【摘要】：隨著我國經(jīng)濟(jì)的快速發(fā)展,各行各業(yè)對(duì)電力的需求不斷增加,電力系統(tǒng)的穩(wěn)定運(yùn)行已成為關(guān)系國計(jì)民生的大事。但由于電力系統(tǒng)規(guī)模龐大,結(jié)構(gòu)復(fù)雜,且長時(shí)間暴露于惡劣的自然條件下,故障的發(fā)生難以避免。同時(shí),地鐵牽引供電系統(tǒng)作為地鐵的能源系統(tǒng),其安全可靠運(yùn)行對(duì)地鐵穩(wěn)定運(yùn)行有著重要意義。但近年來,由于地鐵牽引供電系統(tǒng)故障而造成列車停運(yùn)、晚點(diǎn)的事故時(shí)有發(fā)生。因此,當(dāng)故障發(fā)生時(shí),需要迅速診斷并隔離故障。但在實(shí)際運(yùn)行中,傳統(tǒng)故障診斷方法并沒有較好解決電力系統(tǒng)故障診斷問題,錯(cuò)判、誤判仍時(shí)有發(fā)生。因此,一方面是對(duì)原有故障診斷方法進(jìn)行改進(jìn),另一方面是探索新的故障診斷方法。脈沖神經(jīng)膜系統(tǒng)是一種新穎的具有分布式并行計(jì)算能力的計(jì)算模型,且具有較好的動(dòng)態(tài)特性�；诖�,許多研究將其應(yīng)用于解決實(shí)際問題。本文則將脈沖神經(jīng)膜系統(tǒng)應(yīng)用于電力系統(tǒng)故障診斷與地鐵牽引供電系統(tǒng)故障診斷問題中,主要包括以下3點(diǎn)工作:(1)給出基于波形相似度的輸電線路故障可信度。利用小波變換理論分析了輸電線路出現(xiàn)故障時(shí),其波形信號(hào)出現(xiàn)的幅值與諧波變化,并采用波形相關(guān)系數(shù)來反映線路故障時(shí)其幅值與諧波的變化程度。同時(shí),為了驗(yàn)證波形相似度故障可信度能否有效準(zhǔn)確反映輸電線路故障,利用PSCAD建立模型,仿真180種不同的故障情況,驗(yàn)證其有效性;(2)將脈沖神經(jīng)膜系統(tǒng)應(yīng)用于故障選相中。建立基于脈沖神經(jīng)膜系統(tǒng)的故障選相模型,引入6種故障選相特征值,并分別給出其計(jì)算方法。給出基于故障選相模型的故障選相推理算法,實(shí)現(xiàn)故障選相。利用PSCAD建立模型,仿真450種不同的故障類型,驗(yàn)證所提選相方法有效性;(3)將脈沖神經(jīng)膜系統(tǒng)應(yīng)用于地鐵牽引供電系統(tǒng)故障診斷中。給出基于網(wǎng)絡(luò)拓?fù)浞治龇ǖ墓收蠀^(qū)域確定方法,確定疑似故障元件。對(duì)疑似故障元件分別建立基于脈沖神經(jīng)膜系統(tǒng)的故障診斷模型,這些故障診斷模型分別運(yùn)行計(jì)算得到各個(gè)疑似故障元件的故障可信度,從而確定故障元件。
[Abstract]:With the rapid development of economy in our country, the demand for electricity in various industries is increasing, and the stable operation of power system has become a major event related to the national economy and the people's livelihood. However, due to the large scale of power system, complex structure and long time exposure to harsh natural conditions, it is difficult to avoid the fault. At the same time, as the energy system of subway, its safe and reliable operation is of great significance to the steady operation of subway. But in recent years, due to the subway traction power supply system failure caused by train shutdown, late accidents have occurred from time to time. Therefore, when the fault occurs, it is necessary to diagnose and isolate the fault quickly. However, in practice, the traditional fault diagnosis method has not solved the problem of power system fault diagnosis. Misjudgment and misjudgment still occur from time to time. Therefore, on the one hand, the original fault diagnosis method is improved, on the other hand, a new fault diagnosis method is explored. Pulse neural membrane system is a novel computing model with distributed parallel computing capability and has good dynamic characteristics. Based on this, many researches apply it to solve practical problems. In this paper, the pulse neural membrane system is applied to power system fault diagnosis and subway traction power supply system fault diagnosis, mainly including the following three points: (1) the reliability of transmission line fault based on waveform similarity is given. The wavelet transform theory is used to analyze the amplitude and harmonic variation of the waveform signal when the transmission line fails, and the waveform correlation coefficient is used to reflect the amplitude and harmonic variation degree of the transmission line fault. At the same time, in order to verify whether the fault credibility of waveform similarity can reflect the transmission line fault effectively and accurately, a model is established by using PSCAD to simulate 180 different fault cases, and the validity of the model is verified. (2) the pulse nerve membrane system is applied to fault phase selection. A fault phase selection model based on pulsed neural membrane system is established. Six eigenvalues of fault phase selection are introduced and their calculation methods are given. A fault phase selection reasoning algorithm based on fault phase selection model is presented to realize fault phase selection. The PSCAD model is used to simulate 450 different fault types to verify the effectiveness of the proposed phase selection method. (3) the pulse neural membrane system is applied to the fault diagnosis of metro traction power supply system. A method of fault area determination based on network topology analysis is presented to determine the suspected fault elements. The fault diagnosis models based on the pulse neural membrane system are established for the suspected fault elements. The fault reliability of each suspected fault element is calculated by running these fault diagnosis models, and the fault components are determined.
【學(xué)位授予單位】：西南交通大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：TM711

