本文選題：風電機組 + 故障特征　； 參考：《華北電力大學》2017年碩士論文

【摘要】：目前,風力發(fā)電越來越受到全球各國的重視,隨著風電機組的逐年投運,機組也逐步進入事故高發(fā)階段。風力發(fā)電機組故障診斷能夠有效減少重大事故的發(fā)生,而且可實時監(jiān)測風力發(fā)電機組的運行狀態(tài),識別其異常情況,降低運行成本,保障機組安全高效運行。因此,風力發(fā)電機組的系統(tǒng)監(jiān)測與故障診斷已經(jīng)成為風電發(fā)展中的重要研究方向。針對風力發(fā)電機組故障頻發(fā)的現(xiàn)象,本文在深入分析風電場SCADA數(shù)據(jù)的基礎(chǔ)上,對機組的整體運行狀態(tài)作了評估,并且對其故障率較高的部位—發(fā)電機與齒輪箱,作了進一步的分析與故障診斷設(shè)計。主要內(nèi)容如下:(1)介紹了風力發(fā)電機組的工作原理與結(jié)構(gòu)組成,針對風電機組的運行特性,分析了機組的故障機理,并對機組故障易發(fā)部位及故障診斷常用方法進行研究,提出針對不同信號源的故障診斷方法,進而設(shè)計了本文故障診斷及狀態(tài)監(jiān)測方法。(2)運用模糊綜合評判的方法,由某風電場SCADA系統(tǒng)選取出某個時刻機組的運行數(shù)據(jù),對風電機組建立模糊綜合評判模型,按照所建立的模型,對機組的運行狀態(tài)進行劃分,本文將機組運行狀態(tài)劃分為“優(yōu)、良、中、差”四個等級,當機組工作在“差”狀態(tài)時,表明機組運行已經(jīng)出現(xiàn)故障,需要立即停機進行檢查,避免故障嚴重化,造成更大的損失。(3)風力發(fā)電機組出現(xiàn)故障時,需要對機組的各個子系統(tǒng)進行故障診斷。本文采用非線性狀態(tài)評估方法,對機組的發(fā)電機和齒輪箱分別進行建模與預(yù)警,通過設(shè)置溫度偏移來模擬故障發(fā)生,進而驗證了非線性狀態(tài)評估方法對發(fā)電機與齒輪箱故障診斷與狀態(tài)監(jiān)測的可行性,為風電機組的故障診斷與狀態(tài)監(jiān)測提供了新的思路和參考。(4)風力發(fā)電機組的子系統(tǒng)出現(xiàn)故障時會對機組造成一定的影響,本文通過分析發(fā)電機或齒輪箱出現(xiàn)故障時對機組輸出功率的影響,說明了風電機組故障的相互關(guān)聯(lián)性,并且通過對影響的進一步分析,得出了發(fā)電機側(cè)故障對機組的影響大于齒輪箱側(cè)的結(jié)果,為風電機組排除故障提供了一定的參考。
[Abstract]:At present, wind power generation is paid more and more attention by the countries all over the world. With the wind turbine running year by year, the wind turbine has gradually entered the stage of high accident rate. The fault diagnosis of wind turbine can effectively reduce the occurrence of serious accidents, and can monitor the operating state of wind turbine in real time, identify its abnormal situation, reduce the operating cost and ensure the safe and efficient operation of wind turbine. Therefore, wind turbine system monitoring and fault diagnosis has become an important research direction in wind power development. In this paper, based on the analysis of wind farm SCADA data, the overall operating state of wind turbine generator is evaluated, and the high failure rate of generator and gearbox is discussed. Further analysis and fault diagnosis design are made. The main contents are as follows: (1) the working principle and structure of wind turbine are introduced. According to the operating characteristics of wind turbine, the fault mechanism of wind turbine is analyzed, and the fault prone parts and common methods of fault diagnosis are studied. This paper presents a fault diagnosis method for different signal sources, and then designs the method of fault diagnosis and condition monitoring in this paper. By using the method of fuzzy comprehensive evaluation, the operation data of the unit at a certain time are selected from the SCADA system of a wind farm. The fuzzy comprehensive evaluation model of wind turbine is established. According to the established model, the operating state of the unit is divided into four grades: "excellent, good, medium and bad". When the unit is working in a "bad" state, the operating state of the unit is divided into four grades: "excellent, good, medium and bad". It shows that the operation of the unit has already appeared the fault, it is necessary to stop immediately to check, avoid the fault serious, cause bigger loss. 3) when the wind turbine has the fault, need to carry on the fault diagnosis to each subsystem of the unit. In this paper, the nonlinear state evaluation method is used to model and warn the generator and gearbox respectively, and the fault is simulated by setting temperature offset. Furthermore, the feasibility of nonlinear state evaluation method for fault diagnosis and condition monitoring of generator and gearbox is verified. It provides a new way of thinking and reference for wind turbine fault diagnosis and condition monitoring. By analyzing the influence of generator or gearbox failure on the output power of the unit, this paper explains the interrelation of the wind turbine fault, and through the further analysis of the influence, It is concluded that the effect of generator side fault is greater than that of gearbox side, which provides a certain reference for wind turbine troubleshooting.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315

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