【摘要】：永磁同步電動機(PMSM)具有很多優(yōu)點,其中它具有體積小、噪聲低、效率高、可靠性高、功率密度大等特點被廣泛運用于性能較高的場合,因此永磁同步電機越來越引起人們的關(guān)注和重視。而作為一種新型的交流電動機控制技術(shù),直接轉(zhuǎn)矩控制技術(shù)具有很多優(yōu)點,如控制方法簡單直接、系統(tǒng)具有強魯棒性以及動態(tài)響應(yīng)快。因兩者優(yōu)秀的特征,將這兩種技術(shù)結(jié)合已成為現(xiàn)代交流傳動領(lǐng)域重要的熱點話題。本文分析了永磁同步電機的數(shù)學(xué)模型和直接轉(zhuǎn)矩控制策略,并分析了傳統(tǒng)DTC控制系統(tǒng)本身采用電壓積分對定子磁鏈進行估算的缺點,使定子磁鏈值不能得到準確計算;此外,通常對控制系統(tǒng)需要安裝機械傳感器來測量電機的速度和位置信號,但是在某些惡劣的環(huán)境下安裝機械傳感器,由于環(huán)境的因素機械傳感器自身會存在測量不準確、精度不高等帶來的誤差都會影響控制系統(tǒng)的性能。為了解決上述問題提出了無傳感器技術(shù),本文設(shè)計了基于兩相靜止(α-β)坐標系下EKF的PMSM_DTC控制系統(tǒng)仿真模型,算法運用估計誤差均方差最小的原則方法,進而避免傳統(tǒng)DTC控制方法存在不足和缺陷,仿真結(jié)果表明,定子磁鏈脈動和轉(zhuǎn)矩脈動有明顯程度上的降低。另外,基于EKF觀測器在進行轉(zhuǎn)速的估計上具有精準性和有效性,并實現(xiàn)了無傳感器運行。同時本文對永磁同步電機提出了改進粒子群算法優(yōu)化擴展卡爾曼濾波(EKF)器噪聲矩陣的方法來實現(xiàn)永磁同步電機(PMSM)無傳感器控制,克服了以往關(guān)于擴展卡爾曼濾波器狀態(tài)估計中最優(yōu)噪聲矩陣難以選取的問題。通過將遺傳算法(GA)和粒子群算法(PSO)結(jié)合起來并繼承它們各自的優(yōu)點。結(jié)合改進后的粒子群算法來優(yōu)化擴展卡爾曼濾波器中的噪聲矩陣,然后應(yīng)用于PMSM無傳感器直接轉(zhuǎn)矩控制系統(tǒng)中。本文在Matlab/Simulink平臺搭建系統(tǒng)仿真,將改進的粒子群與簡便的試湊法、遺傳算法和粒子群算法比較來看,其能夠更好的改善擴展卡爾曼濾波器的濾波特性以及抗噪聲能力,從而對感應(yīng)電機無傳感器直接轉(zhuǎn)矩控制(DTC)系統(tǒng)控制性能有明顯的提高。
[Abstract]:Permanent magnet synchronous motor (PMSM) has many advantages, such as small size, low noise, high efficiency, high reliability and high power density. Therefore, permanent magnet synchronous motor (PMSM) has attracted more and more attention. As a new type of AC motor control technology, direct torque control technology has many advantages, such as simple and direct control method, strong robustness and fast dynamic response of the system. Because of their excellent characteristics, the combination of these two technologies has become an important hot topic in the field of modern AC transmission. In this paper, the mathematical model and direct torque control strategy of permanent magnet synchronous motor are analyzed, and the shortcoming of traditional DTC control system to estimate stator flux by voltage integral is analyzed, which makes the stator flux value can not be calculated accurately. Mechanical sensors are usually installed to measure the speed and position signals of the motor. However, in some harsh environments, the mechanical sensors themselves will be inaccurate because of the environmental factors. The error caused by low precision will affect the performance of the control system. In order to solve the above problem, this paper designs a simulation model of PMSM DTC control system based on EKF in two-phase stationary (偽-尾) coordinate system. The algorithm uses the principle method of minimum mean square error of estimation error. The simulation results show that the stator flux ripple and torque ripple are obviously reduced. In addition, the EKF observer is accurate and effective in speed estimation, and realizes sensorless operation. At the same time, an improved particle swarm optimization algorithm is proposed to optimize the extended Kalman filter (EKF) noise matrix to realize sensorless control of permanent magnet synchronous motor (PMSM). It overcomes the problem that the optimal noise matrix is difficult to select in the state estimation of extended Kalman filter. Genetic algorithm (GA) and particle swarm optimization (PSO) are combined and their respective advantages are inherited. The improved particle swarm optimization algorithm is used to optimize the noise matrix in extended Kalman filter and then applied to PMSM sensorless direct torque control system. In this paper, the system simulation is built on Matlab / Simulink platform. Compared with the improved particle swarm optimization, genetic algorithm and particle swarm optimization algorithm, the improved particle swarm optimization algorithm can better improve the filtering characteristics and anti-noise ability of the extended Kalman filter. Thus, the control performance of sensorless direct torque control (DTC) system of induction motor is improved obviously.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM341;TP273

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