【摘要】：隨著風(fēng)電產(chǎn)業(yè)的快速發(fā)展,風(fēng)力發(fā)電技術(shù)已經(jīng)成為國內(nèi)外的研究熱點(diǎn)。由于運(yùn)行中的風(fēng)電機(jī)組事故率高,迫切需要一種高效的機(jī)組故障診斷技術(shù),提高機(jī)組可靠性、運(yùn)行效率,降低維護(hù)費(fèi)用、停機(jī)時間。這對風(fēng)電產(chǎn)業(yè)的發(fā)展具有深遠(yuǎn)的影響。論文介紹了故障診斷的基本原理,總結(jié)了直驅(qū)風(fēng)力發(fā)電機(jī)常見故障及故障機(jī)理,分析現(xiàn)有特征提取的基本原理,在此基礎(chǔ)上提出改進(jìn)小波包特征提取法,該方法利用信號的頻譜分析,先確定分析步長和分析位置,再進(jìn)行小波包分解。通過小波包特征提取、多源特征提取和改進(jìn)小波包特征提取的對比分析,證明了改進(jìn)特征提取方法的有效性。分析標(biāo)準(zhǔn)支持向量機(jī)、多類支持向量機(jī),針對直驅(qū)風(fēng)力發(fā)電機(jī)軸承樣本類別分布不平衡的問題,結(jié)合風(fēng)力發(fā)電故障診斷的研究發(fā)展方向,提出一種基于多類代價敏感支持向量的故障診斷方法。分析網(wǎng)格尋優(yōu)算法、粒子群尋優(yōu)算法和遺傳尋優(yōu)算法,在此基礎(chǔ)上,采用改進(jìn)的粒子群尋優(yōu)算法進(jìn)行代價敏感支持向量機(jī)的三參數(shù)尋優(yōu)。經(jīng)過與傳統(tǒng)尋優(yōu)算法的對比分析,證明了改進(jìn)算法尋優(yōu)速度更快。在前面內(nèi)容的基礎(chǔ)上,構(gòu)建直驅(qū)風(fēng)力發(fā)電機(jī)軸承故障診斷模型,通過對其故障樣本集的模擬,分析模型的故障敏感性、魯棒性、新增類型樣本的識別能力,證明了多類代價敏感支持向量機(jī)故障診斷模型的優(yōu)異性能�？偨Y(jié)了直驅(qū)風(fēng)力發(fā)電機(jī)軸承故障診斷方法有待于完善和進(jìn)一步研究的問題。論文所做的工作對直驅(qū)風(fēng)力發(fā)電機(jī)軸承故障診斷具有重要的參考價值。
[Abstract]:With the rapid development of wind power industry, wind power generation technology has become a hot research topic at home and abroad. Because of the high accident rate of wind turbine in operation, it is urgent to need an efficient fault diagnosis technology to improve the reliability, operation efficiency, maintenance cost and downtime of the unit. This has a profound impact on the development of wind power industry. This paper introduces the basic principle of fault diagnosis, summarizes the common faults and fault mechanism of direct drive wind turbine, analyzes the existing basic principles of feature extraction, and puts forward an improved wavelet packet feature extraction method. In this method, the analysis step size and analysis position are determined by using the spectrum analysis of the signal, and then the wavelet packet decomposition is carried out. Through the comparative analysis of wavelet packet feature extraction, multi-source feature extraction and improved wavelet packet feature extraction, the effectiveness of the improved feature extraction method is proved. The standard support vector machine (SVM) and multi-class support vector machine (SVM) are analyzed to solve the problem of unbalanced distribution of bearing samples for direct drive wind turbines, combined with the research and development direction of wind power fault diagnosis. A fault diagnosis method based on multi-class cost-sensitive support vectors is proposed. The grid optimization algorithm, particle swarm optimization algorithm and genetic optimization algorithm are analyzed. on this basis, the improved particle swarm optimization algorithm is used to optimize the three parameters of cost sensitive support vector machine. Compared with the traditional optimization algorithm, it is proved that the improved algorithm is faster. On the basis of the previous contents, the bearing fault diagnosis model of direct drive wind turbine is constructed. through the simulation of its fault sample set, the fault sensitivity, robustness and recognition ability of the new type samples are analyzed. The excellent performance of multi-class cost-sensitive support vector machine fault diagnosis model is proved. This paper summarizes the problems that need to be improved and further studied in the bearing fault diagnosis method of direct drive wind turbine. The work done in this paper has important reference value for bearing fault diagnosis of direct drive wind turbine.
【學(xué)位授予單位】：長沙理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM315
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本文編號：2485222