【摘要】：永磁同步電機(jī)結(jié)構(gòu)簡單,效率高,性能優(yōu)越,并且具有良好的穩(wěn)定性,也正因?yàn)槿绱?永磁同步電機(jī)廣泛應(yīng)用于風(fēng)力發(fā)電、機(jī)器人、工業(yè)生產(chǎn)等各個(gè)領(lǐng)域。永磁同步電機(jī)因其內(nèi)部轉(zhuǎn)子通過嵌入一塊高性能的永磁體產(chǎn)生固定的磁場(chǎng),省略了勵(lì)磁繞組,降低了電機(jī)的無功功率,從而提高了電機(jī)的功率因數(shù),增加了電機(jī)的效率。同時(shí)簡單的結(jié)構(gòu)使得電機(jī)內(nèi)部不易損壞,提高了電機(jī)的穩(wěn)定性,降低了后期維修的成本。合理的控制電機(jī)的運(yùn)行,充分發(fā)揮電機(jī)的性能,不僅可以增加產(chǎn)能,同時(shí)還可以節(jié)約能源。隨著技術(shù)的進(jìn)步,已經(jīng)發(fā)展出多種控制方法,但是無論是哪一種控制方法,控制器的設(shè)計(jì)都離不開精確的電機(jī)參數(shù)。電機(jī)參數(shù)的獲得方法大致分為離線參數(shù)測(cè)量與在線參數(shù)辨識(shí)兩類。其中通過離線參數(shù)測(cè)量方法例如電機(jī)堵轉(zhuǎn)實(shí)驗(yàn),激勵(lì)響應(yīng)等方法所獲得的電機(jī)參數(shù)都是電機(jī)處于靜止?fàn)顟B(tài)下電機(jī)參數(shù),無法體現(xiàn)電機(jī)運(yùn)行過程中的真實(shí)電機(jī)參數(shù)。針對(duì)這一缺點(diǎn),在線參數(shù)辨識(shí)則具有一定的優(yōu)勢(shì),具體在于辨識(shí)結(jié)果可以跟蹤電機(jī)參數(shù)的變化,進(jìn)而控制方法中控制器參數(shù)可以隨著電機(jī)參數(shù)的變化實(shí)時(shí)調(diào)整,提高電機(jī)控制方法的精確性,提高了電機(jī)的運(yùn)行性能。眾多的參數(shù)辨識(shí)算法由于其側(cè)重點(diǎn)的不同,各自具有不同的優(yōu)缺點(diǎn),可以用收斂速度與收斂精度來表示算法的性能。多新息算法的優(yōu)勢(shì)在于可以通過改變新息長度的大小改變每次計(jì)算所需要的數(shù)據(jù)量的多少,從而增加算法對(duì)數(shù)據(jù)的利用效率。針對(duì)某些算法辨識(shí)過程中數(shù)據(jù)的利用率較低導(dǎo)致算法的性能不太理想的情況,將多新息算法對(duì)原算法進(jìn)行改進(jìn),在保留算法原有優(yōu)勢(shì)的基礎(chǔ)上增加數(shù)據(jù)的利用效率,提高算法的性能,并通過引入遺忘因子的概念,對(duì)改進(jìn)后的算法進(jìn)一步優(yōu)化。本文首先介紹了永磁同步電機(jī)的數(shù)學(xué)模型以及辨識(shí)模型,在此基礎(chǔ)上,運(yùn)用已經(jīng)非常成熟的矢量控制方法搭建永磁同步電機(jī)的矢量控制模型,通過采集電機(jī)運(yùn)行時(shí)的參數(shù),采用隨機(jī)梯度類、最小一乘類、正交投影類等算法辨識(shí)電機(jī)參數(shù),并根據(jù)算法的優(yōu)缺點(diǎn)用多新息算法與遺忘因子進(jìn)行優(yōu)化,通過辨識(shí)結(jié)果的對(duì)比驗(yàn)證算法的有效性。
[Abstract]:Permanent magnet synchronous motor (PMSM) has a simple structure, high efficiency, superior performance and good stability. Because of this, PMSM is widely used in wind power generation, robot, industrial production and other fields. The permanent magnet synchronous motor (PMSM) produces a fixed magnetic field by embedding a high performance permanent magnet into its inner rotor, which omits the excitation winding, reduces the reactive power of the motor, improves the power factor of the motor and increases the efficiency of the motor. At the same time, the simple structure makes the motor not easy to damage, improve the stability of the motor, and reduce the cost of later maintenance. It can not only increase the production capacity but also save energy by controlling the motor operation reasonably and giving full play to the performance of the motor. With the development of technology, a variety of control methods have been developed, but no matter which control method, the controller design can not be separated from accurate motor parameters. The methods of obtaining motor parameters can be divided into two types: offline parameter measurement and on-line parameter identification. The parameters obtained by off-line parameter measurement methods such as motor shutoff experiment and excitation response are all motor parameters in static state, which can not reflect the real motor parameters in the process of motor operation. In order to overcome this shortcoming, on-line parameter identification has some advantages, which is that the identification result can track the change of motor parameters, and then the controller parameters can be adjusted in real time with the change of motor parameters. The accuracy of the motor control method is improved, and the performance of the motor is improved. Many parameter identification algorithms have different advantages and disadvantages due to their different emphases. The convergence rate and precision can be used to express the performance of the algorithm. The advantage of multi-innovation algorithm is that it can increase the efficiency of data utilization by changing the size of innovation length and changing the amount of data needed for each calculation. In view of the fact that the performance of some algorithms is not satisfactory due to the low utilization of data in the process of identification, this paper improves the original algorithm to increase the efficiency of data utilization on the basis of preserving the original advantages of the algorithm. Firstly, the mathematical model and identification model of PMSM are introduced in this paper. On this basis, the vector control model of PMSM is built by using the very mature vector control method, and the parameters of the PMSM are collected when the PMSM is running. The motor parameters are identified by random gradient class, least one multiplication class and orthogonal projection class. The algorithm is optimized by multi-innovation algorithm and forgetting factor according to the advantages and disadvantages of the algorithm. The validity of the algorithm is verified by comparing the identification results.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM341

