【摘要】：葉片是風(fēng)力發(fā)電機(jī)的關(guān)鍵部件之一，對(duì)葉片損傷的研究越來越受到該領(lǐng)域研究人員的關(guān)注。由于葉片的結(jié)構(gòu)巨大、形狀不規(guī)則、材料鋪層復(fù)雜并且長期工作在惡劣的環(huán)境下，所以當(dāng)前需要解決的難題是如何實(shí)現(xiàn)葉片的健康監(jiān)測。目前常用的監(jiān)測手段是通過監(jiān)測其模態(tài)來判斷葉片的損傷狀況，但該方法的缺點(diǎn)是敏感度低，而且一直未能得到有效地解決。針對(duì)這一問題，本文提出利用聲發(fā)射技術(shù)對(duì)風(fēng)電葉片損傷狀況進(jìn)行檢測，并應(yīng)用SVM(Support Vector Machine,支持向量機(jī))對(duì)葉片的兩類損傷模式進(jìn)行識(shí)別。 由于葉片在受到外力破壞時(shí)會(huì)引起材料內(nèi)部應(yīng)變從而產(chǎn)生聲發(fā)射信號(hào)，通過對(duì)聲發(fā)射信號(hào)進(jìn)行采集和分析，能夠?qū)崿F(xiàn)對(duì)聲發(fā)射信號(hào)源的識(shí)別。首先接通聲發(fā)射傳感器、信號(hào)放大器、數(shù)據(jù)采集卡和計(jì)算機(jī)等設(shè)備，搭建聲發(fā)射信號(hào)采集實(shí)驗(yàn)平臺(tái)，用耦合劑將聲發(fā)射傳感器固定在葉片上。然后人工對(duì)靜態(tài)的單個(gè)葉片進(jìn)行加載，模擬葉片的裂紋擴(kuò)展和邊緣破損兩類損傷，并采集損傷時(shí)的聲發(fā)射信號(hào)。 采集到信號(hào)后，分別利用諧波小波包和db10小波包對(duì)聲發(fā)射信號(hào)進(jìn)行4層分解并計(jì)算信號(hào)的各頻段能量值，，將所得能量值進(jìn)行歸一化處理后，所得數(shù)據(jù)作為特征向量，采用SVM對(duì)特征向量進(jìn)行訓(xùn)練學(xué)習(xí)，建立葉片損傷識(shí)別模型。在進(jìn)行葉片的損傷識(shí)別時(shí)，對(duì)兩種小波包的特征提取效果進(jìn)行了比較，仿真結(jié)果表明，采用諧波小波包和SVM結(jié)合的方法可以獲得良好的識(shí)別效果。該方法能夠有效地識(shí)別不同類型的損傷，有助于發(fā)現(xiàn)葉片初期損傷，使葉片可以得到及時(shí)地維護(hù)，防止損傷的進(jìn)一步擴(kuò)展。
[Abstract]:Blade is one of the key components of wind turbine. Because of the huge structure, irregular shape, complicated material layer and long term working environment, the problem that needs to be solved is how to realize the blade health monitoring. At present, the commonly used monitoring method is to judge the damage condition of the blade by monitoring its mode, but the disadvantage of this method is that the sensitivity is low, and it has not been effectively solved. In order to solve this problem, the acoustic emission technique is used to detect the damage of wind turbine blades, and SVM (Support Vector Machine, support vector machine (SVM) is applied to identify the two types of damage patterns. The acoustic emission signal can be obtained by collecting and analyzing the acoustic emission signal because the blade will cause internal strain of the material when it is damaged by external force, and the acoustic emission signal source can be recognized. First, the acoustic emission sensor, signal amplifier, data acquisition card and computer are connected to build the experimental platform of acoustic emission signal acquisition, and the acoustic emission sensor is fixed on the blade with coupling agent. Then, the static single blade is loaded manually to simulate the crack propagation and edge damage of the blade, and the acoustic emission signals are collected. After collecting the signal, the harmonic wavelet packet and the db10 wavelet packet are used to decompose the acoustic emission signal into four layers and calculate the energy values of each frequency band of the signal. After normalizing the energy value, the obtained data is used as the eigenvector. The feature vector is trained and studied by SVM, and the model of blade damage identification is established. In the process of blade damage identification, the feature extraction effects of two kinds of wavelet packets are compared. The simulation results show that the method of harmonic wavelet packet and SVM can obtain good recognition effect. This method can effectively identify different types of damage, help to detect the initial damage of leaves, enable the leaves to be maintained in time, and prevent the damage from spreading further.
【學(xué)位授予單位】：蘭州交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM315

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