

[0059] The optimization objective of the motor layer is:

[0060]

[0061] Among them, η m is the motor efficiency, is the motor power factor, x m is the optimization variable of the motor layer.

[0062] The constraint conditions of the motor layer are:

[0063]

[0064] Among them, P opt is the rated power of the motor after optimization, P rated is the rated power of the motor. According to the power rating of the motor, set the error between the rated powers before and after optimization to be less than or equal to 50 W; τ is the pole pitch of the motor, λ s is the width of the motor primary. According to past design experience, to ensure that the motor has a good quality factor and its performance, the pole pitch length of the motor should be less than twice the width of the motor primary; J s is the current density of the primary armature winding. Select the current density of the primary armature winding to be less than 5.5 A / mm 2 ; L s is the length of the motor primary. According to the installation conditions of the motor, its length is not greater than 1.4 m; F n is the normal force, F emis the electromagnetic thrust. To ensure the reliability of its structural strength, the normal force is set not to be greater than 4 times the electromagnetic thrust; f c is the slot fill factor. For the feasibility of processing, the slot fill factor should not be too high. However, to give full play to the performance of the motor, the slot fill factor should not be too low either. Therefore, the slot fill factor is set between 0.5 and 0.65.

[0065] The optimization objective of the control layer is:

[0066] max: f2(x c ) = η system

[0067] where η system is the motor system efficiency, and x c is the control system parameter.

[0068] The constraint condition of the control layer is:

[0069] s.t.: {ω os ≤0.01

[0070] where ω os is the speed overshoot. To ensure the stability of the motor during the switching process under different working conditions, the speed overshoot is set to be less than or equal to 0.01.

[0071] (2) Optimal design of the motor layer

[0072] (2-1) Take the winding arrangement method and the motor structure parameters as the optimization variables, and use the linear induction motor chain equivalent circuit analysis model shown as Figure 3 to analyze the influence law of each optimization variable on the optimization objective, and select the main optimization variables according to the analysis results and determine the optimization range. The structural parameters of the motor include the primary tooth width b t , slot width b s , slot depth h t , secondary conductor plate thickness d, primary lateral width a1, secondary lateral width c1, etc., as shown Figure 4 shown.

[0073] (2-2) The optimization of the winding arrangement method is based on the fact that the influence of the harmonic magnetic fields of each pole pair on the motor performance lies in the different thrust properties provided, with both positive and negative thrusts. By changing the winding arrangement method, the composition of the harmonic magnetic fields of each pole pair can be changed, and the proportion of the positive thrust can be increased, so as to enhance the motor performance. However, if only the winding arrangement of the motor is optimized separately, a motor structure that cannot be processed or has poor topological performance may be obtained. It is necessary to comprehensively consider the constraint relationship between the winding arrangement and the motor structure parameters for the motor optimization design.

[0074] The steady-state electromagnetic thrust is:

[0075]

[0076] B 3yv+ and B 3yv- are the forward and reverse air-gap magnetic fluxes of each pair of pole harmonics, j v+ and j v- are the forward and reverse armature current layer densities of each pair of pole harmonics, and they are all functions related to the motor structure parameters. Based on this, the influence laws of the motor structure parameters and the harmonics of each pole pair on the motor performance can be analyzed.

[0077] Furthermore, based on the optimization objectives, analyze the coupling relationship between the optimization variables and the degree of influence on the optimization objectives. According to the analysis results, stratify the order of the optimization variables, and then based on the stratification results, use the improved non-dominated sorting genetic algorithm to optimize the variables layer by layer to obtain the optimal winding arrangement and structural parameter scheme, thereby completing the optimization design of the motor layer. And output the electromagnetic parameters corresponding to the optimized electromagnetic scheme to the control layer.

[0078] The influence of the optimization variables on the optimization objectives is expressed as:

[0079]

[0080] where r is the Pearson correlation coefficient, and S X are the sample mean and standard deviation of variable X n , and S Y are the sample mean and standard deviation of variable Y n , and n1 is the number of samples.

[0081] (3) Optimization design of the control layer:

[0082] Model predictive control based on the efficiency optimization function improves the efficiency of the linear drive system under steady and dynamic conditions, reduces the thrust fluctuation during operation, and uses the differential evolution algorithm to iteratively optimize the PI control parameters to improve the response speed and overshoot during the switching process of different working conditions, and enhance the dynamic response ability of the drive system. The control block diagram is as Figure 5 shown.

[0083] Its system loss model is:

[0084]

[0085] where b1, b2, b3, b4, b5 are loss coefficients, ψ dris the secondary d-axis flux linkage, n1 is the inverter loss coefficient, and f1 is a function of the secondary d-axis flux linkage. The loss model can be differentiated to obtain the secondary d-axis flux linkage at the minimum loss. Then, based on the optimal flux linkage, model predictive control is used to find the optimal voltage vector to reduce the motor loss and thrust fluctuation during operation, so as to achieve the purpose of improving the efficiency and thrust quality during operation.

[0086] (4) At the system level, it is judged whether the performance of the optimized drive system can meet the requirements. If it meets, the solution is output; if not, it returns to the motor layer for re-optimization.

