發(fā)布時間：2018-06-21 05:08

  本文選題：永磁同步電機(jī) + 參數(shù)辨識　； 參考：《江南大學(xué)》2016年碩士論文

【摘要】：由于永磁同步電機(jī)(Permanent Magnet Synchronous Motor,PMSM)具有結(jié)構(gòu)簡單,體積小,重量輕,損耗小,效率高等特點(diǎn);并且國內(nèi)的稀土永磁材料儲存豐富,所以能夠得到快速的推廣和應(yīng)用于高性能驅(qū)動系統(tǒng)及其他工業(yè)領(lǐng)域。在實(shí)際的運(yùn)行過程中,永磁同步電機(jī)系統(tǒng)是強(qiáng)耦合,非線性,時變的動態(tài)系統(tǒng),并且PMSM系統(tǒng)參數(shù)容易受到溫度、磁通飽和、定子電流等因素的影響,這些影響不僅降低了運(yùn)行的可靠性也提高了控制系統(tǒng)的難度。而高性能的PMSM控制系統(tǒng)的實(shí)現(xiàn)依賴于精確的電機(jī)參數(shù)。所以,對電機(jī)的參數(shù)進(jìn)行準(zhǔn)確且實(shí)時的辨識是提高PMSM控制系統(tǒng)的前提。本文首先介紹了永磁同步電機(jī)的基本結(jié)構(gòu)以及目前的參數(shù)辨識技術(shù),由基本的永磁同步電機(jī)的三相靜止坐標(biāo)下的數(shù)學(xué)模型,根據(jù)矢量變換的原理,將其變換到兩相旋轉(zhuǎn)d-q軸坐標(biāo)系下的數(shù)學(xué)模型。介紹了基本的矢量控制原理,并綜合比較幾種常見的解耦控制方法,說明了使用0di?的控制方法的原因,同時介紹了空間矢量脈寬調(diào)制技術(shù)的原理及實(shí)現(xiàn)。針對傳統(tǒng)的粒子群算法以及最小二乘法在處理電機(jī)多參數(shù)離線辨識問題時具有速度慢,誤差高的問題,提出了將珊瑚礁算法應(yīng)用于PMSM的多參數(shù)離線辨識中。將辨識的結(jié)果與使用其他傳統(tǒng)方法辨識的PMSM參數(shù)從辨識的速度以及精度兩個方面進(jìn)行對比,驗(yàn)證了珊瑚礁算法的有效性。同時針對基本珊瑚礁算法在辨識電機(jī)離線參數(shù)時可能會陷入局部最優(yōu)的問題,提出了將高斯與柯西變異引入珊瑚礁算法的運(yùn)行過程中,將改進(jìn)珊瑚礁算法與基本珊瑚礁算法同時應(yīng)用于PMSM的多參數(shù)離線辨識中,對比得出改進(jìn)算法在收斂速度以及精確度方面的優(yōu)勢。針對遺忘因子遞推最小二乘辨識對電機(jī)參數(shù)進(jìn)行在線辨識時受到遺忘因子大小影響、辨識結(jié)果不穩(wěn)定的問題,提出了將多新息算法與遺忘因子遞推最小二乘算法相結(jié)合解決,由于新息長度的選擇要同時考慮算法的收斂速度以及算法辨識的精確度,所以通過實(shí)驗(yàn)選取合適的新息長度。通過參數(shù)恒定與參數(shù)階躍變換兩種情況下的仿真實(shí)驗(yàn)可以得出多新息遺忘因子遞推最小二乘法在對PMSM參數(shù)在線辨識時具有更好的穩(wěn)定性、收斂性。尤其是參數(shù)變化時的跟蹤性能良好。
[Abstract]:Permanent Magnet synchronous Motor (PMSM) has the advantages of simple structure, small size, light weight, low loss and high efficiency. Therefore, it can be quickly popularized and applied to high performance drive systems and other industrial fields. The PMSM system is a strongly coupled, nonlinear and time-varying dynamic system in the actual operation process, and the PMSM system parameters are easily affected by temperature, flux saturation, stator current and other factors. These effects not only reduce the reliability of operation, but also improve the difficulty of control system. The realization of high performance PMSM control system depends on precise motor parameters. Therefore, accurate and real-time identification of motor parameters is the prerequisite to improve PMSM control system. This paper first introduces the basic structure of permanent magnet synchronous motor and the current parameter identification technology. According to the principle of vector transformation, the basic mathematical model of permanent magnet synchronous motor under three-phase static coordinates is introduced. It is transformed to the mathematical model of two phase rotating d-q axis coordinate system. The basic principle of vector control is introduced, and several common decoupling control methods are compared synthetically. At the same time, the principle and realization of space vector pulse width modulation are introduced. Aiming at the slow speed and high error of traditional particle swarm optimization (PSO) and least square method (LSM) in the process of multi-parameter off-line identification of motor, a new method of coral reef algorithm is proposed for multi-parameter off-line identification of PMSM. The validity of the coral reef algorithm is verified by comparing the identification results with the PMSM parameters identified by other traditional methods in terms of the speed and accuracy of the identification. At the same time, aiming at the problem that the basic coral reef algorithm may fall into local optimum when identifying the off-line parameters of the motor, it is proposed to introduce Gao Si and Cauchy mutation into the running process of the coral reef algorithm. The improved coral reef algorithm and the basic coral reef algorithm are applied to the multi-parameter off-line identification of PMSM at the same time. The advantages of the improved algorithm in convergence speed and accuracy are compared. In order to solve the problem that the forgetting factor is affected by the magnitude of the forgetting factor and the result of the identification is unstable when the forgetting factor recursive least square identification is carried out on line, a new algorithm is proposed to solve the problem, which combines the multiple innovation algorithm with the forgetting factor recursive least square algorithm. Because the convergence speed of the algorithm and the accuracy of algorithm identification must be taken into account in the selection of innovation length, the appropriate innovation length is selected through experiments. Through the simulation experiments under the condition of constant parameters and step transformation of parameters, it can be concluded that the recursive least square method of multi-innovation forgetting factor has better stability and convergence in on-line identification of PMSM parameters. Especially, the tracking performance is good when the parameters change.
【學(xué)位授予單位】：江南大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2016
【分類號】：TM341
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本文編號：2047367