CN117131667A - Optimization method and system for notch permanent magnet type hybrid excitation doubly salient motor - Google Patents
Optimization method and system for notch permanent magnet type hybrid excitation doubly salient motor Download PDFInfo
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Abstract
The invention relates to a method and a system, which relate to the technical field of slot permanent magnet hybrid excitation motors, and comprise the steps of restraining optimization through the limitation of slot fullness rate and winding current density to obtain an optimization model equation and a multi-target weighted objective function; performing dimension reduction and layering on motor optimization structural parameters by combining a DOE experiment method and comprehensive sensitivity; and optimizing the RBF mathematical model by using an improved NSGA-II algorithm, and calculating a Pareto solution input multi-objective weighting function to obtain the optimal structure of the motor. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor provided by the invention designs and optimizes the motor body under the limit of the limited slot area, so that the performance of the motor is maximized, and the rationality of the motor in actual manufacturing is fully ensured. The invention achieves better effects in terms of calculation cost, rationality and local sensitivity.
Description
Technical Field
The invention relates to the technical field of notch permanent magnet hybrid excitation motors, in particular to an optimization method of a notch permanent magnet hybrid excitation doubly salient motor.
Background
With the rapid development of new energy technology and urgent need in the electrical field, the generator plays an important role as an important component element of the power system, so that research and design of a motor structure with strong performance, high fault tolerance and low fluctuation have important significance. Notch permanent magnet type hybrid excitation doubly salient motors become an important direction in the field of motor development due to excellent output performance, controllable magnetic flux regulating capability and certain fault tolerance.
Many researches at present propose various topological structures aiming at the slot permanent magnet type hybrid excitation doubly salient motor, quite excellent motor performance is obtained, and inherent limitations of the motor body are not considered. Because all excitation sources and armature windings of the hybrid excitation stator side motor are arranged on the stator side, and the stator slot area is limited, if reasonable planning is not carried out, the motor efficiency is low, and the motor is difficult to work in an actual prototype, so that the theoretical effect cannot be achieved. It is necessary to design and optimize the motor body to maximize motor performance with limited slot area by reasonably distributing the stator slot area.
The body optimization of the motor is a multi-objective, nonlinear and strong coupling problem, and in order to improve the comprehensive performance of the motor, a plurality of targets need to be simultaneously optimized under a certain constraint. However, most of the existing optimization technologies are optimization problems of optimizing a single target of a motor or simply converting a plurality of target amounts into a single target function during multi-target optimization, and the optimization means cannot realize weight control on the optimization target, and a certain degree of distortion is caused to an optimization result with high probability, so that the final optimization result of the motor lacks comprehensiveness and advancement.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing motor optimization method has the problems of low efficiency, lack of comprehensiveness and advancement, and how to realize weight control on an optimization target.
In order to solve the technical problems, the invention provides the following technical scheme: an optimization method of a slot permanent magnet type hybrid excitation doubly salient motor comprises the following steps: constraint is carried out on optimization through the limitation of the slot fullness rate and the winding current density, and an optimization model equation and a multi-objective weighted objective function are obtained; performing dimension reduction and layering on motor optimization structural parameters by combining a DOE experiment method and comprehensive sensitivity; and optimizing the RBF mathematical model by using an improved NSGA-II algorithm, and calculating a Pareto solution input multi-objective weighting function to obtain the optimal structure of the motor.
As a preferable scheme of the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor, the optimization method comprises the following steps: the constraints on optimization include limits on slot fill rate and winding current density; based on the limitation of two-dimensional theory calculation and the limitation of the process in the actual winding, the slot fullness rate K constraint condition is expressed as follows:
wherein S is A For the area of the single-side alternating-current winding in each stator slot, S DC S is the area of all direct current excitation windings in each stator slot PM For the groove area occupied by each permanent magnet, S Groove(s) The area of the whole stator slot; the winding current density J is based on the state of the coil during the operation of the motor DC And J A The constraint of (2) is expressed as:
wherein J is DC For the current density of the exciting coil, J A I is the current density in the armature coil dc For exciting winding current, N dc For exciting winding turns, I rms N is the number of turns of the armature winding; according to constraint S A The resistance R of each phase armature winding is obtained as follows:
wherein n is the number of stator poles of each phase, ρ is the resistivity of the armature coil, L is the axial length of the motor, t s Is the stator width of the motor.
