Linear induction motor system-level efficacy optimization method considering multi-traveling-wave magnetic field characteristics

Abstract

The invention provides a linear induction motor system-level efficacy optimization method considering multi-traveling-wave magnetic field characteristics. The method comprises the following steps: defining an optimization model: dividing a system into a motor layer and a control layer for optimization design; analyzing an influence rule of a winding arrangement mode and a motor structure parameter on an optimization target based on a linear induction motor chain type equivalent circuit analysis model, and screening out main optimization variables; and performing hierarchical sorting on the optimization variables according to the coupling relationship of the variables. Optimizing variables layer by layer by adopting a genetic algorithm; selecting a final electromagnetic scheme; taking model prediction control based on an efficiency optimization function as a basic framework of motor control, and solving an optimal flux linkage with minimum system loss under each working condition; and with the speed overshoot as an optimization target, a differential evolution algorithm is adopted to carry out iterative optimization on PI control parameters, and better dynamic performance is sought. The efficiency and the power factor of the linear induction motor driving system can be remarkably improved, and the dynamic response capability of the system is effectively ensured.

Description

Technical Field

[0001] The present invention belongs to the field of linear motor optimization, and more specifically, relates to a linear induction motor system-level power efficiency optimization method taking into account multi-wave magnetic field characteristics. Background Art

[0002] Linear induction motors can directly generate linear motion without the need for transmission mechanisms such as gearboxes, and have broad application prospects in urban rail transit drive systems such as subways. Compared with rotary induction motor-driven rail transit systems, linear induction motor drive systems have the advantages of strong climbing ability, small turning radius and small cross-sectional area. However, due to special structures such as the disconnection of the primary iron core and half-filled slots, linear induction motors face serious end effects and rich spatial harmonics, which makes linear induction motor drive systems have problems such as low efficiency and poor dynamic performance.

[0003] In response to the above problems, researchers have carried out a lot of work on optimizing linear induction motor drive systems. However, most of the work is to optimize the design of the main components such as linear induction motors, converters, and controllers separately, and then simply combine them to form a linear induction motor drive system, without optimizing the drive system as a whole. However, due to many factors such as the time-varying nature of multi-parameter cross-coupling caused by the end effect, the complexity of operating conditions, and the nonlinear delay of converters and controllers, the key indicators such as the efficiency of the linear induction motor drive system in actual operation deviate greatly from the theoretical values, and the overall drive performance is difficult to achieve the optimal. In addition, the traditional motor optimization model does not take into account the rich spatial harmonics of the linear induction motor, resulting in a large deviation between the optimized solution and the actual motor performance, further worsening the effect of the drive system optimization.

[0004] Therefore, it is necessary to propose an accurate and efficient linear induction motor system-level power optimization method from the system level to improve the operating performance of the linear induction motor drive system. Summary of the invention

[0005] In view of the defects of the prior art, the purpose of the present invention is to provide a system-level efficiency optimization method for a linear induction motor taking into account the characteristics of multi-wave magnetic field, aiming to solve the problems that the existing traditional device-level optimization methods are not effective and do not take into account all factors.

[0006] To achieve the above object, a method for optimizing the system-level efficiency of a linear induction motor taking into account the characteristics of a multi-wave magnetic field comprises the following steps:

[0007] S1: According to the design requirements of the drive system, define the optimization model: set the optimization goals and constraints, and divide the drive system into two levels: the motor layer and the control layer for optimization design;

[0008] S2: Based on the linear induction motor chain equivalent circuit analysis model, the influence of winding arrangement and motor structural parameters on the optimization target is analyzed, the main optimization variables are screened out, and the optimization range is determined;

[0009] S3: Based on the optimization goal, analyze the internal connection between the optimization variables and sort the optimization variables in layers 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 the appropriate selection principle, and output the corresponding parameters of the electromagnetic scheme to the control system;

[0011] S5: Using model predictive control based on efficiency optimization function as the basic framework of motor control, the optimal flux with the minimum system loss under various working conditions is solved, and the tracking control of the optimal flux is achieved through model predictive control;

[0012] S6: Taking speed overshoot as the optimization target, the differential evolution algorithm is used to iteratively optimize the PI control parameters to seek better dynamic performance;

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

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

[0015] The optimization goal 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 motor layer constraints 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 value of power error before and after motor optimization; τ is the motor pole pitch, λ s is the primary width of the motor, k s is the design empirical 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 F is the maximum length of the motor; n is the normal force, F em is the electromagnetic thrust, k F is the design empirical coefficient; f c is the slot filling rate, f ideal is the ideal slot fill rate, E f It is the allowable value of slot fill rate error.

[0021] The optimization objectives of the control layer are:

[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 control layer constraints are:

[0025] st:{ω os ≤ω limit

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

[0027] The linear induction motor chain equivalent circuit analysis model in step S2 can accurately describe the operating performance of the linear induction motor under different working conditions, and analyze the influence 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 of each optimization variable on the optimization target 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] Among them, B 3yv is the harmonic air gap flux density of each pole pair, j v is the harmonic current layer density of each primary pole pair, both of which are functions of the motor structure parameters.

