【摘要】：風(fēng)電機(jī)組雙饋異步電機(jī)是風(fēng)電機(jī)組的重要組成部分,是風(fēng)電機(jī)組實(shí)現(xiàn)變速恒頻的重要設(shè)備,雙饋異步電機(jī)的正常運(yùn)行關(guān)系著機(jī)組的運(yùn)行安全。由于風(fēng)電場(chǎng)環(huán)境惡劣、工況多變,導(dǎo)致發(fā)電機(jī)故障頻發(fā),而風(fēng)電場(chǎng)一般位置偏遠(yuǎn),設(shè)備一旦損壞,備件和更換設(shè)備的周期長(zhǎng)。如果能夠及早的發(fā)現(xiàn)發(fā)電機(jī)的故障,診斷出具體故障模式,并及時(shí)的調(diào)整運(yùn)行模式和施行維修措施,能夠縮短維修時(shí)間,降低維修費(fèi)用。國內(nèi)外已經(jīng)針對(duì)風(fēng)電機(jī)組雙饋異步電機(jī)的狀態(tài)監(jiān)測(cè)和故障診斷進(jìn)行了大量的研究工作,并且隨著研究工作的向前推進(jìn),有些研究成果已經(jīng)產(chǎn)品化,并運(yùn)用到風(fēng)電場(chǎng)中,由于研究不夠深入,功能不夠完善,常有誤報(bào),漏報(bào)故障的事件發(fā)生,主要原因是現(xiàn)有的系統(tǒng)主要是進(jìn)行設(shè)定閾值報(bào)警,觀看數(shù)據(jù)趨勢(shì)變化,并沒有將監(jiān)測(cè)參數(shù)和工況聯(lián)系起來,閾值的設(shè)定不夠合理。而且這些系統(tǒng)給出的診斷結(jié)果只是一個(gè)初步的診斷,沒有定位到具體的故障模式,不利于后續(xù)的維修計(jì)劃的實(shí)施。本文在對(duì)模糊識(shí)別理論研究的基礎(chǔ)上,提出了一套適用于風(fēng)電雙饋異步電機(jī)的多元模糊故障診斷流程。在流程的指導(dǎo)下,首先研究了雙饋異步電機(jī)的結(jié)構(gòu)特點(diǎn)和工作原理,并確定發(fā)電機(jī)典型的故障模式,包括轉(zhuǎn)子不平衡、轉(zhuǎn)子不對(duì)中、軸承故障、定子繞組匝間短路、轉(zhuǎn)子繞組匝間短路。建立發(fā)電機(jī)系統(tǒng)典型故障的動(dòng)力學(xué)模型,對(duì)不同的故障模式進(jìn)行故障機(jī)理分析,研究故障發(fā)生時(shí)的多元故障征兆,結(jié)合故障樹分析,得出故障原因和對(duì)應(yīng)的維修措施,建立每種故障的故障知識(shí)庫。結(jié)合風(fēng)電機(jī)組變工況運(yùn)行特性,提出了基于角域信號(hào)重采樣和階比分析的頻域特征提取方法和基于運(yùn)行工況辨識(shí)和概率分布特性的時(shí)域特征提取方法,選定與振動(dòng)參數(shù)、溫度參數(shù)和電氣參數(shù)相關(guān)的運(yùn)行參數(shù),來劃定運(yùn)行區(qū)間,根據(jù)區(qū)間數(shù)據(jù)的高斯分布特性,來劃定各運(yùn)行區(qū)間特征參數(shù)閾值。綜合考慮故障模式的多種故障征兆,結(jié)合模糊識(shí)別方法,研究融合多種故障征兆的多元模糊故障診斷方法,避免單一參數(shù)識(shí)別故障的片面性和不準(zhǔn)確性,達(dá)到對(duì)系統(tǒng)故障進(jìn)行準(zhǔn)確識(shí)別的目的。識(shí)別出故障后,合理的調(diào)整運(yùn)行方式和安排維修措施。在故障特征提取方法、故障模式識(shí)別方法和故障知識(shí)庫的基礎(chǔ)上,結(jié)合現(xiàn)場(chǎng)實(shí)際情況,研究開發(fā)發(fā)電機(jī)的狀態(tài)監(jiān)測(cè)和故障診斷系統(tǒng),作為“風(fēng)電機(jī)組在線狀態(tài)監(jiān)測(cè)和故障診斷系統(tǒng)TCMM V1.0"大系統(tǒng)的模塊到現(xiàn)場(chǎng)實(shí)際應(yīng)用,是理論與工程實(shí)際相結(jié)合。
[Abstract]:Wind turbine doubly-fed induction motor is an important part of wind turbine and an important equipment for wind turbine to realize variable speed and constant frequency. The normal operation of doubly-fed asynchronous motor is related to the safety of unit operation. Because the wind farm environment is bad and the working condition is changeable, the generator faults occur frequently, and the wind farm is generally located in a remote area, once the equipment is damaged, the period of spare parts and replacement equipment is long. If the generator fault can be detected early, the concrete fault mode can be diagnosed, and the operation mode and maintenance measures can be adjusted in time, the maintenance time can be shortened and the maintenance cost can be reduced. A lot of research work has been done on condition monitoring and fault diagnosis of wind turbine doubly-fed asynchronous motor at home and abroad, and with the advance of research work, some research results have been produced and applied to wind farm. Because the research is not deep enough, the function is not perfect enough, often misinformation and failure events occur, the main reason is that the existing system mainly sets the threshold alarm, watches the data trend change, The