[0087] Those skilled in the art can easily understand that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of a multi-line wave magnetic field, characterized in that, It includes the following steps: S1: According to the design requirements of the drive system, define the optimization model: set the optimization objectives and constraints, and divide the drive system into two levels, namely the motor level and the control level, for optimization design; S2: Based on the chain equivalent circuit analysis model of the linear induction motor, analyze the influence laws of the winding arrangement and the motor structure parameters on the optimization objectives, screen out the main optimization variables, and determine the optimization range; S3: Based on the optimization objectives, analyze the internal relationships between the optimization variables, and hierarchically sort the optimization variables according to the coupling relationship of the variables; S4: Use the improved non-dominated sorting genetic algorithm to optimize the variables layer by layer. According to the appropriate selection principle, select the final electromagnetic scheme from the obtained solution set of the optimization, and output the corresponding parameters of the electromagnetic scheme to the control system; S5: Take the model predictive control based on the efficiency optimization function as the basic framework of the motor control, solve the optimal flux linkage with the minimum system loss under various working conditions, and achieve the tracking control of the optimal flux linkage through the model predictive control; S6: And take the speed overshoot as the optimization objective, and use the differential evolution algorithm to iteratively optimize the PI control parameters to seek better dynamic performance; S7: Finally, at the system level, judge whether the performance of the optimized drive system can meet the requirements.

2. The method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of a multi-line wave magnetic field according to claim 1, characterized in that, The optimization model in the step S1 includes two main parts: the motor level and the control level; the optimization objectives are the efficiency, power factor, and dynamic response ability of the linear induction motor and the drive system; the constraints include: the size and power of the motor, the slot fill factor, the upper limit value of the primary current density constrained by the heat dissipation condition, and the speed overshoot; The optimization objective of the motor level is: ， Among them, η m is the motor efficiency, cos φ is the motor power factor, x m is the optimization variable of the motor layer; The constraints of the motor level are: ， Among them, P opt is the rated power after the motor optimization, P rated is the rated power of the motor, P design is the allowable value of the power error before and after the motor optimization; τ is the pole pitch of the motor, λ s is the primary width of the motor, k s is the design experience coefficient; J s is the current density of the primary armature winding, J liit is the limit value of the current density of the primary armature winding; L s is the primary length of the motor, L max is the maximum length of the motor; F n is the normal force, F em is the electromagnetic thrust, k F is the design experience coefficient; f c is the slot fill factor, f ideal is the ideal slot fill factor, E f is the allowable value of the slot fill factor error; The optimization objective of the control level is: ， Among them η system is the motor system efficiency, x c is the control system parameter; The constraints of the control level are: ， wherein ω os is the speed overshoot, ω limit is the overshoot limit value.

3. The method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of a multi-line wave magnetic field according to claim 1, characterized in that The chain equivalent circuit analysis model of the linear induction motor in the step S2 accurately describes the operating performance of the linear induction motor under different working conditions, analyzes the influence laws of the harmonic magnetic fields of each pole pair and the structure parameters of the linear induction motor on the motor performance; based on the analysis model, obtain the influence laws of each optimization variable on the optimization objectives, and select the main optimization variables according to the analysis results, and determine the optimization range; The electromagnetic steady-state thrust calculation formula is: ， Among them, B 3yv is the air-gap magnetic density of each pair of pole harmonics, j v is the current layer density of each pair of pole harmonics in the primary. Both are functions of the motor structure parameters.

4. The method for optimizing the system-level efficiency of a linear induction motor considering the characteristics of a multi-line wave magnetic field according to claim 3, characterized in that wherein the optimization variables include the winding arrangement and the structural parameters of the motor, and the structural parameters of the motor are the primary tooth width b t , slot width b s , slot depth h t , the thickness of the secondary conductor plate d , the primary lateral width a 1, the secondary lateral width c 1.

5. The method for optimizing the system-level efficacy of a linear induction motor considering the characteristics of a multi-line wave magnetic field according to claim 3, wherein The influence laws of the harmonic magnetic fields of each pole pair on the motor performance are specifically as follows: the thrust properties provided by the harmonic magnetic fields of each pole pair are different, among which there are both positive thrusts and negative thrusts; by changing the winding arrangement, change the composition of the harmonic magnetic fields of each pole pair, increase the proportion of the positive thrust, so as to enhance the motor performance.

6. The method for optimizing the system-level efficacy of a linear induction motor considering the characteristics of multi-line wave magnetic fields according to claim 1, wherein In the step S3, based on the optimization objectives, analyze the coupling relationship between the optimization variables and the degree of influence on the optimization objectives, hierarchically sort the optimization variables according to the analysis results, and then based on the hierarchical results, use the improved non-dominated sorting genetic algorithm to optimize the variables layer by layer; The influence of the optimization variables on the optimization objectives is expressed as: ， Among them, r is the Pearson correlation coefficient, and S X are the sample mean and standard deviation of the variable X n , and S Y are the sample mean and standard deviation of the variable Y n , n and 1 is the number of samples.

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