[0031] The optimization variables include the arrangement of the windings and the structural parameters of the motor, wherein the structural parameters of the motor are: primary tooth width b t , groove width b s , groove 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) The drive system is optimized as a whole, which solves the problem that the optimization of a single device may lead to poor overall performance of the drive system. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a flow chart of a method for optimizing the system-level efficiency of a linear induction motor taking into account the characteristics of a multi-traveling wave magnetic field provided by the present invention;

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

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

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

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

[0048] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and a 3kW laboratory prototype as an embodiment. 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] The present invention provides a flow chart of a method for optimizing the system-level efficiency of a linear induction motor taking into account the characteristics of a multi-wave magnetic field, such as Figure 1 As shown, the specific steps include:

[0050] S1: According to the design requirements of the drive system, define the optimization model: set the optimization goals and constraints, and divide the drive system into two levels: the motor layer and the control layer for optimization design;

[0051] S2: Based on the linear induction motor chain equivalent circuit analysis model, the influence of winding arrangement and motor structural parameters on the optimization target is analyzed, the main optimization variables are screened out, and the optimization range is determined;

[0052] S3: Based on the optimization goal, analyze the internal connection between the optimization variables and sort the optimization variables in layers 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 scheme from the optimized solution set according to the appropriate selection principle, and output the corresponding parameters of the electromagnetic scheme to the control system;

[0054] S5: Using model predictive control based on efficiency optimization function as the basic framework of motor control, the optimal flux with the minimum system loss under various working conditions is solved, and the tracking control of the optimal flux is achieved through model predictive control;

[0055] S6: Taking speed overshoot as the optimization target, the differential evolution algorithm is used 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 present invention is described in detail below using a method, and its logic block diagram is as follows: Figure 2 Shown

[0058] (1) According to the requirements of the linear induction motor drive system, the system is divided into two parts: the motor layer and the control layer for optimization design. The optimization objectives are set as the efficiency, power factor, and dynamic response capability of the linear induction motor. The constraints are the motor size and power, the upper limit of the primary current density constrained by the heat dissipation conditions, the speed overshoot, etc.

[0059] The optimization goal 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 motor layer constraints 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 level of the motor, the rated power error before and after optimization is set to be less than or equal to 50W; τ is the motor pole pitch, λ s J is the primary width of the motor. According to previous design experience, in order to ensure that the motor has a good quality factor and performance, the motor pole length should be less than twice the primary width of the motor. s The primary armature winding current density is less than 5.5A / mm according to the working environment and cooling method of the motor. 2 ; L s F is the primary length of the motor. According to the installation conditions of the motor, its length should not exceed 1.4m; n is the normal force, F emis the electromagnetic thrust. In order to ensure the reliability of its structural strength, the normal force is set to be no more than 4 times the electromagnetic thrust; f c For the slot fill rate, for the feasibility of processing, the slot fill rate should not be too high, but in order to give full play to the performance of the motor, the slot fill rate should not be too low. Therefore, the slot fill rate is set between 0.5 and 0.65.

[0065] The optimization objectives of the control layer are:

[0066] max:f2(x c ) = η system

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

[0068] The control layer constraints are:

[0069] st:{ω os ≤0.01

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

[0071] (2) Motor layer optimization design

[0072] (2-1) The winding arrangement and motor parameters are used as optimization variables, and the Figure 3 The linear induction motor chain equivalent circuit analysis model shown in the figure analyzes the influence of each optimization variable on the optimization target, selects the main optimization variables based on the analysis results, and determines the optimization range. The structural parameters of the motor include the primary tooth width b t , groove width b s , groove depth h t , secondary conductor plate thickness d, primary lateral width a1, secondary lateral width c1, as shown Figure 4 shown.

[0073] (2-2) The optimization of the winding arrangement is based on the influence of the harmonic magnetic field of each pole pair on the motor performance. The thrust provided is of different nature, including both positive thrust and negative thrust. The motor performance can be enhanced by changing the winding arrangement and changing the composition of the harmonic magnetic field of each pole pair, increasing the proportion of positive thrust. However, optimizing the motor winding arrangement alone may result in a motor structure that cannot be processed or has poor topological performance. It is necessary to comprehensively consider the constraint relationship between the winding arrangement and the motor structure parameters to optimize the motor design.

[0074] The steady-state electromagnetic thrust is:

[0075]

[0076] B 3yv+ , B 3yv- is the harmonic forward and reverse air gap flux density of each pole pair, j v+ 、j v- are the harmonic forward and reverse armature current layer densities of each pole pair, which are all functions related to the motor structural parameters. Based on this, the influence of the motor structural parameters and the harmonics of each pole pair on the motor performance can be analyzed.

