本文選題：六相永磁同步電機　切入點：故障診斷　出處：《沈陽工業(yè)大學》2017年碩士論文　論文類型：學位論文

【摘要】：由于六相永磁同步電機具有低壓大功率、轉(zhuǎn)矩脈動小、系統(tǒng)可靠性高等優(yōu)點,因此被廣泛應(yīng)用于全電力艦船推動系統(tǒng)、純電動以及混合動力電動車輛牽引系統(tǒng)等高功率等級、高可靠性的場合。六相永磁同步電機控制系統(tǒng)一般由電機、控制器和傳感器等組成,其中控制器是容易出現(xiàn)故障的環(huán)節(jié),其可靠性對整個系統(tǒng)的正常運行非常重要。因此,對六相永磁同步電機控制器進行早期故障診斷就顯得非常重要。本文給出了基于小波包分析、流形學習法和支持向量機相結(jié)合的“特征提取→維數(shù)約簡→模式識別”的六相永磁同步電機控制器故障診斷方法。首先,建立六相永磁同步電機數(shù)學模型,并仿真實現(xiàn)其矢量控制。介紹六相永磁同步電機的結(jié)構(gòu)及其工作原理,推導出自然坐標系下和兩相旋轉(zhuǎn)坐標系下的數(shù)學模型,并且在Simulink中仿真實現(xiàn)六相永磁同步電機數(shù)學模型及其矢量控制。其次,采用小波包分析提取電機六相定子電流中的故障特征向量。詳細地介紹小波分析理論和小波包分析理論。采樣不同IGBT故障狀態(tài)下的六相定子電流,采用小波包分析對電流進行六層小波包分解,計算各個節(jié)點的能量值,并將該能量值歸一化處理后作為故障特征向量。再次,通過流形學習方法對六相永磁同步電機控制器故障特征向量進行維數(shù)約簡。詳細的介紹流形學習的相關(guān)理論及其幾種經(jīng)典算法,并采用流形學習法中局部切空間排列算法對小波包分析提取的特征向量進行降維處理。最后,利用最小二乘支持向量機對六相永磁同步電機控制器IGBT故障進行識別。介紹支持向量機理論,以及在支持向量機理論上進一步發(fā)展的最小二乘支持向量機理論。局部切空間算法約簡后的特征向量分別作為最小二乘支持向量機的訓練樣本和測試樣本,通過仿真實驗對不同的IGBT開路故障進行驗證,仿真驗證表明,這種故障診斷的方法在理論上可以實現(xiàn)對不同IGBT開路故障的準確定位。
[Abstract]:Due to its advantages of low voltage and high power, low torque ripple and high reliability, six-phase permanent magnet synchronous motor is widely used in high power class, such as full electric ship propulsion system, pure electric and hybrid electric vehicle traction system, etc. The control system of six-phase permanent magnet synchronous motor is generally composed of motor, controller and sensor, among which the controller is prone to failure, and its reliability is very important for the normal operation of the whole system. It is very important to diagnose the fault of six-phase PMSM controller in the early stage. In this paper, the feature extraction based on wavelet packet analysis, manifold learning and support vector machine is presented. 鈫扗imension reduction. 鈫扵he fault diagnosis method of six-phase permanent magnet synchronous motor controller based on pattern recognition is introduced. Firstly, the mathematical model of six-phase permanent magnet synchronous motor is established and its vector control is simulated. The structure and working principle of six-phase permanent magnet synchronous motor are introduced. The mathematical models in natural coordinate system and two-phase rotating coordinate system are derived, and the mathematical model and vector control of six-phase permanent magnet synchronous motor are simulated in Simulink. The wavelet packet analysis is used to extract the fault eigenvector from the six-phase stator current of the motor. The wavelet analysis theory and wavelet packet analysis theory are introduced in detail. The six-phase stator current in different IGBT fault states is sampled. The wavelet packet analysis is used to decompose the current into six layers of wavelet packet, calculate the energy value of each node, and normalize the energy value as the fault eigenvector. Through manifold learning method, dimension reduction of fault eigenvector of six-phase PMSM controller is carried out. The related theory of manifold learning and several classical algorithms are introduced in detail. And the local tangent space arrangement algorithm in manifold learning method is used to reduce the dimension of the feature vector extracted by wavelet packet analysis. Finally, Using least square support vector machine (LS-SVM) to identify the IGBT fault of six-phase PMSM controller, the theory of support vector machine (SVM) is introduced. And the least squares support vector machine (LS-SVM) theory, which is further developed in support vector machine (SVM) theory, is used as the training sample and test sample of LS-SVM, respectively, which is reduced by local tangent space algorithm. Different open circuit faults of IGBT are verified by simulation experiments. The simulation results show that the method of fault diagnosis can accurately locate the open circuit faults of different IGBT in theory.
【學位授予單位】：沈陽工業(yè)大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM341;TP18

【參考文獻】

相關(guān)期刊論文 前10條

1 馮靈清;劉艷紅;劉宇晶;;流形學習及其算法分析[J];計算機時代;2017年04期

2 楊欣榮;蔣林;王婧林;王蕾;;基于小波變換的無刷直流電機逆變器故障診斷[J];電測與儀表;2017年05期

3 趙朝賀;;一種改進的支持向量機參數(shù)優(yōu)化方法[J];地理空間信息;2017年01期

4 劉青;朱炳安;;基于小波分析對變速箱的故障診斷[J];機械設(shè)計與制造;2016年11期

5 許金基;張建宇;高立新;;小波分析在故障診斷中的應(yīng)用和發(fā)展[J];設(shè)備管理與維修;2016年08期

6 周長攀;蘇健勇;楊貴杰;楊金波;;基于雙零序電壓注入PWM策略的雙三相永磁同步電機矢量控制[J];中國電機工程學報;2015年10期

7 王志遠;張杰;王雨琦;宋文勝;葛興來;;基于三相電流檢測的逆變器開路故障診斷及容錯方案研究[J];機車電傳動;2014年02期

8 萬鵬;王紅軍;徐小力;;局部切空間排列和支持向量機的故障診斷模型[J];儀器儀表學報;2012年12期

9 王占霞;張曉波;;基于SOM網(wǎng)的風電變流器故障診斷[J];電網(wǎng)與清潔能源;2011年04期

10 鄭蕊蕊;趙繼印;趙婷婷;李敏;;基于遺傳支持向量機和灰色人工免疫算法的電力變壓器故障診斷[J];中國電機工程學報;2011年07期

，

本文編號：1623995