本文關(guān)鍵詞：基于樸素貝葉斯的電力變壓器故障診斷　出處：《上海交通大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 狀態(tài)檢修 變壓器 故障診斷 貝葉斯網(wǎng)絡(luò)

【摘要】：針對電力變壓器在電力系統(tǒng)中的特殊地位和重要作用,對其日常運(yùn)維中的常見故障及其機(jī)理進(jìn)行了深入研究,確定了電力變壓器各種故障的排除方法。電力變壓器發(fā)生故障后,當(dāng)其故障信息不完整或不確定時,會導(dǎo)致處理故障的難度大大增加,有時甚至?xí)涎臃祻S維修時機(jī)。通過對目前較為常用的幾種故障診斷方法的特點進(jìn)行對比,凸顯了貝葉斯網(wǎng)絡(luò)的在這方面優(yōu)勢,確定了基于貝葉斯網(wǎng)絡(luò)的變壓器故障診斷方法。由于TAN模型屬性變量關(guān)聯(lián)邊的不確定性以及因果關(guān)系模型的主觀性,本文確定了樸素貝葉斯網(wǎng)絡(luò)作為故障診斷方法,并通過簡單實例分析,證明了模型具有很強(qiáng)的可靠性和良好的準(zhǔn)確率。在完備數(shù)據(jù)分析的基礎(chǔ)上,簡要概括了在屬性缺失情況下的EM及SEM算法。給出了利用樸素貝葉斯網(wǎng)絡(luò)進(jìn)行故障診斷的基本方法。同項目結(jié)合,在對一定規(guī)模的數(shù)據(jù)樣本進(jìn)行分析處理的基礎(chǔ)上,對網(wǎng)絡(luò)模型進(jìn)行了仿真計算。通過對結(jié)果的分析,說明了貝葉斯網(wǎng)絡(luò)在故障診斷領(lǐng)域的出色能力和廣闊前景。論文最后探討了應(yīng)用貝葉斯網(wǎng)絡(luò)進(jìn)行故障診斷工作的重點以及研究方向。
[Abstract]:In view of the special position and important role of power transformer in power system, the common faults and their mechanism in daily operation and maintenance are deeply studied. The troubleshooting methods of power transformer are determined. When the fault information of power transformer is incomplete or uncertain, the difficulty of dealing with the fault will be greatly increased. By comparing the characteristics of several commonly used fault diagnosis methods, it highlights the advantages of Bayesian network in this respect. The method of transformer fault diagnosis based on Bayesian network is determined. Because of the uncertainty of attribute variable correlation edge of TAN model and the subjectivity of causality model. In this paper, the naive Bayesian network is determined as the fault diagnosis method, and through a simple example analysis, it is proved that the model has strong reliability and good accuracy, on the basis of complete data analysis. The EM and SEM algorithms in the absence of attributes are briefly summarized, and the basic method of fault diagnosis using naive Bayesian network is given. On the basis of analyzing and processing the data samples of a certain scale, the network model is simulated and calculated. The excellent capability and broad prospect of Bayesian network in the field of fault diagnosis are explained. Finally, the emphasis and research direction of the application of Bayesian network in fault diagnosis are discussed.
【學(xué)位授予單位】：上海交通大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TM407

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