[0077] Furthermore, based on the optimization goal, the coupling relationship between the optimization variables and the degree of influence on the optimization goal are analyzed. The optimization variables are layered according to the analysis results. Then, based on the layered results, the improved non-dominated sorting genetic algorithm is used 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. The electromagnetic parameters corresponding to the optimized electromagnetic scheme are output to the control layer.

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

[0079]

[0080] 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.

[0081] (3) Control layer optimization design:

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

[0083] The system loss model is:

[0084]

[0085] Among them, b1, b2, b3, b4, b5 are loss coefficients, ψ dris the secondary d-axis flux, n1 is the inverter loss coefficient, f1 is the function of the secondary d-axis flux, the loss model can be differentiated to obtain the secondary d-axis flux with the minimum loss, and then based on the optimal flux, the 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 efficiency and thrust quality during operation.

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

[0087] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles 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 taking into account the characteristics of a multi-wave magnetic field, characterized in that: The following steps are involved: S1: According to the design requirements of the drive system, define the optimization model: set the optimization goals and constraints, and divide the drive system into two levels: the motor layer and the control layer for optimization design; S2: Based on the linear induction motor chain equivalent circuit analysis model, the influence of winding arrangement and motor structural parameters on the optimization target is analyzed, the main optimization variables are screened out, and the optimization range is determined; S3: Based on the optimization goal, analyze the internal connection between the optimization variables and sort the optimization variables in layers according to the coupling relationship of the variables; 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 the appropriate selection principle, and output the corresponding parameters of the electromagnetic scheme to the control system; S5: Using model predictive control based on efficiency optimization function as the basic framework of motor control, the optimal flux with the minimum system loss under various working conditions is solved, and the tracking control of the optimal flux is achieved through model predictive control; S6: Taking speed overshoot as the optimization target, the differential evolution algorithm is used to iteratively optimize the PI control parameters to seek better dynamic performance; S7: Finally, at the system level, determine 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 taking into account the characteristics of a multi-wave magnetic field according to claim 1, characterized in that: The optimization model in step S1 includes two main parts: a motor layer and a control layer; the optimization targets are the linear induction motor, the drive system efficiency, the power factor, and the dynamic response capability; the constraints include: the motor size, power, slot fill rate, the primary current density upper limit constrained by the heat dissipation conditions, and the speed overshoot; The optimization goal of the motor layer is: Among them, η m is the motor efficiency, is the motor power factor, x m is the optimization variable of the motor layer; The motor layer constraints are: 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 value of power error before and after motor optimization; τ is the motor pole pitch, λ s is the primary width of the motor, k s is the design empirical coefficient; J s 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 F is the maximum length of the motor; n is the normal force, F em is the electromagnetic thrust, k F is the design empirical coefficient; f c is the slot filling rate, f ideal is the ideal slot fill rate, E f is the allowable value of slot fill rate error; The optimization objectives of the control layer are: max:f2(x c )=η system where η system is the motor system efficiency, x c is the control system parameter; The control layer constraints are: st:{ω os ≤ω limit where 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 taking into account the characteristics of a multi-wave magnetic field according to claim 1, characterized in that: The linear induction motor chain equivalent circuit analysis model in step S2 accurately describes the operating performance of the linear induction motor under different working conditions, and analyzes the influence 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 of each optimization variable on the optimization target is obtained, and the main optimization variables are selected according to the analysis results, and the optimization range is determined; The electromagnetic steady-state thrust calculation formula is: Among them, B 3yv is the harmonic air gap flux density of each pole pair, j v is the harmonic current layer density of each primary pole pair, both of which are functions of the motor structure parameters.

4. The method for optimizing the system-level efficiency of a linear induction motor taking into account the characteristics of a multi-wave magnetic field according to claim 3, characterized in that: The optimization variables include the arrangement of the windings and the structural parameters of the motor, wherein the structural parameters of the motor are: primary tooth width b t , groove width b s , groove depth h t , secondary conductor plate thickness d, primary lateral width a1, secondary lateral width c1.

5. The method for optimizing the system-level efficiency of a linear induction motor taking into account the characteristics of a multi-wave magnetic field according to claim 3, characterized in that: The specific law of the influence of the harmonic magnetic field of each pole logarithm on the performance of the motor is as follows: the thrust provided by the harmonic magnetic field of each pole logarithm is different in nature, including both positive thrust and negative thrust; by changing the winding arrangement, the composition of the harmonic magnetic field of each pole logarithm is changed, and the proportion of positive thrust is increased, thereby enhancing the performance of the motor.

6. The method for optimizing the system-level efficiency of a linear induction motor taking into account the characteristics of a multi-wave magnetic field according to claim 1, characterized in that: 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, the optimization variables are sequentially stratified according to the analysis results, and then based on the stratification results, the improved non-dominated sorting genetic algorithm is used to optimize the variables layer by layer; The influence of optimization variables on optimization objectives is expressed as: 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.

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