本文選題：神經(jīng)網(wǎng)絡(luò) + BP算法��； 參考：《鄭州大學(xué)》2014年碩士論文

【摘要】：變壓器是電力系統(tǒng)核心的部件之一，，擔(dān)負(fù)著電壓變換的重要任務(wù)，它的正常工作是整個(gè)電力系統(tǒng)正常供電的必要條件。如果變壓器發(fā)生了故障，那么將對(duì)整個(gè)電力系統(tǒng)正常供電造成極大的破壞。因此，能夠快速、準(zhǔn)確的診斷出變壓器故障類型對(duì)變壓器故障的及時(shí)、迅速維修復(fù)原具有重要的意義。而神經(jīng)網(wǎng)絡(luò)尤其是BP神經(jīng)網(wǎng)絡(luò)具有結(jié)構(gòu)簡(jiǎn)單、并行性高、非線性關(guān)系建模能力強(qiáng)等特點(diǎn)，非常適合用來(lái)解決故障診斷這類多變量的、內(nèi)部關(guān)系復(fù)雜的問(wèn)題。所以，將神經(jīng)網(wǎng)絡(luò)應(yīng)用于變壓器的故障診斷是可行的。 許繼集團(tuán)是我國(guó)電力裝備研發(fā)和電力生產(chǎn)的大型骨干企業(yè)，一直都很重視變壓器故障診斷技術(shù)的發(fā)展和研究，近幾年與我校老師合作研發(fā)變壓器故障診斷系統(tǒng)�；谠擁�(xiàng)目的需求，本文做了兩方面工作：一是研究神經(jīng)網(wǎng)絡(luò)相關(guān)理論算法，并將神經(jīng)網(wǎng)絡(luò)理論應(yīng)用于變壓器故障診斷，這方面主要工作是掌握BP算法以及應(yīng)用BP神經(jīng)網(wǎng)絡(luò)進(jìn)行應(yīng)用建模；二是設(shè)計(jì)并在VC++6.0的平臺(tái)上實(shí)現(xiàn)了基于神經(jīng)網(wǎng)絡(luò)的變壓器故障診斷系統(tǒng)，該系統(tǒng)包含三個(gè)模塊：數(shù)據(jù)管理、網(wǎng)絡(luò)訓(xùn)練和故障診斷。數(shù)據(jù)管理模塊主要實(shí)現(xiàn)了故障樣本的預(yù)處理，網(wǎng)絡(luò)訓(xùn)練模塊主要實(shí)現(xiàn)了三種BP算法來(lái)訓(xùn)練網(wǎng)絡(luò)，故障診斷模塊主要實(shí)現(xiàn)對(duì)故障樣本的診斷。 在本文中，我們以來(lái)源于實(shí)際生產(chǎn)環(huán)境的60組變壓器故障數(shù)據(jù)為例展示了本系統(tǒng)的效果，使用L-M算法診斷故障準(zhǔn)確率最高可以達(dá)到91%。同時(shí)，本系統(tǒng)在許繼集團(tuán)的相關(guān)產(chǎn)品中得到了實(shí)際應(yīng)用，滿足了用戶的需求。
[Abstract]:The transformer is one of the core components of the power system, which takes on the important task of the voltage transformation. Its normal work is the necessary condition for the normal power supply of the whole power system. If the transformer fails, it will cause great damage to the normal power supply of the whole power system. Type is of great significance to the timely and rapid maintenance and restoration of transformer faults. The neural network, especially the BP neural network, has the characteristics of simple structure, high parallelism and strong nonlinear relation modeling ability. It is very suitable to solve the problem of the multivariable and complex internal relations of fault diagnosis. So, the neural network is applied to the neural network. The fault diagnosis of the transformer is feasible.
Xu Ji group is a large backbone enterprise of power equipment R & D and power production in China. It has always attached great importance to the development and research of transformer fault diagnosis technology. In recent years, the transformer fault diagnosis system has been developed in cooperation with our teachers. Based on the requirements of this project, this paper has done two aspects of work: one is to study the theory of neural network related theory. The neural network theory is applied to the transformer fault diagnosis. The main work is to master the BP algorithm and apply the BP neural network to model the application. Two is to design and implement the transformer fault diagnosis system based on the neural network on the VC++6.0 platform. The system contains three modules: data management, network training and the system. The data management module mainly realizes the preprocessing of the fault samples. The network training module mainly implements three kinds of BP algorithms to train the network, and the fault diagnosis module mainly realizes the diagnosis of the fault samples.
In this paper, 60 sets of transformer fault data from the actual production environment have been presented as an example to show the effect of this system. The L-M algorithm is used to diagnose the highest fault accuracy and can reach the highest level of 91%.. The system has been applied to the related products of Xu Ji group and meets the needs of the users.
【學(xué)位授予單位】：鄭州大學(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10條

1 楊海馬;劉瑾;張菁;;BP神經(jīng)網(wǎng)絡(luò)在變壓器故障診斷中的應(yīng)用[J];變壓器;2009年01期

2 李小軍;金正洪;師旭;師耀林;楊瑛;;變壓器類設(shè)備油氣分析、故障診斷技術(shù)發(fā)展與應(yīng)用實(shí)例[J];變壓器;2011年02期

3 胡導(dǎo)福;文閃閃;何益鳴;;基于BP神經(jīng)網(wǎng)絡(luò)的變壓器故障診斷及其應(yīng)用[J];電力科學(xué)與技術(shù)學(xué)報(bào);2008年02期

4 談克雄,朱德恒,李福祺,高文勝,高勝友;發(fā)電機(jī)變壓器放電故障診斷的基礎(chǔ)研究和應(yīng)用[J];電力系統(tǒng)自動(dòng)化;2004年15期

5 湯素麗;羅宇鋒;;人工神經(jīng)網(wǎng)絡(luò)技術(shù)的發(fā)展與應(yīng)用[J];電腦開(kāi)發(fā)與應(yīng)用;2009年10期

6 占才亮;;人工智能技術(shù)在電力系統(tǒng)故障診斷中的應(yīng)用[J];廣東電力;2011年09期

7 王曉強(qiáng);;電氣設(shè)備的預(yù)防性試驗(yàn)[J];廣東科技;2010年04期

8 索勝軍,孫光偉,王巖;幾種改進(jìn)BP算法的性能比較[J];哈爾濱建筑大學(xué)學(xué)報(bào);2000年01期

9 耿小慶;和金生;于寶琴;;幾種改進(jìn)BP算法及其在應(yīng)用中的比較分析[J];計(jì)算機(jī)工程與應(yīng)用;2007年33期

10 張鈴;張鈸;;神經(jīng)網(wǎng)絡(luò)中BP算法的分析[J];模式識(shí)別與人工智能;1994年03期

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