As a preferable scheme of the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor, the optimization method comprises the following steps: the optimization model equation and the multi-objective weighted objective function comprise an optimization model equation which puts forward constraints and objective compositions, expressed as:
wherein P is output power, U ri To output voltage ripple, P a T is the copper loss of the armature ri To output torque ripple; the multi-objective weighted objective function is expressed as follows according to the optimized weight and in combination with the sample library mean value:
wherein x is i As a parameter variable, a parameter value is used,and->Average of samples of output power, voltage ripple, armature copper loss, and torque ripple, respectively, P (x) i )、U ri (x i )、P a (x i ) And T ri (x i ) Optimized values of output power, voltage ripple, armature copper loss and torque ripple, respectively, weight coefficient lambda 1 、λ 2 、λ 3 And lambda (lambda) 4 Respectively the importance of each target in the optimization process, and lambda 1 +λ 2 +λ 3 +λ 4 ＝1。
As a preferable scheme of the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor, the optimization method comprises the following steps: the integrated sensitivity includes calculating a Pearson correlation coefficient r xiyi Expressed as:
wherein x is i For the ith design parameter, y i For the ith optimization objective, N is the sample size; generating an S-row orthogonal table by a DOE experiment method, selecting S groups of fixed sizes, and expressing the sensitivity index H (X) of a single coefficient to a single optimization target by using an S group coefficient mean value i ) Expressed as:
wherein, the integrated sensitivity G (X) i ) Expressed as:
G(X i )＝λ 1 |H 1 (X i )|+λ 2 |H 2 (X i )|+…+λ k |H k (X i )|
wherein H is 1 (X i )、H 2 (X i ) And H k (X i ) Sensitivity index, lambda, of design coefficients to 1 st, 2 nd and k th targets, respectively 1 、λ 2 And lambda (lambda) k Respectively, weight indexes of different target importance degrees.
As a preferable scheme of the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor, the optimization method comprises the following steps: the step-down and layering of the motor optimized structural parameters comprises the steps of dividing the comprehensive sensitivity into a low-sensitivity layer and a high-sensitivity layer, determining coefficients of the low-sensitivity layer by a single-parameter scanning method, selecting n horizontal components for m coefficients of the high-sensitivity layer, and obtaining n by finite elements m And (3) carrying out normalization treatment on the group sample database, and carrying out normalization treatment on the database according to the following steps of 4: the 1 proportion is divided into a training sample and a detection sample, and an RBF mathematical model is established.
As a preferable scheme of the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor, the optimization method comprises the following steps: the optimization of the mathematical model by using the improved NSGA-II algorithm comprises the steps of introducing an initial population generated by a set of good points, and taking N sets of good points under a four-dimensional space, wherein the N sets of good points are expressed as follows:
P(i)＝{(r 1 i 1 ,r 2 i 2 ,...,r 4 i 4 },i＝1,2,3,...,N
wherein N is population number, r= (r 1 ,r 2 ,...,r 4 ) For the best point, r j Expressed as:
wherein P is the minimum prime number satisfying P.gtoreq.2X4+3, and r is j Mapping to a search space, expressed as:
wherein ua j And la j The upper and lower limits of the j-th dimension, respectively.
As a preferable scheme of the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor, the optimization method comprises the following steps: the method comprises the steps of optimizing a logarithmic model by using an improved NSGA-II algorithm, sorting a combined population, sorting from small to large according to an adaptation value, sorting the crowding distance in an ascending order, determining the probability of each group of variables being selected according to a linear function, and assigning the probability P (i) of selecting an individual with a rank i according to the sorting order to be expressed as:
wherein M is the number of combined populations, eta + And eta - For a given coefficient, satisfy eta + +η - =2, and 0.ltoreq.η - Is less than or equal to 1; gradually increasing the selection pressure of the parent population in the algorithm iteration process, eta - The values of (2) are expressed as:
wherein n is the current algebra; constraint conditions are applied to the merged population, and the constraint model is expressed as:
wherein x is i Optimizing variables in the merging population; and obtaining an optimized Pareto solution input multi-objective weighting function after m iterations to obtain an optimal structure of the motor.
