發(fā)布時間：2018-12-27 08:21

【摘要】：隨著世界能源的緊缺,風能已經(jīng)在各種場合扮演著重要的角色,風力發(fā)電技術在全世界范圍內(nèi)已廣泛使用。然而,由于地理位置偏僻,維修技術復雜等各方面原因,使得風力發(fā)電過程中容易出現(xiàn)故障,且維修難度和運行維護成本較高,因此風力發(fā)電系統(tǒng)運行狀態(tài)評估等工作就顯得尤為重要。風力發(fā)電機組系統(tǒng)龐大,對其進行故障診斷和維修工程難度較大,很難靠單一的技術實現(xiàn)對設備準確、完整的診斷。綜上,針對風力發(fā)電的故障診斷與評估需要一套合理的評價體系來解決這些難題。因此,本文的主要研究內(nèi)容如下:(1)對當前風力發(fā)電機組的基本結(jié)構、工作原理進行了闡述,針對風電機組的運行特性,分析了機組的故障機理,并對機組故障多發(fā)部位及故障診斷常用方法進行了研究,提出了針對不同信號源的故障診斷方法。最后基于風電機組SCADA系統(tǒng)的實測運行數(shù)據(jù),設計了兩種故障診斷與狀態(tài)綜合評價方法。(2)運用熵權模糊綜合評判方法,根據(jù)某風場SCADA系統(tǒng)選取的機組正常運行和故障數(shù)據(jù),對機組建立綜合評判模型。依照所建模型,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”四個等級來評估風電機組的健康狀況,當計算得出有運行狀態(tài)為“差”的子系統(tǒng)時,將其視為故障狀態(tài),需要立即停機檢查,避免故障嚴重化,造成更大損失。(3)當故障發(fā)生時,對機組的各個區(qū)域和部件需進行全面的診斷。在此基礎上,建立風電機組故障樹模型。通過故障樹模型生成了風電機組故障診斷知識庫。介紹了三種故障推理機方式,并采用正反向混合推理設計分級存儲方式下的故障診斷與狀態(tài)評估推理過程流程圖,實現(xiàn)了將復雜風機系統(tǒng)簡單化,模塊化。(4)本文最后通過分析機組齒輪箱系統(tǒng)和發(fā)電機系統(tǒng)正常和故障狀態(tài)時對機組輸出功率影響,結(jié)合故障樹模型,發(fā)現(xiàn)即使細小的故障也能影響其正常運行。因此運用以上方法,當機組某個部件出現(xiàn)異�，F(xiàn)象時,系統(tǒng)能夠及時有效的發(fā)現(xiàn)故障原因并能盡快處理故障,為風電機組的故障診斷及處理提供一定參考。
[Abstract]:With the shortage of energy in the world, wind energy has played an important role in various situations. Wind power generation technology has been widely used in the world. However, due to the remote geographical location and complex maintenance technology, the wind power generation process is prone to failure, and the maintenance difficulty and operation maintenance costs are high. Therefore, wind power system operating state evaluation and other work is particularly important. The wind turbine system is huge, it is difficult to diagnose and maintain the wind turbine system, it is difficult to realize the accurate and complete diagnosis of the equipment by a single technology. In summary, it is necessary to solve these problems by a set of reasonable evaluation system for fault diagnosis and evaluation of wind power generation. Therefore, the main contents of this paper are as follows: (1) the basic structure and working principle of the wind turbine are expounded, and the fault mechanism of the wind turbine is analyzed according to the operating characteristics of the wind turbine. The common methods of fault diagnosis are studied, and the fault diagnosis methods for different signal sources are put forward. Finally, based on the measured operation data of wind turbine SCADA system, two kinds of fault diagnosis and state comprehensive evaluation methods are designed. (2) based on the normal operation and fault data of a certain wind field SCADA system, the entropy weight fuzzy comprehensive evaluation method is used. A comprehensive evaluation model is established for the unit. According to the established model, the operating state of the unit is divided into four grades: "excellent, good, medium and poor" to evaluate the health of wind turbine. When it is calculated that there is a subsystem with a "bad" running state, it should be regarded as a fault state, and it needs to be checked immediately to avoid the serious failure and cause more losses. (3) when the fault occurs, it is necessary to prevent the failure from becoming more serious. (3) when the fault occurs, All areas and components of the unit need to be fully diagnosed. On this basis, the fault tree model of wind turbine is established. The fault diagnosis knowledge base of wind turbine is generated by fault tree model. In this paper, three kinds of fault inference machine are introduced, and the flow chart of fault diagnosis and state evaluation reasoning is designed by using forward and backward hybrid reasoning, which simplifies the complicated fan system. (4) at last, by analyzing the influence of generator and gearbox system on the output power of the unit, and combining with the fault tree model, it is found that even small faults can affect the normal operation of the unit. Therefore, with the above method, the system can find the fault cause and deal with the fault as soon as possible when a unit has abnormal phenomenon, which provides a certain reference for the fault diagnosis and treatment of wind turbine.
【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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