本文選題：風(fēng)力發(fā)電 + 狀態(tài)監(jiān)測(cè)　； 參考：《華北電力大學(xué)》2014年碩士論文

【摘要】：風(fēng)力發(fā)電作為一種具有大規(guī)模開發(fā)潛能的可再生能源,近年來在世界范圍內(nèi)受到了廣泛關(guān)注,其中風(fēng)力發(fā)電機(jī)組大型設(shè)備狀態(tài)監(jiān)測(cè)成為風(fēng)電研究領(lǐng)域的重要組成部分。本文以風(fēng)力發(fā)電為背景,基于數(shù)據(jù)挖掘方法研究風(fēng)電機(jī)組狀態(tài)監(jiān)測(cè)方法。研究主要分為兩個(gè)方向：一、故障之間的聯(lián)系；二、故障與參數(shù)的聯(lián)系。本課題研究的主要內(nèi)容如下： 1、分析風(fēng)電機(jī)組故障數(shù)據(jù),研究數(shù)據(jù)挖掘中的關(guān)聯(lián)規(guī)則及其算法并對(duì)算法加以改進(jìn)。以槳距角不對(duì)稱故障為研究案例,采用改進(jìn)Apriori關(guān)聯(lián)規(guī)則算法對(duì)變槳故障前后的大量連續(xù)報(bào)警信息進(jìn)行深入分析,并結(jié)合機(jī)組和變槳系統(tǒng)的運(yùn)行機(jī)理,發(fā)現(xiàn)了某些故障間的密切聯(lián)系。過濾去除次要冗余信息,提煉出有效主導(dǎo)故障報(bào)警,大大減少了報(bào)警量,有效提高運(yùn)行人員的工作效率。 2、在尋找故障與參數(shù)之間隱含聯(lián)系時(shí),參數(shù)維數(shù)過多會(huì)產(chǎn)生“維數(shù)災(zāi)難”,為此研究特征選擇算法,建立ReliefF特征選擇模型,同時(shí)結(jié)合相關(guān)度分析,對(duì)風(fēng)電機(jī)組參數(shù)進(jìn)行降維處理,從47個(gè)參數(shù)中提取了8個(gè)分類能力強(qiáng)的特征參數(shù),剔除冗余信息,降低特征向量的維數(shù),為分類工作打好堅(jiān)實(shí)的基礎(chǔ)。 3、為分析故障與參數(shù)之間的關(guān)系,研究各類分類算法的理論知識(shí)與基本步驟,同時(shí)建立了BP神經(jīng)網(wǎng)絡(luò)分類模型,以槳距角不對(duì)稱故障為分析對(duì)象,利用特征選擇算法提取的參數(shù)來辨別風(fēng)機(jī)運(yùn)行狀態(tài),以達(dá)到風(fēng)電機(jī)組狀態(tài)監(jiān)測(cè)的目的。結(jié)合實(shí)際數(shù)據(jù)分析得到BP神經(jīng)網(wǎng)絡(luò)分類能夠較好的對(duì)槳距角不對(duì)稱故障進(jìn)行分類,判斷風(fēng)機(jī)是否正常運(yùn)行,較好地達(dá)到風(fēng)電機(jī)組狀態(tài)監(jiān)測(cè)的目的。
[Abstract]:Wind power generation, as a renewable energy with large-scale development potential, has attracted worldwide attention in recent years, in which large-scale wind turbine equipment condition monitoring has become an important part of wind power research. In this paper, wind power generation as a background, based on data mining method to study wind turbine condition monitoring method. The research is divided into two directions: first, the relationship between faults and parameters. The main contents of this research are as follows: 1. The fault data of wind turbine are analyzed, and the association rules and their algorithms in data mining are studied and improved. Taking the unsymmetrical fault of pitch angle as a case study, the improved Apriori association rule algorithm is used to analyze a large number of continuous alarm information before and after the fault, and combined with the operation mechanism of the unit and the variable propeller system. A close connection has been found between certain faults. Filter the secondary redundant information, extract the effective leading fault alarm, greatly reduce the alarm amount, and effectively improve the working efficiency of the operators. 2, when looking for the hidden relation between fault and parameter, too many parameter dimension will produce "dimension disaster". Therefore, the feature selection algorithm is studied, the ReliefF feature selection model is established, and the correlation analysis is carried out. Through dimensionality reduction of wind turbine parameters, 8 feature parameters with strong classification ability are extracted from 47 parameters, redundant information is eliminated and dimension of eigenvector is reduced, which lays a solid foundation for classification work. 3. In order to analyze the relationship between fault and parameters, the theoretical knowledge and basic steps of all kinds of classification algorithms are studied. At the same time, a BP neural network classification model is established, and the asymmetric fault of pitch angle is taken as the analysis object. The parameters extracted by the feature selection algorithm are used to distinguish the running state of wind turbine in order to achieve the purpose of wind turbine condition monitoring. Combined with the actual data analysis, BP neural network classification can be a good classification of pitch angle asymmetry fault classification, to judge whether the fan is running normally, and to achieve the purpose of wind turbine condition monitoring.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2014
【分類號(hào)】：TM614

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本文編號(hào)：1859111