【摘要】：小電流接地系統(tǒng)是指中低壓配電網(wǎng)絡中，，采用中性點經(jīng)消弧線圈接地、經(jīng)大電阻接地或者中性點不接地等運行方式的系統(tǒng)。通常情況下，小電流系統(tǒng)發(fā)生故障后，故障電流十分微弱，不容易被檢測到，這對故障選線問題提出了巨大挑戰(zhàn)。目前小電流接地故障選線方法主要分為三類：一是利用故障穩(wěn)態(tài)特征分量進行選線，二是利用故障暫態(tài)特征分量進行選線，三是注入特殊信號進行選線。這些方法在實際應用中均取得了一些成效，但從選線的準確率和穩(wěn)定性上來說還遠遠不夠理想。 概率神經(jīng)網(wǎng)絡是一種可用于模式分類的神經(jīng)網(wǎng)絡，在機械、材料、環(huán)境工程乃至經(jīng)濟領(lǐng)域有著較好應用，而在配電網(wǎng)故障選線上很少有人嘗試。經(jīng)過反復研究和探索，尋找到配電線路零序電流小波能量、有功分量和五次諧波分量三種故障特征量作為選線的依據(jù)。對故障模式進行了合理定義，重點突破了概率神經(jīng)網(wǎng)絡中多種故障特征量有效融合的問題，提出一種基于概率神經(jīng)網(wǎng)絡的模式識別選線方法。 通過對小電流接地系統(tǒng)模型進行大量Matlab仿真試驗，研究了故障位置、接地電阻、故障合閘角、中性點接地方式、配電線路結(jié)構(gòu)和噪聲干擾對故障特征量的影響，收集了大量故障數(shù)據(jù)樣本。同時對電弧高阻接地、混合線纜配電網(wǎng)、噪聲干擾下的接地故障以及中性點不同接地方式系統(tǒng)的概率神經(jīng)網(wǎng)絡模式識別選線進行了廣泛試驗，驗證了該方法具有較好的通用性和抗干擾能力。將該方法與概率神經(jīng)網(wǎng)絡單一特征量選線和BP神經(jīng)網(wǎng)絡故障選線方法進行了比較，證明了該方法具有準確率高，操作簡單快速，故障知識豐富，易于拓展知識庫等特點。最后提出了一套本方法的故障選線裝置設(shè)計方案。
[Abstract]:Low current grounding system is a system in which neutral point is grounded by arc-suppression coil grounding through large resistance or neutral point is not grounded in medium and low voltage distribution network. Usually, the fault current is very weak after the failure of the small current system, and it is not easy to detect, which poses a great challenge to the fault line selection problem. At present, there are three kinds of fault line selection methods for low current grounding fault: one is to select the line by using the steady-state characteristic component of the fault, the other is to select the line by using the transient characteristic component of the fault, and the third is to inject special signals to select the line. These methods have achieved some results in practical application, but they are far from ideal in terms of accuracy and stability of line selection. Probabilistic neural network (PNN) is a kind of neural network which can be used for pattern classification. It has good applications in mechanical, material, environmental engineering and even economic fields, but it is seldom tried in fault line selection of distribution network. Through repeated research and exploration, three fault characteristic quantities of zero-sequence current wavelet energy, active power component and fifth harmonic component of distribution line are found as the basis of line selection. In this paper, the fault mode is defined reasonably, and the problem of effective fusion of multiple fault features in probabilistic neural network is broken through, and a method of pattern recognition and line selection based on probabilistic neural network is proposed. Through a large number of Matlab simulation tests on the small current grounding system model, the effects of fault location, grounding resistance, fault closing angle, neutral grounding mode, distribution line structure and noise interference on the fault characteristic quantity are studied. A large number of fault data samples were collected. At the same time, a wide range of experiments have been carried out on arc high resistance grounding, hybrid cable distribution network, grounding fault under noise interference and probabilistic neural network pattern recognition of different neutral grounding mode systems. It is proved that this method has good generality and anti-interference ability. The method is compared with the probabilistic neural network single feature selection method and the BP neural network fault line selection method. It is proved that this method has the advantages of high accuracy, simple and fast operation, rich fault knowledge and easy to expand the knowledge base. Finally, a design scheme of fault line selection device based on this method is put forward.
【學位授予單位】：南昌大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TM862

【參考文獻】

相關(guān)期刊論文 前10條

1 邵玉槐;謝朋海;張德志;;基于TMS320F2812 DSP的電能質(zhì)量監(jiān)測裝置的設(shè)計[J];電測與儀表;2009年02期

2 張艷霞;王清亮;;應用故障暫態(tài)特性實現(xiàn)配電網(wǎng)故障選線的新方法[J];電力系統(tǒng)自動化;2009年16期

3 陳慧玲;王金全;陳曉寧;;基于DSP和ARM9結(jié)構(gòu)的廠用電微機快切裝置[J];電力自動化設(shè)備;2009年12期

4 毛鵬,段玉倩,姜娜;基于相關(guān)分析的故障選線方法[J];電網(wǎng)技術(shù);2004年02期

5 李斌;束洪春;;諧振接地系統(tǒng)單相接地故障瞬時功率分析[J];電網(wǎng)技術(shù);2012年07期

6 黎群輝;張航;;基于改進概率神經(jīng)網(wǎng)絡的交通標志圖像識別方法[J];系統(tǒng)工程;2006年04期

7 周志成;何俊佳;徒有鋒;尹小根;;基于信息融合技術(shù)的NUGS綜合故障選線方法[J];高電壓技術(shù);2009年04期

8 陳龍;倪道宏;章國寶;;中高壓斷路器在線監(jiān)測裝置的設(shè)計與實現(xiàn)[J];電子設(shè)計工程;2012年16期

9 徐軍;呂琨璐;蘇全志;尹佳;陳世舉;;DSP在電源設(shè)計中的應用[J];電測與儀表;2013年01期

10 吳樂鵬;黃純;林達斌;朱智軍;蔣洪濤;;基于暫態(tài)小波能量的小電流接地故障選線新方法[J];電力自動化設(shè)備;2013年05期

相關(guān)博士學位論文 前3條

1 李德臣;基于可拓理論的小電流故障選線方法研究[D];中國礦業(yè)大學;2010年

2 王念彬;基于小波神經(jīng)網(wǎng)絡的小電流接地系統(tǒng)故障選線方法的研究[D];中國礦業(yè)大學（北京）;2010年

3 張志剛;基于小波變換和神經(jīng)網(wǎng)絡的小電流接地故障檢測方法研究[D];哈爾濱工業(yè)大學;2010年

本文編號：2368796