Another object of the present invention is to provide an optimization system for a slot permanent magnet type hybrid excitation doubly salient motor, which can define and manage constraint conditions through a constraint optimization module, and solve the problem that various constraint conditions cannot be accurately processed and satisfied at present.
As a preferable scheme of the optimizing system of the notch permanent magnet type hybrid excitation doubly salient motor, the optimizing system comprises the following steps: the system comprises a constraint optimization module, a dimension reduction layering module and a multi-objective optimization calculation module; the constraint optimization module is used for defining and managing constraint conditions in the design problem, collecting the constraint conditions and converting the constraint conditions into constraint equations in a mathematical form; the dimension reduction layering module sets design variables to different values by using a DOE experiment method, analyzes and models experiment results, and performs parameter optimization by adopting an optimization algorithm based on sensitivity analysis; the multi-objective optimization calculation module is used for defining an optimization objective, searching a design space by using an improved NSGA-II multi-objective optimization algorithm, generating a non-dominant solution, constructing the non-dominant solution into a Pareto solution, mapping the Pareto solution into a single solution space, and obtaining the optimal structure of the motor.
A computer device comprising a memory and a processor, said memory storing a computer program, characterized in that execution of said computer program by said processor is the step of implementing a method for optimizing a slot permanent magnet hybrid excitation doubly salient motor.
A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a method for optimizing a slot permanent magnet hybrid excitation doubly salient motor.
The invention has the beneficial effects that: the optimization method of the notch permanent magnet type hybrid excitation doubly salient motor fully considers the limitation of theoretical calculation and the limitation of the process in actual winding, reasonably plans the stator slot area by combining the characteristic of limited stator side motor slot area, designs and optimizes the motor body under the limitation of limited slot area, maximizes the motor performance and fully ensures the rationality of the motor in actual manufacturing. The DOE experimental method and the comprehensive sensitivity are combined to perform dimension reduction and layering on the optimized structural parameters of the motor, so that the local sensitivity under a certain fixed size is eliminated, and the calculation cost of later optimization is greatly reduced. The invention achieves better effects in terms of calculation cost, rationality and local sensitivity.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. Wherein:
fig. 1 is an overall flowchart of an optimization method of a slot permanent magnet type hybrid excitation doubly salient motor according to a first embodiment of the present invention.
Fig. 2 is a graph of an optimization result before an algorithm of an optimization method of a slot permanent magnet type hybrid excitation doubly salient motor according to a second embodiment of the present invention is improved.
Fig. 3 is a graph of an optimized result after an algorithm of an optimization method of a slot permanent magnet type hybrid excitation doubly salient motor according to a second embodiment of the present invention is improved.
Fig. 4 is an overall flowchart of an optimization system of a slot permanent magnet type hybrid excitation doubly salient motor according to a third embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention, but the present invention may be practiced in other ways other than those described herein, and persons skilled in the art will readily appreciate that the present invention is not limited to the specific embodiments disclosed below.
Further, reference herein to "one embodiment" or "an embodiment" means that a particular feature, structure, or characteristic can be included in at least one implementation of the invention. The appearances of the phrase "in one embodiment" in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments.
While the embodiments of the present invention have been illustrated and described in detail in the drawings, the cross-sectional view of the device structure is not to scale in the general sense for ease of illustration, and the drawings are merely exemplary and should not be construed as limiting the scope of the invention. In addition, the three-dimensional dimensions of length, width and depth should be included in actual fabrication.
Also in the description of the present invention, it should be noted that the orientation or positional relationship indicated by the terms "upper, lower, inner and outer", etc. are based on the orientation or positional relationship shown in the drawings, are merely for convenience of describing the present invention and simplifying the description, and do not indicate or imply that the apparatus or elements referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as limiting the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
The terms "mounted, connected, and coupled" should be construed broadly in this disclosure unless otherwise specifically indicated and defined, such as: can be fixed connection, detachable connection or integral connection; it may also be a mechanical connection, an electrical connection, or a direct connection, or may be indirectly connected through an intermediate medium, or may be a communication between two elements. The specific meaning of the above terms in the present invention will be understood in specific cases by those of ordinary skill in the art.
Example 1
Referring to fig. 1, for one embodiment of the present invention, there is provided an optimization method of a slot permanent magnet type hybrid excitation doubly salient motor, including:
s1: and constraining the optimization through the limitation of the slot fullness rate and the winding current density to obtain an optimization model equation and a multi-objective weighted objective function.
