基于Ansoft的永磁同步電機結(jié)構(gòu)參數(shù)優(yōu)化研究
發(fā)布時間：2018-09-05 13:27
【摘要】：對永磁同步電機(Permanent Magnet Synchronous Motor,PMSM)的研究不斷加深,促進了電機優(yōu)化設(shè)計的發(fā)展,也對優(yōu)化算法提出了更高的要求。PMSM結(jié)構(gòu)多樣,加大了內(nèi)部磁場的復(fù)雜程度,導(dǎo)致等效磁路法等傳統(tǒng)方法無法達到需要的精度。電磁場數(shù)值分析雖然具備良好的精確性,但計算消耗過大,因此需要一種新型算法來縮短電機設(shè)計的周期。本文從PMSM基本尺寸的確定和傳統(tǒng)性能分析方法開始,經(jīng)過二維有限元法相關(guān)理論的介紹,過渡到電磁場數(shù)值分析方法,在此基礎(chǔ)上利用電機設(shè)計分析軟件建立電機的初始模型,并對電機性能進行分析計算。為改善電機性能,選取磁極厚度、極弧系數(shù)、氣隙長度以及偏心距為設(shè)計變量,齒槽轉(zhuǎn)矩、空載氣隙磁密波形正弦畸變率為目標(biāo)函數(shù)進行優(yōu)化。首先通過仿真實驗確定各變量的取值范圍,然后設(shè)計正交試驗來獲取回歸分析所需的樣本空間;接著,分別建立2個目標(biāo)函數(shù)基于支持向量機(Support Vector Machine,SVM)的響應(yīng)面模型,并通過引入變異操作的微粒群優(yōu)化算法(Particle Swarm Optimization,PSO)分別對2個目標(biāo)函數(shù)進行單目標(biāo)優(yōu)化;將優(yōu)化結(jié)果代入有限元軟件,齒槽轉(zhuǎn)矩由初始的4.37N?m降為0.264N?m,空載氣隙磁密波形正弦畸變率由29.14%降為17.36%,仿真實驗驗證了結(jié)果的準確性;最后,鑒于實際的電機優(yōu)化一般均為多目標(biāo)優(yōu)化問題,因此將兩個目標(biāo)函數(shù)放在一個優(yōu)化過程中,采用PSO同時對其進行多目標(biāo)優(yōu)化,仿真后齒槽轉(zhuǎn)矩為0.31N?m,空載氣隙正弦畸變率為22.33%,兩者均比較理想�！癝VM+PSO”算法優(yōu)化效果良好,可以保證較高的準確性;同時需要的樣本空間小,尋優(yōu)歷經(jīng)的進化代數(shù)少、收斂速度快;兩種算法結(jié)合后延續(xù)了各自的先進性。此外,SVM回歸分析采用了“黑箱方法”,大大降低了相關(guān)人員對電機知識的依賴,有效簡化了電機設(shè)計過程;由于算法的通用性,也為電機其他性能參數(shù)的優(yōu)化提供了指導(dǎo)與借鑒。
[Abstract]:The research on PMSM (permanent Magnet synchronous Motor) has been deepened, which has promoted the development of the optimal design of PMSM, and put forward higher requirements for the optimization algorithm. The structure of PMSM is diverse, and the complexity of the internal magnetic field is increased. As a result, traditional methods such as equivalent magnetic circuit method can not achieve the required accuracy. Although the numerical analysis of electromagnetic field has good accuracy, the calculation consumption is too large, so a new algorithm is needed to shorten the period of motor design. This paper begins with the determination of the basic dimensions of PMSM and the traditional performance analysis method, through the introduction of the relevant theory of two-dimensional finite element method to the electromagnetic field numerical analysis method, on this basis, the initial model of the motor is established by using the motor design and analysis software. The performance of the motor is analyzed and calculated. In order to improve the performance of the motor, the magnetic pole thickness, polar arc coefficient, air gap length and eccentricity are selected as design variables, the tooth slot torque and sinusoidal distortion rate of no-load air-gap magnetic density waveform are selected as objective functions to optimize. First, the range of values of each variable is determined by simulation experiments, and then orthogonal test is designed to obtain the sample space required for regression analysis. Then, two response surface models of objective functions based on support vector machine (Support Vector Machine,SVM) are established, respectively. The particle swarm optimization algorithm (Particle Swarm Optimization,PSO) with mutation operation is introduced to optimize the two objective functions, and the optimization results are added to the finite element software. The slotted torque is reduced from the initial 4.37N?m to 0.264 Nm, and the sinusoidal distortion rate of the no-load air-gap magnetic density waveform is reduced from 29.14% to 17.36. The simulation results verify the accuracy of the results. Therefore, the two objective functions are put into one optimization process, and the multi-objective optimization is carried out simultaneously by using PSO. The simulation results show that the slotting torque is 0.31 NM, and the sinusoidal distortion rate of no-load air gap is 22.33. Both of them are ideal. "SVM PSO" algorithm has good optimization effect. It can ensure higher accuracy; at the same time, the sample space is small, the evolution algebra is less, and the convergence speed is fast. The two algorithms combine to continue their advanced nature. In addition, SVM regression analysis adopts "black box method", which greatly reduces the dependence of related personnel on motor knowledge, and simplifies the process of motor design effectively. It also provides guidance and reference for the optimization of other performance parameters of motor.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM341
