本文選題：PHM + 數(shù)據(jù)融合��； 參考：《電子科技大學(xué)》2015年碩士論文

【摘要】：隨著人們對(duì)環(huán)境保護(hù)日益重視,國(guó)內(nèi)外風(fēng)電產(chǎn)業(yè)呈現(xiàn)出高速發(fā)展態(tài)勢(shì)。由于國(guó)內(nèi)風(fēng)電產(chǎn)業(yè)起步較晚,產(chǎn)業(yè)發(fā)展雖然快速但非常粗放,風(fēng)電裝備制造、風(fēng)電場(chǎng)運(yùn)營(yíng)等諸多方面的產(chǎn)業(yè)成熟度不高,由此帶來(lái)風(fēng)電裝備、電場(chǎng)的事故頻發(fā),目前已成為制約我國(guó)風(fēng)電產(chǎn)業(yè)健康、快速發(fā)展的重要原因。因此,開(kāi)展風(fēng)電裝備故障預(yù)測(cè)與健康管理(PHM,Prognostics and Health Management)的研究具有重要的現(xiàn)實(shí)意義和應(yīng)用價(jià)值。以國(guó)內(nèi)某風(fēng)電場(chǎng)為對(duì)象,基于對(duì)典型風(fēng)電場(chǎng)健康管理需求的充分的調(diào)研,提出了適合國(guó)內(nèi)風(fēng)力機(jī)健康管理的PHM技術(shù)研究框架。首先,按照FMECA(Failure Mode Effects and Criticality Analysis)的分析流程,完成了風(fēng)力機(jī)系統(tǒng)的FMECA分析,給出風(fēng)力機(jī)的主要部件、對(duì)應(yīng)的故障模式、故障主要原因及嚴(yán)酷度等。其次,提出了基于風(fēng)電場(chǎng)SCADA(Supervisory Control And Data Acquisition)數(shù)據(jù)預(yù)處理的方法。提取重要部件的狀態(tài)特征之后,采用加權(quán)D_S證據(jù)理論融合技術(shù)。然后,基于模糊理論提出并建立了變權(quán)模糊綜合評(píng)價(jià)模型,經(jīng)過(guò)實(shí)際數(shù)據(jù)驗(yàn)證,評(píng)價(jià)結(jié)果貼近風(fēng)力機(jī)的實(shí)際情況,驗(yàn)證了模型的有效性;根據(jù)風(fēng)力機(jī)不同的設(shè)備特性,采用合適的診斷方法進(jìn)行了故障診斷的研究,為故障預(yù)測(cè)提供支持。最后,開(kāi)展了風(fēng)力機(jī)故障預(yù)測(cè)的研究,基于灰色理論提出了等維灰數(shù)動(dòng)態(tài)預(yù)測(cè)模型,仿真和實(shí)際應(yīng)用表明,提出的預(yù)測(cè)模型提高了風(fēng)力機(jī)故障預(yù)測(cè)的精度。以國(guó)內(nèi)某風(fēng)電場(chǎng)采集的實(shí)際監(jiān)測(cè)數(shù)據(jù)為樣本,分別對(duì)風(fēng)力機(jī)的狀態(tài)評(píng)價(jià)、故障診斷和狀態(tài)預(yù)測(cè)進(jìn)行實(shí)例驗(yàn)證。采用J2EE架構(gòu)設(shè)計(jì)和開(kāi)發(fā)了風(fēng)力機(jī)故障預(yù)測(cè)與健康管理原型系統(tǒng),該系統(tǒng)集成了實(shí)時(shí)狀態(tài)評(píng)價(jià)、故障診斷和狀態(tài)預(yù)測(cè),實(shí)現(xiàn)了風(fēng)力機(jī)的健康管理。實(shí)驗(yàn)結(jié)果表明,論文改進(jìn)的數(shù)據(jù)融合方法、變權(quán)模糊綜合評(píng)價(jià)模型及等維灰數(shù)動(dòng)態(tài)預(yù)測(cè)模型等方法有效、可行,PHM原型系統(tǒng)很好的實(shí)現(xiàn)了風(fēng)力機(jī)的狀態(tài)評(píng)價(jià)、故障診斷和狀態(tài)預(yù)測(cè)。本文的研究成果對(duì)于提高風(fēng)力機(jī)運(yùn)行的可靠性,降低其故障發(fā)生率,提高風(fēng)電場(chǎng)的運(yùn)營(yíng)效率具有很好的實(shí)際應(yīng)用價(jià)值。
[Abstract]:As people pay more and more attention to environmental protection, wind power industry at home and abroad presents a high-speed development trend. Due to the relatively late start of domestic wind power industry and the rapid but extensive industrial development, the industrial maturity of wind power equipment manufacturing, wind farm operation and many other aspects is not high, resulting in frequent accidents of wind power equipment and electric fields. At present, it has become an important reason for restricting the healthy and rapid development of wind power industry in China. Therefore, the research on wind power equipment fault prediction and health management has important practical significance and application value. Taking a domestic wind farm as an object, a PHM technology research framework suitable for domestic wind turbine health management is put forward based on the sufficient investigation on the health management requirements of typical wind farms. Firstly, according to the analysis flow of FMECAA failure Mode effects and criticality Analysis, the FMECA analysis of wind turbine system is completed, and the main components, corresponding fault modes, main causes and severity of wind turbine system are given. Secondly, a method of data preprocessing based on SCADA-SCADA supervisory control And data acquisition is proposed. After extracting the state features of important parts, the weighted DS evidence theory fusion technique is adopted. Then, based on the fuzzy theory, a fuzzy comprehensive evaluation model with variable weights is proposed and established. The evaluation results are close to the actual situation of the wind turbine, and the validity of the model is verified by the actual data, according to the different equipment characteristics of the wind turbine, the fuzzy comprehensive evaluation model is established based on the fuzzy theory. The research of fault diagnosis is carried out with proper diagnosis method, which provides support for fault prediction. Finally, the research of wind turbine fault prediction is carried out. Based on the grey theory, the dynamic prediction model of equal dimension grey number is proposed. The simulation and practical application show that the proposed prediction model improves the accuracy of wind turbine fault prediction. Taking the actual monitoring data collected from a domestic wind farm as the sample, the status evaluation, fault diagnosis and state prediction of the wind turbine are verified by examples. The prototype system of wind turbine fault prediction and health management is designed and developed with J2EE architecture. The system integrates real-time state evaluation, fault diagnosis and condition prediction, and realizes the health management of wind turbine. The experimental results show that the improved data fusion method, the variable weight fuzzy comprehensive evaluation model and the equal dimension grey number dynamic prediction model are effective, and the PHM prototype system is feasible to realize the wind turbine state evaluation. Fault diagnosis and condition prediction. The research results of this paper have good practical application value to improve the reliability of wind turbine operation, reduce its fault rate, and improve the operational efficiency of wind farm.
【學(xué)位授予單位】：電子科技大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM614

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 彭華東;陳曉清;任明;楊代勇;董明;;風(fēng)電機(jī)組故障智能診斷技術(shù)及系統(tǒng)研究[J];電網(wǎng)與清潔能源;2011年02期

相關(guān)碩士學(xué)位論文 前1條

1 張登峰;風(fēng)力發(fā)電設(shè)備狀態(tài)評(píng)價(jià)系統(tǒng)設(shè)計(jì)[D];鄭州大學(xué);2011年

，

本文編號(hào)：2028408