Still further, constraints on optimization include limits on slot fill rate and winding current density.
It should be noted that, according to the limitations of two-dimensional theory calculation and the limitations of the process in actual winding, the slot fullness rate K cannot exceed 0.5, the constraint is expressed as:
wherein S is A For the area of the single-side alternating-current winding in each stator slot, S DC S is the area of all direct current excitation windings in each stator slot PM For the groove area occupied by each permanent magnet, S Groove(s) The area of the whole stator slot; the winding current density J is based on the state of the coil during the operation of the motor DC And J A Cannot exceed 10A/mm 2 The constraint is expressed as:
wherein J is DC For the current density of the exciting coil, J A I is the current density in the armature coil dc For exciting winding current, N dc For exciting winding turns, I rms N is the number of turns of the armature winding; according to constraint S A The resistance R of each phase armature winding is obtained as follows:
wherein n is the number of stator poles of each phase, ρ is the resistivity of the armature coil, L is the axial length of the motor, t s When the structural parameters of the motor body are changed, R is also changed for the width of the stator of the motor.
Further, the optimization model equation and the multi-objective weighted objective function include an optimization model equation that proposes constraints and objective compositions.
It should be noted that the optimization model equation for constraint and target composition is expressed as:
wherein P is output power, U ri To output voltage ripple, P a T is the copper loss of the armature ri To output torque ripple; the multi-objective weighted objective function is expressed as follows according to the optimized weight and in combination with the sample library mean value:
wherein x is i As a parameter variable, a parameter value is used,and->Average of samples of output power, voltage ripple, armature copper loss, and torque ripple, respectively, P (x) i )、U ri (x i )、P a (x i ) And T r i (xi) is the optimized value of output power, voltage pulsation, armature copper loss and torque pulsation, respectively, weight coefficient lambda 1 、λ 2 、λ 3 And lambda (lambda) 4 Respectively the importance of each target in the optimization process, and lambda 1 +λ 2 +λ 3 +λ 4 =1, respectively set λ according to importance 1 、λ 2 、λ 3 And lambda (lambda) 4 0.35, 0.15 and 0.15, F (x) i ) min The smaller the value of (c), the better the motor optimization.
S2: and carrying out dimension reduction and layering on the motor optimization structural parameters by combining a DOE experimental method and comprehensive sensitivity.
Still further, the integrated sensitivity includes a sensitivity index to a single optimization objective and an integrated sensitivity after introducing an added weight coefficient.
It should be noted that Pearson correlation coefficientExpressed as:
wherein x is i For the ith design parameter, y i For the ith optimization objective, N is the sample size; generating an S-row orthogonal table by a DOE experiment method, selecting S groups of fixed sizes, and expressing the sensitivity index H (X) of a single coefficient to a single optimization target by using an S group coefficient mean value i ) Expressed as:
wherein, the integrated sensitivity G (X) i ) Expressed as:
G(X i )＝λ 1 |H 1 (X i )|+λ 2 |H 2 (X i ) | +λ 3 |H 3 (X i )|+λ 4 |H 4 (X i )|
wherein H is 1 (X i )、H 2 (X i )、H 3 (X i ) And H 4 (X i ) The sensitivity index of the design parameters to output power, voltage ripple, armature copper loss, and torque ripple, respectively.
Further, the step-down and layering of motor optimization structural parameters includes dividing the integrated sensitivity into a low sensitivity layer and a high sensitivity layer.
As shown in Table 1, h pm 、h s 、t s 、h r 、t r And d n The thickness of the permanent magnet, the length of the stator pole, the width of the stator pole, the length of the rotor pole, the width of the rotor pole and the inner diameter of the rotor are respectively represented by G (X) i ) Dividing the design parameters into two layers, h, =0.1 as boundary r And d n The method is low in sensitivity level, and is directly optimized by adopting a single-parameter scanning method; h is a pm 、h s 、t s And t r For high sensitivity level, after the data sample base is simulated, a mathematical model is established and then optimized by an improved NSGA-II algorithm.
TABLE 1 sensitivity analysis results
Selecting 5 horizontal components for 4 parameters of a high sensitive layer, carrying out finite element simulation to obtain 625 groups of sample databases, carrying out normalization processing, and carrying out database normalization processing according to 4:1 is divided into a training sample and a detection sample, and an RBF mathematical model is established.