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 呂小意;黃松;王艷;紀(jì)志成;;基于蒼狼算法的感應(yīng)電機(jī)改進(jìn)模型參數(shù)辨識(shí)[J];系統(tǒng)仿真學(xué)報(bào);2016年12期

2 張建宇;吳定會(huì);;基于遺忘因子多新息隨機(jī)梯度算法PMSM參數(shù)辨識(shí)[J];微特電機(jī);2016年11期

3 荀倩;王培良;李祖欣;蔡志端;秦海鴻;;基于遞推最小二乘法的永磁伺服系統(tǒng)參數(shù)辨識(shí)[J];電工技術(shù)學(xué)報(bào);2016年17期

4 陳愛國;王士同;;基于多代表點(diǎn)的大規(guī)模數(shù)據(jù)模糊聚類算法[J];控制與決策;2016年12期

5 時(shí)振偉;紀(jì)志成;王艷;;多元系統(tǒng)耦合帶遺忘因子有限數(shù)據(jù)窗遞推最小二乘辨識(shí)方法[J];控制與決策;2016年10期

6 王飛宇;田井呈;卓克瓊;趙朝會(huì);;基于改進(jìn)模型參考自適應(yīng)算法的永磁同步電機(jī)轉(zhuǎn)動(dòng)慣量辨識(shí)[J];電機(jī)與控制應(yīng)用;2016年08期

7 全亞威;田娜;紀(jì)志成;王艷;;基于珊瑚礁算法的永磁同步電機(jī)參數(shù)辨識(shí)[J];系統(tǒng)仿真學(xué)報(bào);2016年04期

8 孔飛;吳定會(huì);;一種改進(jìn)的雞群算法[J];江南大學(xué)學(xué)報(bào)(自然科學(xué)版);2015年06期

9 沈蛟驍;余海濤;王亞魯;徐鳴飛;陳輝;;標(biāo)準(zhǔn)粒子群算法在永磁同步電機(jī)參數(shù)辨識(shí)中的應(yīng)用研究[J];微電機(jī);2015年12期

10 謝穎;李吉興;楊忠學(xué);張巖;;改進(jìn)遺傳蟻群算法及其在電機(jī)結(jié)構(gòu)優(yōu)化中的研究[J];電機(jī)與控制學(xué)報(bào);2015年10期

相關(guān)博士學(xué)位論文 前2條

1 李冉;永磁同步電機(jī)無位置傳感器運(yùn)行控制技術(shù)研究[D];浙江大學(xué);2012年

2 寇曉麗;群智能算法及其應(yīng)用研究[D];西安電子科技大學(xué);2009年

相關(guān)碩士學(xué)位論文 前3條

1 龐榮;深度神經(jīng)網(wǎng)絡(luò)算法研究及應(yīng)用[D];西南交通大學(xué);2016年

2 常凱;基于模型參考自適應(yīng)的永磁同步電機(jī)無速度傳感器控制系統(tǒng)的設(shè)計(jì)[D];東北大學(xué);2009年

3 趙鵬軍;優(yōu)化問題的幾種智能算法[D];西安電子科技大學(xué);2009年

，

本文編號(hào)：2159193