發(fā)布時(shí)間：2018-01-27 22:47

  本文關(guān)鍵詞： 電壓傳感器 波形分析 支持向量機(jī) 參數(shù)優(yōu)化 GUI　出處：《西華大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：如今我國(guó)經(jīng)濟(jì)社會(huì)正處于持續(xù)高速發(fā)展階段,電力需求長(zhǎng)期保持快速增長(zhǎng),電力系統(tǒng)規(guī)模日益擴(kuò)大。相應(yīng)的電網(wǎng)運(yùn)行方式及系統(tǒng)控制的復(fù)雜程度愈來(lái)愈高,如何降低事故風(fēng)險(xiǎn)保持電能的安全可靠輸送成為了目前建設(shè)堅(jiān)強(qiáng)智能電網(wǎng)的一項(xiàng)重大挑戰(zhàn)。研究表明,各種設(shè)備的絕緣水平及運(yùn)行方式是影響電力系統(tǒng)可靠性的重要因素,而造成系統(tǒng)中各種設(shè)備絕緣故障的主要原因則是內(nèi)部過電壓及外部過電壓。對(duì)變電站電壓信號(hào)進(jìn)行實(shí)時(shí)監(jiān)測(cè)及故障信號(hào)的處理分析有助于工作人員及時(shí)掌握系統(tǒng)運(yùn)行情況,查找故障原因,及時(shí)發(fā)現(xiàn)絕緣薄弱處以便提出應(yīng)對(duì)方案快速排除故障減少經(jīng)濟(jì)損失。并且通過過電壓數(shù)據(jù)對(duì)故障發(fā)生原因的分析也有助于為電網(wǎng)設(shè)計(jì)提供更確實(shí)可靠的科學(xué)依據(jù),為電網(wǎng)中各設(shè)備絕緣水平的確定及各類保護(hù)元件的合理選取配置提供幫助。本文首先介紹了一種氧化鋅避雷器電壓傳感器,分別在工作特性,安全防護(hù)等方面對(duì)其可行性進(jìn)行了分析。另外為了驗(yàn)證其可靠性及準(zhǔn)確性,本文又加以仿真計(jì)算、試驗(yàn)測(cè)試及現(xiàn)場(chǎng)對(duì)比測(cè)量等手段。通過最終結(jié)果分析,氧化鋅避雷器電壓傳感器在頻率響應(yīng)及線性度方面具有優(yōu)良表現(xiàn),可用其實(shí)現(xiàn)對(duì)暫態(tài)過電壓的采集監(jiān)測(cè),所獲信號(hào)數(shù)據(jù)準(zhǔn)確度高。為有針對(duì)的研究所采集的暫態(tài)過電壓數(shù)據(jù),本文對(duì)各暫態(tài)過電壓產(chǎn)生機(jī)理進(jìn)行了詳細(xì)分析。利用ATPDraw電磁暫態(tài)仿真軟件對(duì)現(xiàn)場(chǎng)進(jìn)行建模,通過仿真波形與實(shí)際測(cè)量波形相結(jié)合的方式研究各過電壓特點(diǎn),并基于樹形結(jié)構(gòu)對(duì)暫態(tài)過電壓進(jìn)行了分層歸類,為智能識(shí)別系統(tǒng)的建立打下基礎(chǔ)。特征量的選取在識(shí)別系統(tǒng)中起著至關(guān)重要的作用,本文在時(shí)頻域分析的基礎(chǔ)上利用小波變換分析、奇異值理論等手段對(duì)過電壓數(shù)據(jù)進(jìn)行處理,使信號(hào)局部特性得以凸顯,提取了多種特征參量。根據(jù)各類暫態(tài)過電壓特點(diǎn),選取能表征相應(yīng)過電壓的特征參量建立了各層分類器。在暫態(tài)過電壓識(shí)別系統(tǒng)實(shí)現(xiàn)的過程中,本文將傳統(tǒng)二分類支持向量機(jī)理念與多分類支持向量機(jī)相結(jié)合采取了多層遞進(jìn)歸類方式開發(fā)程序。為進(jìn)一步提高識(shí)別系統(tǒng)的準(zhǔn)確率及性能,首先采用主成分分析及歸一化方法對(duì)數(shù)據(jù)進(jìn)行預(yù)處理,然后綜合使用交叉驗(yàn)證、遺傳算法、粒子群優(yōu)化算法三種手段對(duì)各層支持向量機(jī)參數(shù)進(jìn)行優(yōu)化,選取最優(yōu)方式確定支持向量機(jī)參數(shù)。最后本文制作了暫態(tài)過電壓識(shí)別系統(tǒng)圖形操作界面,進(jìn)一步提升了其交互性、可視性及操作性。
[Abstract]:Nowadays, the economy and society of our country are in the stage of sustained and high-speed development, the power demand has been growing rapidly for a long time, the scale of the power system is expanding day by day, and the complexity of the corresponding operation mode and system control of the power network is becoming more and more high. How to reduce the risk of accidents to maintain the safe and reliable transmission of electricity has become a major challenge in building a strong smart grid. The insulation level and operation mode of various equipments are important factors that affect the reliability of power system. The main causes of insulation failure of various equipments in the system are internal overvoltage and external overvoltage. The real-time monitoring of substation voltage signal and the processing and analysis of fault signal are helpful for the staff to master the system in time. Operational status. Find the cause of the failure. When the insulation weakness is discovered in time, the solution is put forward to eliminate the fault quickly and reduce the economic loss. The analysis of the cause of the fault through the overvoltage data is also helpful to provide a more reliable scientific basis for the power network design. . It can help to determine the insulation level of each equipment and the reasonable selection and configuration of various protective elements. Firstly, a voltage sensor of zinc oxide arrester is introduced in this paper. In addition, in order to verify its reliability and accuracy, this paper carries on the simulation calculation, the test test and the field contrast measurement and so on, through the final result analysis. The voltage sensor of zinc oxide arrester has excellent performance in frequency response and linearity, which can be used to collect and monitor transient overvoltage. The obtained signal data has high accuracy. It is the transient overvoltage data collected by the research institute. In this paper, the generation mechanism of transient overvoltage is analyzed in detail, and the field modeling is carried out by using ATPDraw electromagnetic transient simulation software. By combining the simulation waveform with the actual measurement waveform, the characteristics of each overvoltage are studied, and the transient overvoltage is classified in layers based on the tree structure. It lays the foundation for the establishment of intelligent recognition system. The selection of feature plays an important role in the recognition system. In this paper, wavelet transform is used on the basis of time-frequency domain analysis. Singular value theory and other means to process the over-voltage data, so that the local characteristics of the signal can be highlighted, extracted a variety of characteristic parameters, according to the characteristics of various transient overvoltage. Selecting the characteristic parameters which can represent the corresponding overvoltage, the classifier of each layer is established, and the realization of transient overvoltage identification system is carried out. In this paper, the traditional two-classification support vector machine and multi-classification support vector machine are combined to develop a multi-layer progressive classification program to further improve the accuracy and performance of the recognition system. First, the data are preprocessed by principal component analysis and normalization, then the parameters of support vector machine are optimized by three methods: crossover verification, genetic algorithm and particle swarm optimization. Finally, the graphical operation interface of transient overvoltage recognition system is made, which further improves its interaction, visibility and maneuverability.
【學(xué)位授予單位】：西華大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TM63

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相關(guān)期刊論文 前10條

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本文編號(hào)：1469084