S3: and optimizing the RBF mathematical model by using an improved NSGA-II algorithm, and calculating a Pareto solution input multi-objective weighting function to obtain the optimal structure of the motor.
Further, optimizing the mathematical model using the modified NSGA-ii algorithm includes introducing a set of points of merit to generate an excellent initial population.
It should be noted that taking N sets of points in four-dimensional space is expressed as:
P(i)＝{(r 1 i 1 ,r 2 i 2 ,...,r 4 i 4 },i＝1,2,3,...,N
wherein N is population number, r= (r 1 ,r 2 ,...,r 4 ) For the best point, r j Expressed as:
wherein P is the minimum prime number satisfying P.gtoreq.2X4+3, and r is j Mapping to a search space, expressed as:
wherein ua j And la j The upper and lower limits of the j-th dimension, respectively.
Still further, optimizing the mathematical model using the modified NSGA-ii algorithm further includes ranking the pooled populations.
It should be noted that, sorting from small to large according to the adaptation value, then sorting the congestion distance in ascending order, determining the probability of each group of variables being selected according to the linear function, and assigning the selection probability P (i) to the individual ranked i according to the sorting order as follows:
wherein M is the number of combined populations, eta + And eta - For a given coefficient, satisfy eta + +η - =2, and 0.ltoreq.η - Is less than or equal to 1; gradually increasing the selection pressure of the parent population in the algorithm iteration process, eta - The values of (2) are expressed as:
wherein n is the current algebra; constraint conditions are applied to the merged population, and the constraint model is expressed as:
wherein x is i Optimizing variables in the merging population; only when corresponding P, U ri 、P a And T ri The set of variables is considered to be undistorted only if the values of the four optimization objectives are all better than the average value in the sample library. At this time, the population after k iterations in the early stage of the algorithm is excellent enough, so that the number of the constrained population is ensured to be larger than N, and the next generation subgroup can be smoothly generated. And obtaining an optimized Pareto solution input multi-objective weighting function after m iterations to obtain an optimal structure of the motor.
Example 2
Referring to fig. 2-3, for one embodiment of the present invention, an optimization method of a slot permanent magnet type hybrid excitation doubly salient motor is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
And obtaining an optimized group of Pareto solutions after m iterations, substituting the optimized group of Pareto solutions into the multi-objective weighting function to obtain the optimal structure of the motor. And on the premise of ensuring the optimization accuracy, weighing the calculated cost, setting the early iteration number k as 200, setting the total iteration number m as 500, setting the population size N as 100, setting the cross proportion as 0.7, setting the variation proportion as 0.4 and setting the variation rate as 0.02, and obtaining the comparison of the optimization results before and after the algorithm improvement.
From fig. 2 and 3, it can be seen that the Pareto solution set after the algorithm is improved is more concentrated and more approximates to the Pareto front, and more excellent individuals can be generated.
As shown in table 2, table 3 shows that the optimization results before and after the algorithm improvement are different, but generally better than the initial size and the average value of the sample library, only the output torque pulsation performance before the algorithm improvement is distorted, and the output torque pulsation performance is higher than the average value. Although the armature copper loss before algorithm improvement is small, the balance of multiple targets of the motor is considered, and the result is not considered. The improved target result performance of the algorithm is excellent, the output power performance is improved by 19.8% compared with the average value of a sample library, the armature copper loss is reduced by 6.7%, the output voltage pulsation is reduced by 24.5%, and the output torque pulsation is reduced by 21.5%. Through finite element analysis of structural parameters optimized after algorithm improvement, the result of algorithm optimization is quite close to the simulation result, the output power error is 0.45%, the armature copper loss error is 0.54%, the output voltage pulsation error is 0.3%, and the output torque pulsation error is 0.19%. At this time, the current density in the armature coil was calculated to be 5.61A/mm based on the finite element analysis 2 The requirements on current density are met, and the values of motor output power and output voltage pulsation meet the constraint conditions in an optimization model equation, so that the optimization result meets the design requirements.
