【摘要】：我國作為一個能源需求大國,對能源的需求與日俱增。但我國的能源結構處于欠合理狀態(tài),主要表現(xiàn)為對于化石能源的依賴嚴重,清潔能源占比不高等問題。隨著化石能源的枯竭、環(huán)境惡化等問題的出現(xiàn),都要求我國逐步發(fā)展清潔能源來改變傳統(tǒng)的能源結構。在眾多清潔能源中,風能作為其中最具代表性的一種,憑借著分布廣泛、商業(yè)化程度高、技術成熟等優(yōu)勢正在越來越多的發(fā)揮著重要作用。但風電機組工作環(huán)境惡劣,經常面臨風速不穩(wěn)定、內外環(huán)境溫差大、載荷多變等問題。不同類型的風電機組也將面臨不同的惡劣環(huán)境,如海上風電機組所處環(huán)境空氣濕度大、鹽分高,機組中零部件易受到腐蝕;陸上風電機組面臨的最大環(huán)境問題是空氣中沙塵大,當機組密封條件不佳時,沙塵進入機組極易造成齒輪損壞等問題,眾多因素導致了風電場運維成本持續(xù)居高不下。據估計,在風電機組的運行壽命周期內,運維費用是發(fā)電總成本的重要組成部分,約占總成本的25%~30%。同時,對海上風電機組的運行統(tǒng)計中發(fā)現(xiàn),50%的停運時間是由齒輪箱故障造成的。根據以上數(shù)據可以看出,對風電機組行星齒輪箱運行狀態(tài)做出及時的識別與診斷,具有極大實際應用意義。本文主要研究了風電機組行星齒輪箱故障的主要原因及其故障檢測的有效方法:1)分析了風電機組行星齒輪箱中不同類型故障出現(xiàn)的主要原因,總結了不同類型的故障特征,并針對不同故障特征提出了對應的運行維護方法,提高了機組運行的可靠性。2)提出一種基于改進EEMD的自適應信號分解方法�？梢葬槍Σ煌盘栕赃m應給出不同的分解參數(shù),在實際應用中一定程度的解決了傳統(tǒng)EEMD分解過程中的模態(tài)混疊問題、提高了計算速度、改善了分解效果。達到了信號自適應分解的目的。3)利用提出的改進EEMD方法實際信號進行分解,然后使用單重分形維數(shù)提取經改進EEMD分解后各個分量中的分形特征,通過對比信號特征實現(xiàn)了對行星齒輪箱故障的實時有效的診斷。4)利用多重分形維數(shù)譜與支持向量機的結合方法,實現(xiàn)了對行星齒輪箱在不同轉速情況下的故障的診斷,進一步證實了分形維數(shù)對于信號特征具有良好的提取能力。同時證明了支持向量機對于信號的分類效果良好。5)最后通過使用單重分形維數(shù)提取經過改進EEMD分解后的各個分量的分形特性,然后將提取的得到的信號特征作為支持向量機的輸入向量進行信號分類,實現(xiàn)了對實際故障信號的有效診斷。
[Abstract]:As a large country of energy demand, China has a growing demand for energy. However, the energy structure of our country is in an unreasonable state, which is mainly due to the heavy dependence on fossil energy and the low proportion of clean energy. With the depletion of fossil energy and the appearance of environmental deterioration, China needs to develop clean energy gradually to change the traditional energy structure. Among the many clean energy sources, wind energy, as one of the most representative, is playing an important role with the advantages of wide distribution, high degree of commercialization, mature technology and so on. However, wind turbine often faces some problems such as unstable wind speed, large temperature difference between inside and outside environment, variable load and so on. Different types of wind turbine units will also be faced with different adverse environment, such as offshore wind turbine units in high air humidity, high salinity, unit components are vulnerable to corrosion; The biggest environmental problem faced by onshore wind turbines is the large dust in the air. When the sealing conditions of the units are not good, the sand dust entering the units is easy to cause gear damage and so on. Many factors cause the operation and maintenance costs of the wind farms to remain high. It is estimated that the cost of operation and maintenance is an important part of the total cost of power generation in the operational life cycle of wind turbine, accounting for about 2530% of the total cost. At the same time, it is found that 50% of the outage time is caused by gearbox failure. It can be seen from the above data that it is of great practical significance to identify and diagnose the running state of planetary gearbox of wind turbine unit in time. In this paper, the main causes of planetary gearbox faults of wind turbine and the effective methods of fault detection are studied. 1) the main causes of different types of faults in planetary gearboxes of wind turbines are analyzed, and the characteristics of different types of faults are summarized. According to different fault characteristics, the corresponding operation and maintenance methods are proposed to improve the reliability of unit operation. 2) an adaptive signal decomposition method based on improved EEMD is proposed. Different decomposition parameters can be given according to different signal adaptations. In practical application, modal aliasing in the traditional EEMD decomposition process is solved to a certain extent, and the calculation speed is improved and the decomposition effect is improved. The purpose of adaptive signal decomposition is achieved. 3) the improved EEMD method is used to decompose the actual signal, and then the fractal features of each component after the improved EEMD decomposition are extracted by using the single multifractal dimension. The real-time and effective fault diagnosis of planetary gearbox is realized by comparing the signal features. 4) the fault diagnosis of planetary gearbox under different rotational speeds is realized by the combination of multifractal dimension spectrum and support vector machine. It is further proved that fractal dimension has a good ability to extract signal features. At the same time, it is proved that SVM has a good effect on signal classification. 5) finally, the fractal characteristics of each component after improved EEMD decomposition are extracted by using single multifractal dimension. Then, the extracted signal features are classified as input vectors of support vector machine (SVM), and the effective diagnosis of actual fault signals is realized.
【學位授予單位】：上海電力學院
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM315

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