monitoring parameters are not associated with the operating conditions, and the threshold setting is not reasonable. Moreover, the diagnosis result given by these systems is only a preliminary diagnosis, which does not locate the specific fault mode, and is not conducive to the implementation of the subsequent maintenance plan. Based on the research of fuzzy recognition theory, a set of multivariate fuzzy fault diagnosis flow for wind power doubly-fed induction motor is presented in this paper. Under the guidance of the flow chart, the structural characteristics and working principle of the doubly-fed induction motor are studied firstly, and the typical fault modes of the generator are determined, including rotor imbalance, rotor misalignment, bearing fault, stator winding inter-turn short circuit, and so on. Short circuit between turns of rotor winding. The dynamic model of typical faults of generator system is established, the fault mechanism of different fault modes is analyzed, and the multiple fault symptoms when faults occur are studied. Combined with fault tree analysis, the fault causes and corresponding maintenance measures are obtained. Build the fault knowledge base for each fault. Combined with the off-condition operation characteristics of wind turbine, a frequency-domain feature extraction method based on angle domain signal resampling and order ratio analysis and a time-domain feature extraction method based on operating condition identification and probability distribution characteristics are proposed. The operating parameters related to temperature parameters and electrical parameters are used to delineate the operation interval and the threshold value of each operation interval characteristic parameter is determined according to the Gao Si distribution characteristics of the interval data. Considering the various fault symptoms of the fault mode and combining the fuzzy identification method, the multi-variable fuzzy fault diagnosis method is studied to avoid the one-sidedness and inaccuracy of single parameter fault identification. To achieve the purpose of accurate identification of system faults. After identifying the fault, adjust the operation mode and arrange the maintenance measures reasonably. On the basis of fault feature extraction method, fault pattern recognition method and fault knowledge base, combined with the actual situation in the field, the condition monitoring and fault diagnosis system of generator is researched and developed. As the module of the large-scale system of "On-line condition monitoring and fault diagnosis system TCMM V1.0" of wind turbine, it is a combination of theory and engineering practice.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM343

【引證文獻(xiàn)】

相關(guān)會(huì)議論文 前2條

1 劉永揚(yáng);孫紅巖;謝志江;;齒式聯(lián)接不對(duì)中轉(zhuǎn)子診斷理論的應(yīng)用[A];設(shè)備監(jiān)測(cè)與診斷技術(shù)及其應(yīng)用——第十二屆全國設(shè)備監(jiān)測(cè)與診斷學(xué)術(shù)會(huì)議論文集[C];2005年

2 羅云林;侯學(xué)智;;基于征兆分解和模糊邏輯的飛機(jī)組件多故障診斷[A];2006中國控制與決策學(xué)術(shù)年會(huì)論文集[C];2006年

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本文編號(hào)：2221063