Table 2 motor structural parameter comparison
| Structural parameters | Before optimization | Before algorithm improvement | After the algorithm is improved |
| h pm /mm | 4 | 2.7 | 3.3 |
| h s /mm | 36.5 | 38.4 | 38.1 |
| t s /mm | 8 | 9.2 | 9.3 |
| h r /mm | 12.5 | 13 | 13 |
| t r /mm | 8.4 | 7.4 | 10.6 |
| d n /mm | 100 | 102 | 102 |
TABLE 3 optimization target results comparison
| Optimization objective | Before optimization | Sample library mean | Before algorithm improvement | After the algorithm is improved | Post-improvement simulation |
| Output power/W | 3327.2 | 3032 | 3502.6 | 3649 | 3632.5 |
| Copper loss/W of armature | 244.5 | 198.2 | 178.6 | 186 | 185 |
| Output voltage ripple | 0.137 | 0.132 | 0.1007 | 0.0994 | 0.0997 |
| Output torque ripple | 0.305 | 0.337 | 0.4047 | 0.2639 | 0.2644 |
Example 3
Referring to fig. 4, for one embodiment of the present invention, an optimization system of a slot permanent magnet hybrid excitation doubly salient motor is provided, which includes a constraint optimization module, a dimension reduction layering module, and a multi-objective optimization calculation module.
The constraint optimization module is used for defining and managing constraint conditions in the design problem, collecting the constraint conditions and converting the constraint conditions into constraint equations in a mathematical form; the dimension reduction layering module sets design variables to different values by using a DOE experiment method, analyzes and models experiment results, and performs parameter optimization by adopting an optimization algorithm based on sensitivity analysis; the multi-objective optimization calculation module is used for defining an optimization objective, searching a design space by using an improved NSGA-II multi-objective optimization algorithm, generating a non-dominant solution, constructing the non-dominant solution into a Pareto solution, mapping the Pareto solution into a single solution space, and obtaining the optimal structure of the motor.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. The optimizing method of the slot permanent magnet type hybrid excitation doubly salient motor is characterized by comprising the following steps of:
constraint is carried out on optimization through the limitation of the slot fullness rate and the winding current density, and an optimization model equation and a multi-objective weighted objective function are obtained;
performing dimension reduction and layering on motor optimization structural parameters by combining a DOE experiment method and comprehensive sensitivity;
and optimizing the RBF mathematical model by using an improved NSGA-II algorithm, and calculating a Pareto solution input multi-objective weighting function to obtain the optimal structure of the motor.
2. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor according to claim 1, wherein the optimization method comprises the following steps: the constraints on optimization include limits on slot fill rate and winding current density;
according to the limitation of two-dimensional theory calculation, the slot fullness rate K constraint condition is expressed as follows:
wherein S is A For the area of the single-side alternating-current winding in each stator slot, S DC S is the area of all direct current excitation windings in each stator slot PM For the groove area occupied by each permanent magnet, S Groove(s) The area of the whole stator slot;
the winding current density J is based on the state of the coil during the operation of the motor DC And J A The constraint of (2) is expressed as:
wherein J is DC For the current density of the exciting coil, J A I is the current density in the armature coil dc For exciting winding current, N dc For exciting winding turns, I rms N is the number of turns of the armature winding; according to constraint S A The resistance R of each phase armature winding is obtained as follows:
wherein n is the number of stator poles of each phase, ρ is the resistivity of the armature coil, L is the axial length of the motor, t s Is the stator width of the motor.
3. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor according to claim 1, wherein the optimization method comprises the following steps: the optimization model equation and the multi-objective weighted objective function comprise an optimization model equation which puts forward constraints and objective compositions, expressed as:
wherein P is output power, U ri To output voltage ripple, P a T is the copper loss of the armature ri To output torque ripple;
the multi-objective weighted objective function is expressed as follows according to the optimized weight and in combination with the sample library mean value:
wherein x is i As a parameter variable, a parameter value is used,and->Average of samples of output power, voltage ripple, armature copper loss, and torque ripple, respectively, P (x) i )、U ri (x i )、P a (x i ) And T ri (x i ) Optimized values of output power, voltage ripple, armature copper loss and torque ripple, respectively, weight coefficient lambda 1 、λ 2 、λ 3 And lambda (lambda) 4 Respectively the importance of each target in the optimization process, and lambda 1 +λ 2 +λ 3 +λ 4 ＝1。
4. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor according to claim 1, wherein the optimization method comprises the following steps: the integrated sensitivity includes calculating Pearson correlation coefficientsExpressed as:
wherein x is i For the ith design parameter, y i For the ith optimization objective, N is the sample size;
generating an S-row orthogonal table by a DOE experiment method, selecting S groups of fixed sizes, and expressing the sensitivity index H (X) of a single coefficient to a single optimization target by using an S group coefficient mean value i ) Expressed as:
wherein, the integrated sensitivity G (X) i ) Expressed as:
G(X i )＝λ 1 |H 1 (X i )|+λ 2 |H 2 (X i )|+…+λ k |H k (X i )|
wherein H is 1 (X i )、H 2 (X i ) And H k (X i ) Sensitivity index, lambda, of design coefficients to 1 st, 2 nd and k th targets, respectively 1 、λ 2 And lambda (lambda) k Respectively, weight indexes of different target importance degrees.
5. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor according to claim 1, wherein the optimization method comprises the following steps: the step-down and layering of the motor optimized structural parameters comprises the steps of dividing the comprehensive sensitivity into a low-sensitivity layer and a high-sensitivity layer, determining coefficients of the low-sensitivity layer by a single-parameter scanning method, selecting n horizontal components for m coefficients of the high-sensitivity layer, and obtaining n by finite elements m And (3) carrying out normalization treatment on the group sample database, and carrying out normalization treatment on the database according to the following steps of 4: the 1 proportion is divided into a training sample and a detection sample, and an RBF mathematical model is established.
6. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor according to claim 1, wherein the optimization method comprises the following steps: the optimizing of the RBF mathematical model by using the improved NSGA-II algorithm comprises the steps of introducing an initial population generated by a set of good points, and taking N sets of good points under a four-dimensional space, wherein the N sets of good points are expressed as follows:
P(i)＝{(r 1 i 1 ,r 2 i 2 ,...,r 4 i 4 },i＝1,2,3,...,N
wherein N is population number, r= (r 1 ,r 2 ,...,r 4 ) For the best point, r j Expressed as:
wherein P is the minimum prime number satisfying P.gtoreq.2X4+3, and r is j Mapping to a search space, expressed as:
wherein ua j And la j The upper and lower limits of the j-th dimension, respectively.
7. The optimization method of the notch permanent magnet type hybrid excitation doubly salient motor according to claim 6, wherein: the optimizing of the RBF mathematical model by using the improved NSGA-II algorithm further comprises the steps of sorting the combined population, sorting from small to large according to the adaptation value, sorting the crowding distance in ascending order, determining the selected probability of each group of variables according to the linear function, and assigning the selected probability P (i) to the individuals in the ranking i according to the sorting order as follows:
wherein M is a combined populationQuantity, eta + And eta - For a given coefficient, satisfy eta + +η - =2, and 0.ltoreq.η - Is less than or equal to 1; gradually increasing the selection pressure of the parent population in the algorithm iteration process, eta - The values of (2) are expressed as:
wherein n is the current algebra;
constraint conditions are applied to the merged population, and the constraint model is expressed as:
wherein x is i Optimizing variables in the merging population;
and obtaining an optimized Pareto solution input multi-objective weighting function after m iterations to obtain an optimal structure of the motor.
8. A system employing the method of optimizing a slot permanent magnet hybrid excitation doubly salient motor as claimed in any one of claims 1 to 7, characterized in that: the system comprises a constraint optimization module, a dimension reduction layering module and a multi-objective optimization calculation module;
the constraint optimization module is used for defining and managing constraint conditions in the design problem, collecting the constraint conditions and converting the constraint conditions into constraint equations in a mathematical form;
the dimension reduction layering module sets design variables to different values by using a DOE experiment method, analyzes and models experiment results, and performs parameter optimization by adopting an optimization algorithm based on sensitivity analysis;
the multi-objective optimization calculation module is used for defining an optimization objective, searching a design space by using an improved NSGA-II multi-objective optimization algorithm, generating a non-dominant solution, constructing the non-dominant solution into a Pareto solution, mapping the Pareto solution into a single solution space, and obtaining the optimal structure of the motor.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, carries out the steps of the method for optimizing a slot permanent magnet hybrid excitation doubly salient motor according to any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor realizes the steps of the optimizing method of the slot permanent magnet hybrid excitation doubly salient motor according to any one of claims 1 to 7.
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