發(fā)布時間：2018-11-28 16:13

【摘要】：使用對有種溶解氣體分析的方法進行變壓器故障診斷，可在變壓器運行期進行故障分析的特點，對于變壓器維修模式的轉(zhuǎn)變有很大的推動作用，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及其存在問題的基礎上，將極限學習機算法應用于變壓器故障診斷。 提出了基于極限學習機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)對極限學習機的診斷性能的影響，給出了診斷的具體實現(xiàn)方法。這種方法有不容易出現(xiàn)局部值的特點，且訓練速度快，參數(shù)設定簡單，易于應用，適合于在線診斷。并通過實例驗證了該方法的性能。 給了基于WELM的變壓器故障診斷方法。這種方法主要針對DGA數(shù)據(jù)中存在的數(shù)據(jù)不均現(xiàn)象，使用加權方案使數(shù)據(jù)恢復平衡性。研究了不同加權方案對診斷性能的影響。通過實驗證明了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折交叉驗證的方法對KELM參數(shù)進行優(yōu)化的方法，給出了具體參數(shù)優(yōu)化和診斷實現(xiàn)過程。實驗證明，相比SVM算法，基于KELM的變壓器故障診斷方法診斷準確率更高，訓練時間更短。
[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.
【學位授予單位】：華北電力大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM407

【參考文獻】

相關期刊論文 前1條

1 董明,孟源源,徐長響,嚴璋;基于支持向量機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];中國電機工程學報;2003年07期

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本文編號：2363439