本文選題：油浸式變壓器 + 故障診斷　； 參考：《華北電力大學(北京)》2017年碩士論文

【摘要】：電力傳輸與人們的生產(chǎn)、生活息息相關(guān),油浸式變壓器是輸配電系統(tǒng)中關(guān)鍵的樞紐設(shè)備,為了不對生產(chǎn)、生活產(chǎn)生影響,及時而準確地檢測出油浸式變壓器早期潛伏性故障,保證變壓器正常、穩(wěn)定運行,顯的尤為重要。本文針對如何提高油浸式變壓器故障診斷正確率的問題展開研究,對已有方法進行深入研究,并對其進行改進;在此基礎(chǔ)上提出了一種極限學習機與D-S證據(jù)理論相結(jié)合的故障診斷方法。論文首先針對極限學習機缺乏概率輸出的問題,引入了多分類概率極限學習機的方法,此方法先將多分類問題分解為多個二分類問題,再使用Sigmoid函數(shù)將二分類極限學習機輸出映射為概率輸出,然后通過求解一個二次規(guī)劃問題,使多個二分極限學習機的概率輸出融合為多分類概率輸出,將多分類概率極限學機應用于變壓器故障診斷,為變壓器故障診斷提供了概率參考;文中還對D-S證據(jù)理論存在沖突證據(jù)時,會存在融合結(jié)果與實際結(jié)果不符的問題,對其進行改進,為此本文引進證據(jù)體相似度的概念,計算出證據(jù)集的加權(quán)平均值,確定證據(jù)集主元后,對證據(jù)集進行修正,使證據(jù)融合在有沖突的情況下,也有較強的處理能力。其次,本文將多分類概率極限學習機與改進的D-S證據(jù)理論相結(jié)合,建立基于信息融合技術(shù)的故障診斷模型,將整個診斷過程分為初步診斷層和融合診斷層。本文將油浸式變壓器DGA氣體的原始數(shù)據(jù)構(gòu)造為氣體含量、氣體比值、三比值三個特征向量作為初步診斷層的輸入,得到三個概率輸出,然后使用改進的D-S證據(jù)理論進行融合診斷,得到最終的診斷結(jié)果。最后,本文在以上研究內(nèi)容的基礎(chǔ)之上,設(shè)計并實現(xiàn)了基于信息融合技術(shù)的油浸式變壓器故障診斷系統(tǒng)。
[Abstract]:Power transmission is closely related to people's production and life. Oil-immersed transformer is the key hub equipment in transmission and distribution system. In order not to affect production and life, early latent faults of oil-immersed transformer can be detected in time and accurately.It is very important to ensure the normal and stable operation of the transformer.In this paper, how to improve the correct rate of fault diagnosis of oil-immersed transformer is studied, and the existing methods are deeply studied and improved.On the basis of this, a fault diagnosis method combining extreme learning machine and D-S evidence theory is proposed.Firstly, aiming at the problem of the lack of probability output, this paper introduces the method of multi-classification probabilistic learning machine, which decomposes the multi-classification problem into multiple two-classification problems.Then the output of binary extreme learning machine is mapped to probabilistic output by using Sigmoid function. Then, by solving a quadratic programming problem, the probabilistic output of multiple binary extreme learning machines is fused into multi-class probabilistic output.The application of multi-class probabilistic limit machine to transformer fault diagnosis provides a probabilistic reference for transformer fault diagnosis, and when there is conflict evidence in D-S evidence theory, there will be a problem that the fusion result does not agree with the actual result.This paper introduces the concept of evidence body similarity, calculates the weighted average of evidence set, determines the principal component of evidence set, modifies the evidence set, and makes evidence fusion in the case of conflict.Also has the strong processing ability.Secondly, a fault diagnosis model based on information fusion technology is established by combining multi-classification probabilistic extreme learning machine with improved D-S evidence theory. The whole diagnosis process is divided into primary diagnosis layer and fusion diagnosis layer.In this paper, the original data of DGA gas of oil-immersed transformer are constructed as gas content, gas ratio and three characteristic vectors as the input of the preliminary diagnostic layer, and three probabilistic outputs are obtained.Then the improved D-S evidence theory is used for fusion diagnosis and the final diagnosis results are obtained.Finally, on the basis of the above research, this paper designs and implements an oil-immersed transformer fault diagnosis system based on information fusion technology.
【學位授予單位】：華北電力大學(北京)
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TP202;TM407;TP277

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本文編號：1763871