【摘要】：使用對(duì)有種溶解氣體分析的方法進(jìn)行變壓器故障診斷，可在變壓器運(yùn)行期進(jìn)行故障分析的特點(diǎn)，對(duì)于變壓器維修模式的轉(zhuǎn)變有很大的推動(dòng)作用，具有重要的研究意義。本文在分析現(xiàn)有變壓器故障診斷方法的特點(diǎn)及其存在問(wèn)題的基礎(chǔ)上，將極限學(xué)習(xí)機(jī)算法應(yīng)用于變壓器故障診斷。 提出了基于極限學(xué)習(xí)機(jī)的油浸式電力變壓器故障診斷方法。分析了不同隱藏層激活函數(shù)對(duì)極限學(xué)習(xí)機(jī)的診斷性能的影響，給出了診斷的具體實(shí)現(xiàn)方法。這種方法有不容易出現(xiàn)局部值的特點(diǎn)，且訓(xùn)練速度快，參數(shù)設(shè)定簡(jiǎn)單，易于應(yīng)用，適合于在線診斷。并通過(guò)實(shí)例驗(yàn)證了該方法的性能。 給了基于WELM的變壓器故障診斷方法。這種方法主要針對(duì)DGA數(shù)據(jù)中存在的數(shù)據(jù)不均現(xiàn)象，使用加權(quán)方案使數(shù)據(jù)恢復(fù)平衡性。研究了不同加權(quán)方案對(duì)診斷性能的影響。通過(guò)實(shí)驗(yàn)證明了WELM有更好的診斷效果。 在研究KELM參數(shù)優(yōu)化的基礎(chǔ)上提出了基于KELM的變壓器故障診斷方法。提出了使用粒子群優(yōu)化算法結(jié)合K折交叉驗(yàn)證的方法對(duì)KELM參數(shù)進(jìn)行優(yōu)化的方法，給出了具體參數(shù)優(yōu)化和診斷實(shí)現(xiàn)過(guò)程。實(shí)驗(yàn)證明，相比SVM算法，基于KELM的變壓器故障診斷方法診斷準(zhǔn)確率更高，訓(xùn)練時(shí)間更短。
[Abstract]:Using the method of dissolved gas analysis for transformer fault diagnosis, it can be used to analyze the characteristics of transformer fault during the operation period, which has a great role in promoting the transformation of transformer maintenance mode, and has an important significance in research. On the basis of analyzing the characteristics of existing transformer fault diagnosis methods and their existing problems, this paper applies the extreme learning machine algorithm to transformer fault diagnosis. An oil-immersed power transformer fault diagnosis method based on extreme learning machine is proposed. The influence of different hidden layer activation functions on the diagnostic performance of LLM is analyzed, and the realization method of diagnosis is given. This method is not easy to appear the local value, and the training speed is fast, the parameter setting is simple, the method is easy to be applied, and it is suitable for on-line diagnosis. The performance of the method is verified by an example. The method of transformer fault diagnosis based on WELM is given. This method mainly aims at the uneven data in DGA data, and uses the weighted scheme to restore the balance of the data. The influence of different weighting schemes on diagnostic performance was studied. Experimental results show that WELM has better diagnostic effect. Based on the study of KELM parameter optimization, a transformer fault diagnosis method based on KELM is proposed. The particle swarm optimization (PSO) algorithm combined with K-fold cross-validation is proposed to optimize KELM parameters. The process of parameter optimization and diagnosis is given. Experimental results show that compared with SVM algorithm, transformer fault diagnosis method based on KELM has higher accuracy and shorter training time.
【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)】：TM407

【參考文獻(xiàn)】

相關(guān)期刊論文 前1條

1 董明,孟源源,徐長(zhǎng)響,嚴(yán)璋;基于支持向量機(jī)及油中溶解氣體分析的大型電力變壓器故障診斷模型研究[J];中國(guó)電機(jī)工程學(xué)報(bào);2003年07期

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本文編號(hào)：2363439