本文選題：PHM + 數據融合　； 參考：《電子科技大學》2015年碩士論文

【摘要】：隨著人們對環(huán)境保護日益重視,國內外風電產業(yè)呈現出高速發(fā)展態(tài)勢。由于國內風電產業(yè)起步較晚,產業(yè)發(fā)展雖然快速但非常粗放,風電裝備制造、風電場運營等諸多方面的產業(yè)成熟度不高,由此帶來風電裝備、電場的事故頻發(fā),目前已成為制約我國風電產業(yè)健康、快速發(fā)展的重要原因。因此,開展風電裝備故障預測與健康管理(PHM,Prognostics and Health Management)的研究具有重要的現實意義和應用價值。以國內某風電場為對象,基于對典型風電場健康管理需求的充分的調研,提出了適合國內風力機健康管理的PHM技術研究框架。首先,按照FMECA(Failure Mode Effects and Criticality Analysis)的分析流程,完成了風力機系統(tǒng)的FMECA分析,給出風力機的主要部件、對應的故障模式、故障主要原因及嚴酷度等。其次,提出了基于風電場SCADA(Supervisory Control And Data Acquisition)數據預處理的方法。提取重要部件的狀態(tài)特征之后,采用加權D_S證據理論融合技術。然后,基于模糊理論提出并建立了變權模糊綜合評價模型,經過實際數據驗證,評價結果貼近風力機的實際情況,驗證了模型的有效性;根據風力機不同的設備特性,采用合適的診斷方法進行了故障診斷的研究,為故障預測提供支持。最后,開展了風力機故障預測的研究,基于灰色理論提出了等維灰數動態(tài)預測模型,仿真和實際應用表明,提出的預測模型提高了風力機故障預測的精度。以國內某風電場采集的實際監(jiān)測數據為樣本,分別對風力機的狀態(tài)評價、故障診斷和狀態(tài)預測進行實例驗證。采用J2EE架構設計和開發(fā)了風力機故障預測與健康管理原型系統(tǒng),該系統(tǒng)集成了實時狀態(tài)評價、故障診斷和狀態(tài)預測,實現了風力機的健康管理。實驗結果表明,論文改進的數據融合方法、變權模糊綜合評價模型及等維灰數動態(tài)預測模型等方法有效、可行,PHM原型系統(tǒng)很好的實現了風力機的狀態(tài)評價、故障診斷和狀態(tài)預測。本文的研究成果對于提高風力機運行的可靠性,降低其故障發(fā)生率,提高風電場的運營效率具有很好的實際應用價值。
[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.
【學位授予單位】：電子科技大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：TM614

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本文編號：2028408