本文選題：變壓器　切入點(diǎn)：參數(shù)尋優(yōu)　出處：《安徽理工大學(xué)》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(guó)電網(wǎng)的互聯(lián)已大致形成,對(duì)于電力系統(tǒng)安全穩(wěn)定運(yùn)行的要求越來越嚴(yán)格。變壓器作為電能傳送過程當(dāng)中的核心裝備之一,對(duì)于整個(gè)電力系統(tǒng)而言,起到了電能輸送以及電壓等級(jí)變換的作用。然而,其也是導(dǎo)致電力系統(tǒng)網(wǎng)絡(luò)發(fā)生故障的電氣裝備之一。能否準(zhǔn)確地預(yù)判出變壓器的潛伏性故障類型,對(duì)于變壓器的安全穩(wěn)定運(yùn)行是至關(guān)重要的。因此,針對(duì)于變壓器的運(yùn)行狀態(tài)進(jìn)行監(jiān)控是必要的。本文將圍繞變壓器故障診斷方面的內(nèi)容進(jìn)行相關(guān)闡述。就變壓器的診斷方式而言,當(dāng)其是建立在分析變壓器油中溶解氣體的故障診斷方法時(shí),相對(duì)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(nèi)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(zhǔn)確率等方面存在缺陷,進(jìn)而提出了結(jié)合支持向量機(jī)(SVM)算法的模型診斷方式。不過,僅靠單一的診斷方式進(jìn)行故障的判定,其效果必然是不盡人意的。為了進(jìn)一步提高診斷的準(zhǔn)確率以及收斂速度,提出了交叉驗(yàn)證的方式以及粒子群算法對(duì)支持向量機(jī)的核函數(shù)進(jìn)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(duì)變壓器的故障類型進(jìn)行相應(yīng)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(yàn)證的SVM分類結(jié)果是84%,粒子群算法優(yōu)化后得到的SVM分類結(jié)果是85.3333%。從而驗(yàn)證了優(yōu)化核函數(shù)的參數(shù)c/g的可行性,進(jìn)而驗(yàn)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(xué)位授予單位】：安徽理工大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM407

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本文編號(hào)：1691229