本文選題：雙饋感應(yīng)風(fēng)力發(fā)電機 + 故障診斷　； 參考：《上海電機學(xué)院》2017年碩士論文

【摘要】：隨著風(fēng)力發(fā)電的強勁復(fù)蘇,截止2015年末,我國風(fēng)電裝機容量新增32.97GW,裝機容量又創(chuàng)歷史新高。雙饋感應(yīng)風(fēng)力發(fā)電機作為雙饋型風(fēng)電機組的關(guān)鍵設(shè)備,在運轉(zhuǎn)時其工作狀況決定整個風(fēng)電機組的效率。為保證雙饋感應(yīng)風(fēng)力發(fā)電機安全長久穩(wěn)定運轉(zhuǎn),對雙饋感應(yīng)風(fēng)力發(fā)電機故障診斷研究極其關(guān)鍵。本論文的主要研究內(nèi)容如下:(1)研究分析雙饋感應(yīng)風(fēng)力發(fā)電機的故障類型及對應(yīng)故障產(chǎn)生機理,選用故障率較高的定子故障和軸承故障作為研究對象。設(shè)計3.5KW雙饋感應(yīng)風(fēng)力發(fā)電機故障模擬實驗平臺,該實驗平臺能夠?qū)崿F(xiàn)定子繞組的匝間短路故障、相間短路故障、單相接地故障和軸承故障。根據(jù)定子繞組和軸承的故障特征表現(xiàn),確定分別以定子繞組電壓和軸承振動信號作為數(shù)據(jù)采集對象。(2)提出一種改進的小波閾值函數(shù)和閾值方法,改進后的閾值函數(shù)能夠?qū)胄盘枌崿F(xiàn)更好的去噪效果,保留更多有效信號,信噪比更高且均方根誤差更小相比于傳統(tǒng)小波閾值函數(shù)。以定子電壓和軸承振動信號作為實例分別進行去噪處理,對比分析去噪前后的信號,驗證了改進的小波閾值去噪方法的有效性。(3)對經(jīng)小波去噪后的定子電壓和軸承振動信號分別采用小波包進行頻帶分解,計算各個頻帶的能量值并歸一化處理組成故障診斷的特征向量,與沒有故障的頻帶能量信號對比分析,驗證了小波包提取故障特征的精確性。(4)提出一種改進的BP神經(jīng)網(wǎng)絡(luò)隱含層節(jié)點算法,改進的隱含層節(jié)點數(shù)算法能夠確定更優(yōu)的隱含層節(jié)點數(shù),使網(wǎng)絡(luò)的性能更優(yōu)。以定子繞組和軸承數(shù)據(jù)為樣本訓(xùn)練改進的BP神經(jīng)網(wǎng)絡(luò),并用測試數(shù)據(jù)對訓(xùn)練好的網(wǎng)絡(luò)進行檢測。實驗結(jié)果表明,改進的BP神經(jīng)網(wǎng)絡(luò)對發(fā)電機定子繞組和軸承故障類型識別準(zhǔn)確率較高。(5)利用虛擬儀器LabVIEW設(shè)計了雙饋感應(yīng)風(fēng)力發(fā)電機的故障診斷系統(tǒng),包括用戶登入、數(shù)據(jù)采集、數(shù)據(jù)顯示、數(shù)據(jù)分析和故障診斷模塊,然后通過LabVIEW的Web服務(wù)器功能,實現(xiàn)故障診斷系統(tǒng)與互聯(lián)網(wǎng)結(jié)合,增強了故障診斷系統(tǒng)的共享性和交互性。
[Abstract]:With the strong recovery of wind power generation, by the end of 2015, the installed capacity of wind power in China has been increased by 32.97 GW, and the installed capacity has reached a new record. As the key equipment of the doubly-fed wind turbine, the efficiency of the wind turbine is determined by the working condition of the doubly-fed induction wind turbine. In order to ensure the safe and stable operation of doubly-fed induction wind turbine, it is very important to study the fault diagnosis of doubly-fed induction wind turbine. The main contents of this thesis are as follows: (1) the fault types and corresponding fault generation mechanism of doubly-fed induction wind turbine are analyzed, and the stator faults and bearing faults with high failure rate are selected as the research objects. The fault simulation experiment platform of 3.5KW doubly-fed induction wind turbine is designed. The platform can realize inter-turn short-circuit fault, inter-phase short-circuit fault, single-phase grounding fault and bearing fault of stator winding. According to the fault characteristics of stator winding and bearing, this paper proposes an improved wavelet threshold function and threshold method, taking stator winding voltage and bearing vibration signal as data acquisition objects, respectively. The improved threshold function can achieve better denoising effect, retain more effective signals, have higher signal-to-noise ratio and lower root mean square error than the traditional wavelet threshold function. Taking stator voltage and bearing vibration signal as examples, the signal before and after de-noising is compared and analyzed. The validity of the improved wavelet threshold denoising method is verified. The wavelet packet is used to decompose the stator voltage and bearing vibration signal after wavelet denoising. The eigenvector of fault diagnosis is formed by calculating the energy value of each frequency band and normalizing processing, and compared with the energy signal of frequency band without fault. This paper verifies the accuracy of wavelet packet extraction of fault features and proposes an improved BP neural network hidden layer node algorithm. The improved hidden layer node number algorithm can determine the number of hidden layer nodes and make the network performance better. The improved BP neural network is trained with stator winding and bearing data as samples, and the trained neural network is detected by test data. The experimental results show that the improved BP neural network has high accuracy in identifying the stator windings and bearing fault types of generators. The fault diagnosis system of doubly-fed induction wind turbines is designed by using virtual instrument LabVIEW, including user login, data acquisition, etc. Data display, data analysis and fault diagnosis module, and then through the Web server function of LabVIEW, the fault diagnosis system is combined with the Internet, which enhances the sharing and interaction of the fault diagnosis system.
【學(xué)位授予單位】：上海電機學(xué)院
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TM315

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