本文選題：變壓器　切入點：參數(shù)尋優(yōu)　出處：《安徽理工大學》2017年碩士論文

【摘要】：伴隨著世界工業(yè)水平的快速提升,全國電網(wǎng)的互聯(lián)已大致形成,對于電力系統(tǒng)安全穩(wěn)定運行的要求越來越嚴格。變壓器作為電能傳送過程當中的核心裝備之一,對于整個電力系統(tǒng)而言,起到了電能輸送以及電壓等級變換的作用。然而,其也是導致電力系統(tǒng)網(wǎng)絡發(fā)生故障的電氣裝備之一。能否準確地預判出變壓器的潛伏性故障類型,對于變壓器的安全穩(wěn)定運行是至關重要的。因此,針對于變壓器的運行狀態(tài)進行監(jiān)控是必要的。本文將圍繞變壓器故障診斷方面的內容進行相關闡述。就變壓器的診斷方式而言,當其是建立在分析變壓器油中溶解氣體的故障診斷方法時,相對比較穩(wěn)妥。首先,本文將從油中溶解氣體的來源、溶解、耗損等方面內容的介紹之后提出變壓器故障的類型及傳統(tǒng)意義上的診斷方法(三比值法)。由于三比值法在診斷速率、準確率等方面存在缺陷,進而提出了結合支持向量機(SVM)算法的模型診斷方式。不過,僅靠單一的診斷方式進行故障的判定,其效果必然是不盡人意的。為了進一步提高診斷的準確率以及收斂速度,提出了交叉驗證的方式以及粒子群算法對支持向量機的核函數(shù)進行參數(shù)c/g的改良,從而得到更加合適的分類效果及精確率。對變壓器的故障類型進行相應的編碼之后,建立變壓器的故障診斷仿真模型,得出基于交叉驗證的SVM分類結果是84%,粒子群算法優(yōu)化后得到的SVM分類結果是85.3333%。從而驗證了優(yōu)化核函數(shù)的參數(shù)c/g的可行性,進而驗證了選取以SVM為核心的變壓器故障診斷方法是可行的。
[Abstract]:With the rapid improvement of the world industrial level, the interconnection of the national power grid has been formed, and the requirements for the safe and stable operation of the power system are becoming more and more strict.As one of the core equipments in the process of power transmission, transformer plays the role of power transmission and voltage grade conversion for the whole power system.However, it is also one of the electrical equipments that lead to the failure of power system network.It is very important for the safe and stable operation of transformers to determine accurately the latent fault types of transformers.Therefore, it is necessary to monitor the operation state of transformer.This paper will focus on transformer fault diagnosis related to the content of the elaboration.As far as the diagnosis mode of transformer is concerned, it is relatively safe to establish a fault diagnosis method based on the analysis of dissolved gas in transformer oil.First of all, after introducing the source, dissolution and consumption of dissolved gas in oil, this paper puts forward the types of transformer faults and the traditional diagnosis method (three-ratio method).Due to the shortcomings of the three-ratio method in diagnosis rate and accuracy, a model diagnosis method combining support vector machine (SVM) algorithm is proposed.However, only by a single diagnosis method to determine the fault, its effect must not be satisfactory.In order to further improve the accuracy and convergence rate of diagnosis, the cross-validation method and particle swarm optimization (PSO) algorithm are proposed to improve the kernel function parameters of support vector machine (SVM), c / g, so as to obtain a more appropriate classification effect and accuracy rate.After the corresponding coding of transformer fault type, the simulation model of transformer fault diagnosis is established, and the result of SVM classification based on cross verification is 84 and the SVM classification result obtained by particle swarm optimization is 85.3333.The feasibility of optimizing the parameter c / g of the kernel function is verified, and the feasibility of selecting the transformer fault diagnosis method with SVM as the core is verified.
【學位授予單位】：安徽理工大學
【學位級別】：碩士
【學位授予年份】：2017
【分類號】：TM407

【參考文獻】

相關期刊論文 前10條

1 屠立忠;李亞運;李志紅;崔強;;基于遺傳優(yōu)化支持向量機的變壓器故障診斷[J];科技創(chuàng)新與應用;2016年12期

2 李宗岳;陳志軍;李名遠;;基于改進FOA-LSSVM的變壓器故障診斷[J];水電能源科學;2016年04期

3 龔健;;基于支持向量機的變壓器故障診斷研究[J];電氣開關;2016年02期

4 劉春鵬;姚毅;賈金玲;楊紅英;宿廣福;;兩種前向神經網(wǎng)絡在變壓器故障診斷中的應用[J];計算機測量與控制;2016年02期

5 苑津莎;張利偉;李中;張英慧;;基于互補免疫算法的變壓器故障診斷[J];電工技術學報;2015年24期

6 韓麗;孫望;羅朋;余鵬璽;杜剛;李云鵬;;差分進化算法在變壓器故障診斷中應用[J];電測與儀表;2015年16期

7 忽曉偉;陳景召;郝江濤;馬世偉;;基于神經網(wǎng)絡的變壓器故障診斷研究[J];數(shù)字技術與應用;2015年08期

8 謝榮斌;張霖;鄢小虎;楊俊;盧文華;;模擬退火優(yōu)化SVM參數(shù)的變壓器故障診斷[J];計算機測量與控制;2015年05期

9 史麗萍;余鵬璽;羅朋;徐天然;劉鵬;李佳佳;;模糊支持向量機在變壓器故障診斷中的應用[J];電測與儀表;2015年08期

10 畢建權;鹿鳴明;郭創(chuàng)新;王逸飛;劉瀟洋;;一種基于多分類概率輸出的變壓器故障診斷方法[J];電力系統(tǒng)自動化;2015年05期

，

本文編號：1691229