本文編號：2224408
[Abstract]:The research on PMSM (permanent Magnet synchronous Motor) has been deepened, which has promoted the development of the optimal design of PMSM, and put forward higher requirements for the optimization algorithm. The structure of PMSM is diverse, and the complexity of the internal magnetic field is increased. As a result, traditional methods such as equivalent magnetic circuit method can not achieve the required accuracy. Although the numerical analysis of electromagnetic field has good accuracy, the calculation consumption is too large, so a new algorithm is needed to shorten the period of motor design. This paper begins with the determination of the basic dimensions of PMSM and the traditional performance analysis method, through the introduction of the relevant theory of two-dimensional finite element method to the electromagnetic field numerical analysis method, on this basis, the initial model of the motor is established by using the motor design and analysis software. The performance of the motor is analyzed and calculated. In order to improve the performance of the motor, the magnetic pole thickness, polar arc coefficient, air gap length and eccentricity are selected as design variables, the tooth slot torque and sinusoidal distortion rate of no-load air-gap magnetic density waveform are selected as objective functions to optimize. First, the range of values of each variable is determined by simulation experiments, and then orthogonal test is designed to obtain the sample space required for regression analysis. Then, two response surface models of objective functions based on support vector machine (Support Vector Machine,SVM) are established, respectively. The particle swarm optimization algorithm (Particle Swarm Optimization,PSO) with mutation operation is introduced to optimize the two objective functions, and the optimization results are added to the finite element software. The slotted torque is reduced from the initial 4.37N?m to 0.264 Nm, and the sinusoidal distortion rate of the no-load air-gap magnetic density waveform is reduced from 29.14% to 17.36. The simulation results verify the accuracy of the results. Therefore, the two objective functions are put into one optimization process, and the multi-objective optimization is carried out simultaneously by using PSO. The simulation results show that the slotting torque is 0.31 NM, and the sinusoidal distortion rate of no-load air gap is 22.33. Both of them are ideal. "SVM PSO" algorithm has good optimization effect. It can ensure higher accuracy; at the same time, the sample space is small, the evolution algebra is less, and the convergence speed is fast. The two algorithms combine to continue their advanced nature. In addition, SVM regression analysis adopts "black box method", which greatly reduces the dependence of related personnel on motor knowledge, and simplifies the process of motor design effectively. It also provides guidance and reference for the optimization of other performance parameters of motor.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM341
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，本文編號：2224408
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