發(fā)布時間：2018-04-17 00:41

  本文選題：變壓器 + 溶解氣體分析　； 參考：《華北電力大學(xué)》2014年碩士論文

【摘要】：油色譜在線監(jiān)測技術(shù)對于發(fā)現(xiàn)變壓器早期潛伏性故障具有重要的實際價值�；谟椭腥芙鈿怏w比值的傳統(tǒng)診斷方法具有簡單、實用的特點,但診斷準(zhǔn)確率不高。本文在分析傳統(tǒng)比值診斷方法以及常用智能診斷方法不足的基礎(chǔ)上,將蟻群優(yōu)化支持向量機(jī)應(yīng)用于變壓器故障診斷。主要內(nèi)容如下： 收集了517組變壓器事故前油中溶解氣體數(shù)據(jù),并采用大衛(wèi)三角法,四比值法以及改良三比值診斷法對其進(jìn)行診斷,通過對診斷結(jié)果的分析,論證了傳統(tǒng)診斷方法的不足。 由于不同種類氣體含量數(shù)值差距較大,對數(shù)據(jù)采用標(biāo)準(zhǔn)化的數(shù)據(jù)變換方法,并通過對比數(shù)據(jù)變換前后診斷正確率的變化,分析了數(shù)據(jù)變換對診斷精度的影響。針對目前色譜儀檢測的氣體種類以及檢測精度的差異,采用相關(guān)性分析方法與距離可分性判據(jù)對特征參量進(jìn)行選擇,并分析了冗余參量及不相關(guān)參量對診斷精度的影響。 支持向量機(jī)診斷的精度與參數(shù)的選取密切相關(guān),鑒于蟻群算法良好的優(yōu)化性能,提出了蟻群優(yōu)化支持向量機(jī)的方法。為證明該方法的可行性及優(yōu)越性,將其與廣泛使用的遺傳優(yōu)化算法進(jìn)行理論分析與對比,并對蟻群系統(tǒng)的設(shè)計進(jìn)行了詳細(xì)地介紹。 基于現(xiàn)場數(shù)據(jù)建立了基于蟻群優(yōu)化支持向量機(jī)的變壓器故障診斷模型,重點對比了本文方法與遺傳算法優(yōu)化支持向量機(jī)方法的診斷效果�？紤]到優(yōu)化算法的不確定性對診斷結(jié)果的影響,采用對測試數(shù)據(jù)進(jìn)行多次診斷并比較結(jié)果,增加了算法對比結(jié)果的說服力。最后,將診斷結(jié)果與改良三比值的診斷效果進(jìn)行對比,充分證明了蟻群優(yōu)化支持向量機(jī)故障診斷方法的優(yōu)越性。 基于現(xiàn)場數(shù)據(jù)建立了基于支持向量機(jī)回歸算法和時間序列分析的變壓器故障預(yù)測模型,重點對比了本文方法與灰色預(yù)測方法的診斷效果。實例仿真表明,基于蟻群優(yōu)化支持向量回歸的變壓器故障預(yù)測模型能很好地應(yīng)用于變壓器油中溶解氣體含量的預(yù)測,并且預(yù)測效果優(yōu)于灰色預(yù)測模型,具有較好的泛化能力。
[Abstract]:On-line monitoring of oil chromatography is of great practical value in detecting early latent faults of transformers.The traditional diagnosis method based on dissolved gas ratio in oil is simple and practical, but the diagnostic accuracy is not high.On the basis of analyzing the deficiency of traditional ratio diagnosis method and common intelligent diagnosis method, this paper applies ant colony optimization support vector machine to transformer fault diagnosis.The main contents are as follows:517 sets of dissolved gas data in transformer oil before accident were collected and diagnosed by using David's triangle method, four-ratio method and modified three-ratio diagnostic method. Through the analysis of the diagnostic results, the shortcomings of the traditional diagnostic methods were demonstrated.Because of the large difference in numerical value of different kinds of gases, a standardized data transformation method is used to analyze the effect of data transformation on diagnostic accuracy by comparing the changes of diagnostic accuracy before and after data transformation.Aiming at the difference of gas types and detection accuracy of chromatograph at present, the correlation analysis method and distance separability criterion are used to select the characteristic parameters, and the effects of redundant and irrelevant parameters on the diagnostic accuracy are analyzed.The accuracy of SVM diagnosis is closely related to the selection of parameters. In view of the good performance of ant colony algorithm, a method of ant colony optimization support vector machine is proposed.In order to prove the feasibility and superiority of this method, it is theoretically analyzed and compared with the widely used genetic optimization algorithm, and the design of ant colony system is introduced in detail.Based on field data, a transformer fault diagnosis model based on ant colony optimization support vector machine is established.Considering the influence of the uncertainty of the optimization algorithm on the diagnosis results, the test data are diagnosed several times and the results are compared, which increases the persuasiveness of the comparison results of the algorithm.Finally, the result of diagnosis is compared with that of the improved three ratio, which fully proves the superiority of the ant colony optimization support vector machine (SVM) method for fault diagnosis.Based on field data, a transformer fault prediction model based on support vector machine regression algorithm and time series analysis is established, and the diagnosis results of this method and grey prediction method are compared.The simulation results show that the transformer fault prediction model based on ant colony optimization support vector regression can be applied to predict dissolved gas content in transformer oil, and the prediction effect is better than that of grey prediction model, and it has better generalization ability.
【學(xué)位授予單位】：華北電力大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TM407

【參考文獻(xiàn)】

相關(guān)期刊論文 前6條

1 霍斌;白妙青;;支持向量機(jī)在農(nóng)業(yè)經(jīng)濟(jì)預(yù)測中的研究[J];山西大學(xué)學(xué)報(自然科學(xué)版);2009年03期

2 吳慶洪;張穎;馬宗民;;蟻群算法綜述[J];微計算機(jī)信息;2011年03期

3 徐文,王大忠,周澤存,陳珩;結(jié)合遺傳算法的人工神經(jīng)網(wǎng)絡(luò)在電力變壓器故障診斷中的應(yīng)用[J];中國電機(jī)工程學(xué)報;1997年02期

4 朱永利,吳立增,李雪玉;貝葉斯分類器與粗糙集相結(jié)合的變壓器綜合故障診斷[J];中國電機(jī)工程學(xué)報;2005年10期

5 王永強(qiáng);律方成;李和明;;基于粗糙集理論和貝葉斯網(wǎng)絡(luò)的電力變壓器故障診斷方法[J];中國電機(jī)工程學(xué)報;2006年08期

6 林升梁;劉志;;基于RBF核函數(shù)的支持向量機(jī)參數(shù)選擇[J];浙江工業(yè)大學(xué)學(xué)報;2007年02期

相關(guān)博士學(xué)位論文 前5條

1 鄭元兵;變壓器故障診斷與預(yù)測集成學(xué)習(xí)方法及維修決策模型研究[D];重慶大學(xué);2011年

2 肖燕彩;支持向量機(jī)在變壓器狀態(tài)評估中的應(yīng)用研究[D];北京交通大學(xué);2008年

3 楊廷方;變壓器在線監(jiān)測與故障診斷新技術(shù)的研究[D];華中科技大學(xué);2008年

4 鄭含博;電力變壓器狀態(tài)評估及故障診斷方法研究[D];重慶大學(xué);2012年

5 鄧燕;基于粗糙集—支持向量機(jī)的油氣儲層參數(shù)預(yù)測方法研究[D];中國地質(zhì)大學(xué)（北京）;2013年

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本文編號：1761334