【相似文獻(xiàn)】

相關(guān)期刊論文 前10條

1 周偉;引用半結(jié)構(gòu)化故障診斷模型 推進(jìn)故障診斷模型探索工作[J];內(nèi)燃機(jī)車;2004年08期

2 袁海斌;袁海文;李行善;;層次故障診斷模型的模糊Petri網(wǎng)推理[J];儀器儀表學(xué)報(bào);2006年S2期

3 胡家元;曹順安;盛凱;王露;;基于可信度理論的火電廠水汽化學(xué)過程故障診斷模型研究[J];工業(yè)儀表與自動(dòng)化裝置;2009年03期

4 張巖;張勇;文福拴;李繼紅;;容納時(shí)序約束的改進(jìn)模糊Petri網(wǎng)故障診斷模型[J];電力系統(tǒng)自動(dòng)化;2014年05期

5 李大琪，鄒逢興，強(qiáng)靜;一個(gè)面向?qū)ο蟮南到y(tǒng)故障診斷模型[J];國防科技大學(xué)學(xué)報(bào);1996年04期

6 韓爽,戈志華,丁常富;汽輪機(jī)組振動(dòng)故障診斷模型[J];華北電力大學(xué)學(xué)報(bào);2002年01期

7 曾慶鋒;何正友;楊健維;;基于有色Petri網(wǎng)的電力系統(tǒng)故障診斷模型研究[J];電力系統(tǒng)保護(hù)與控制;2010年14期

8 李海潮;陳強(qiáng);;基于克隆選擇算法的故障診斷模型及應(yīng)用研究[J];礦山機(jī)械;2007年06期

9 許宇勝,蔣偉進(jìn);基于MAS的故障診斷模型與實(shí)現(xiàn)研究[J];振動(dòng)與沖擊;2004年04期

10 柳新民,邱靜,劉冠軍;基于AR-連續(xù)HMM的故障診斷模型及應(yīng)用[J];機(jī)械科學(xué)與技術(shù);2005年03期

相關(guān)會(huì)議論文 前6條

1 肖小鋒;蔡金燕;馬颯颯;;基于知識(shí)的故障診斷模型研究[A];2004全國測(cè)控、計(jì)量與儀器儀表學(xué)術(shù)年會(huì)論文集（下冊(cè)）[C];2004年

2 錢政;嚴(yán)璋;;范例推理綜合故障診斷模型的建立及其應(yīng)用[A];第二屆全國信息獲取與處理學(xué)術(shù)會(huì)議論文集[C];2004年

3 陶建武;高玉寶;常文秀;;基于知識(shí)的故障診斷模型的建立及應(yīng)用[A];第十屆全國信號(hào)處理學(xué)術(shù)年會(huì)（CCSP-2001）論文集[C];2001年

4 劉思華;何峰;;基于多AGENT電網(wǎng)故障診斷模型研究[A];山東電機(jī)工程學(xué)會(huì)2012年度學(xué)術(shù)年會(huì)論文集[C];2012年

5 王太勇;李書明;張豐春;李韋;曾子平;;基于函數(shù)型連接網(wǎng)絡(luò)的故障診斷模型[A];材料科學(xué)與工程技術(shù)——中國科協(xié)第三屆青年學(xué)術(shù)年會(huì)論文集[C];1998年

6 何佳洲;周志華;陳兆乾;;基于IHMCAP算法的一個(gè)故障診斷模型[A];1998年中國智能自動(dòng)化學(xué)術(shù)會(huì)議論文集（下冊(cè)）[C];1998年

，

本文編號(hào)：2297960