本文選題：狀態(tài)監(jiān)測 + 無線網(wǎng)絡(luò)��； 參考：《華南理工大學(xué)》2014年碩士論文

【摘要】：風能作為一種清潔、綠色的能源在注重低碳和環(huán)保的當今社會得到越來越多的發(fā)展和重視，風力發(fā)電的經(jīng)濟效益是風力發(fā)電企業(yè)主要考慮的指標和因素，在風電場的運行和維護過程中，風機日常維護和故障維修費用占發(fā)電企業(yè)支出的比例越來越高。風電機組是由多種機械和電氣設(shè)備構(gòu)成的復(fù)雜整體，一旦某個部位發(fā)生故障將直接影響整機的運行，目前國內(nèi)風電場主要采用定期維護和事后檢修的方法，由于維護部件的不確定性往往造成人力和物力的較大浪費，而狀態(tài)監(jiān)測和故障診斷系統(tǒng)在新型風機上有所應(yīng)用，但對于生產(chǎn)時間較早且已進入故障高發(fā)期的風電機組來說無法通用。 本文設(shè)計了一套集數(shù)據(jù)采集、無線傳輸和診斷預(yù)測為一體的系統(tǒng)，對風力機易發(fā)故障部位進行狀態(tài)監(jiān)測和故障診斷，提供實時可視的工作狀態(tài)數(shù)據(jù)，對已經(jīng)發(fā)生的故障能迅速定位故障部位，并能預(yù)測未來一段時間內(nèi)部件工作狀態(tài)的趨勢。系統(tǒng)通過壓電式加速度傳感器和霍爾電流傳感器，采集發(fā)電機和齒輪箱在工作狀態(tài)時的振動信號和電流信號，通過電荷放大器與采集卡進入工控機，在工控機上進行數(shù)據(jù)庫的讀取和存儲，利用多天線技術(shù)、提高發(fā)射和接收增益等方法建立覆蓋范圍較大的Wi-Fi網(wǎng)絡(luò)，，實現(xiàn)風力機與主控端之間的無線通信，采集到的數(shù)據(jù)傳輸至主控終端后進行分析和診斷，利用小波包分析和傅里葉變換相結(jié)合對信號處理，提取信號中隱藏的故障特征向量，最后通過BP神經(jīng)網(wǎng)絡(luò)對獲取的特征向量進行分析，獲取部件的故障部位和故障程度，同時利用神經(jīng)網(wǎng)絡(luò)對部件未來的工作狀態(tài)進行預(yù)測，為風電場運行維護人員提供指導(dǎo)性的維修意見，提高維修維護的針對性和實效性。 本套系統(tǒng)在汕尾市紅海灣風電場進行現(xiàn)場安裝和試驗，結(jié)果表明該系統(tǒng)能有效的對風電機組當前的工作狀態(tài)進行監(jiān)測，能準確診斷出發(fā)電機和齒輪箱的常見故障。
[Abstract]:As a kind of clean and green energy, wind energy is developing and paying more and more attention to the low-carbon and environment-friendly society. The economic benefit of wind power generation is the main index and factor to be considered by wind power enterprises.In the process of operation and maintenance of wind farm, the expenses of fan daily maintenance and fault maintenance account for more and more expenses of power generation enterprises.Wind turbine is a complex whole composed of a variety of mechanical and electrical equipment. Once a fault occurs in a certain part, it will directly affect the operation of the whole machine. At present, the domestic wind farm mainly adopts the methods of regular maintenance and after-repair.Because the uncertainty of maintenance components often leads to a great waste of manpower and material resources, the condition monitoring and fault diagnosis system is applied to the new fan.But for the wind turbine unit with earlier production time and has entered the period of high failure, it is impossible to generalize.In this paper, a system which integrates data acquisition, wireless transmission and diagnosis and prediction is designed to monitor and diagnose the status of wind turbine fault prone parts, and to provide real-time and visual working state data.The fault location can be quickly located and the working state of the components can be predicted for some time to come.The system collects vibration and current signals of generator and gearbox by piezoelectric accelerometer and Hall current sensor, and enters industrial control computer through charge amplifier and data acquisition card.In order to realize wireless communication between wind turbine and main control terminal, the database is read and stored on industrial control computer, and multi-antenna technology is used to improve the transmitting and receiving gain to set up a Wi-Fi network with a large coverage range, so as to realize the wireless communication between the wind turbine and the main control terminal.After the collected data is transmitted to the main control terminal for analysis and diagnosis, wavelet packet analysis and Fourier transform are used to process the signal and extract the hidden fault feature vector of the signal.Finally, the feature vectors obtained are analyzed by BP neural network, and the fault location and fault degree of components are obtained. At the same time, the neural network is used to predict the working state of components in the future.To provide guidance for wind farm maintenance personnel, improve the pertinence and effectiveness of maintenance.The system has been installed and tested in Hongwan wind farm in Shanwei City. The results show that the system can effectively monitor the current working state of wind turbine and accurately diagnose the common faults of generator and gearbox.
【學(xué)位授予單位】：華南理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM614

【參考文獻】

相關(guān)期刊論文 前10條

1 魏秀業(yè);潘宏俠;黃晉英;;齒輪箱傳感器優(yōu)化布置研究[J];兵工學(xué)報;2010年11期

2 錢雅云;馬宏忠;;雙饋異步電機故障診斷方法綜述[J];大電機技術(shù);2011年05期

3 苗銳;陳國初;李月;徐余法;俞金壽;;基于隨機集含糊證據(jù)的風力發(fā)電機故障診斷方法[J];電力系統(tǒng)自動化;2012年07期

4 王惠中;范少偉;;無線傳輸技術(shù)在風電故障診斷系統(tǒng)中的應(yīng)用[J];風機技術(shù);2011年04期

5 賀德馨;;中國風能發(fā)展戰(zhàn)略研究[J];中國工程科學(xué);2011年06期

6 許伯強,李和明,孫麗玲,趙振波;大型異步電動機狀態(tài)監(jiān)測與故障診斷方法評述[J];華北電力大學(xué)學(xué)報;2002年03期

7 王瑞闖;林富洪;;風力發(fā)電機在線監(jiān)測與診斷系統(tǒng)研究[J];華東電力;2009年01期

8 魏書榮;符楊;馬宏忠;;雙饋風力發(fā)電機定子繞組匝間短路診斷與實驗研究[J];電力系統(tǒng)保護與控制;2010年11期

9 王成;王志新;;基于無線局域網(wǎng)的大型海上風電場遠程監(jiān)控系統(tǒng)設(shè)計[J];機電一體化;2007年05期

10 中央黨校課題組;曹新;;中國新能源發(fā)展戰(zhàn)略問題研究[J];經(jīng)濟研究參考;2011年52期

本文編號：1744391