本文選題：中低壓燃?xì)庹{(diào)壓器 + 安全預(yù)警��； 參考：《北京建筑大學(xué)》2017年碩士論文

【摘要】：中低壓燃?xì)庹{(diào)壓器(以下簡稱調(diào)壓器)安全預(yù)警技術(shù)可預(yù)先診斷部件完成其功能的健康狀況,對出現(xiàn)故障征兆部位進(jìn)行提前預(yù)警、維修,避免零部件損壞。合理的安全預(yù)警系統(tǒng)以及性能質(zhì)量良好的調(diào)壓器,是保障調(diào)壓器安全正常運(yùn)行的關(guān)鍵,對整個(gè)城市燃?xì)庀到y(tǒng)的安全運(yùn)行和燃?xì)庥脩舻纳?cái)產(chǎn)安全具有重要意義。本文通過安全預(yù)警系統(tǒng)應(yīng)用測評以及數(shù)據(jù)案例分析,得出現(xiàn)有安全預(yù)警系統(tǒng)存在預(yù)測準(zhǔn)確率不高,未實(shí)現(xiàn)智能預(yù)警。為了進(jìn)一步優(yōu)化安全預(yù)警系統(tǒng),首先調(diào)取了關(guān)于調(diào)壓器運(yùn)行數(shù)據(jù)的表紙,并通過問卷調(diào)查的形式獲取專家的判定結(jié)果,同時(shí)調(diào)取了SCADA系統(tǒng)中相應(yīng)的能量矩值,組成樣本數(shù)據(jù)。其次,經(jīng)比較分析機(jī)器學(xué)習(xí)的幾個(gè)常用算法,確定了支持向量機(jī)(SVM)算法的適用性。共建立了三個(gè)支持向量機(jī)安全預(yù)警模型,采用網(wǎng)格搜索與10折交叉驗(yàn)證相結(jié)合的方法進(jìn)行模型參數(shù)優(yōu)化,通過對調(diào)壓器樣本數(shù)據(jù)的訓(xùn)練、驗(yàn)證,得到預(yù)測結(jié)果準(zhǔn)確率最高的模型為最優(yōu)模型,該模型預(yù)測結(jié)果準(zhǔn)確率為76%,具有良好的泛化能力,可用于安全預(yù)警技術(shù)的優(yōu)化。最后,參考GB27790-2011《城鎮(zhèn)燃?xì)庹{(diào)壓器》規(guī)范要求,進(jìn)行靜特性檢測與分析,通過對比全新調(diào)壓器與調(diào)修后調(diào)壓器的各性能參數(shù),得出調(diào)壓器投入使用前調(diào)修的必要性。并基于現(xiàn)有離線和在線安全預(yù)警系統(tǒng)的設(shè)計(jì),提出了Matlab與C#語言混合編程的優(yōu)化方法,對離線安全預(yù)警系統(tǒng)的界面和程序進(jìn)行了優(yōu)化,提高了系統(tǒng)的應(yīng)用推廣性。
[Abstract]:The safety warning technology of medium and low pressure gas regulator (hereinafter referred to as regulator) can pre-diagnose the health condition of the components to complete its function, and carry out early warning and maintenance of the parts with fault symptoms, so as to avoid the damage of parts and components.A reasonable safety warning system and a good performance regulator are the key to ensure the safe and normal operation of the regulator. It is of great significance to the safe operation of the whole city gas system and the safety of the life and property of the gas users.Through the application evaluation of the security early warning system and the data case analysis, it is concluded that the prediction accuracy of the existing security early warning system is not high, and the intelligent early warning is not realized.In order to further optimize the safety early warning system, the paper is first collected about the operation data of the regulator, and the expert's judgment result is obtained by questionnaire survey. At the same time, the corresponding energy moment value in the SCADA system is obtained to form the sample data.Secondly, by comparing and analyzing several common algorithms of machine learning, the applicability of support vector machine (SVM) algorithm is determined.Three support vector machine (SVM) security early warning models are established. The model parameters are optimized by combining grid search with 10 fold cross validation. The training of voltage regulator sample data is used to verify the model parameters.The model with the highest prediction accuracy is the optimal model. The prediction accuracy of the model is 76. It has a good generalization ability and can be used to optimize the security early warning technology.Finally, referring to the specification of GB27790-2011, the static characteristics of the regulator are tested and analyzed. By comparing the performance parameters of the new regulator and the adjusted and repaired regulator, the necessity of adjusting and repairing the regulator before it is put into use is obtained.Based on the design of off-line and on-line security early warning system, a hybrid programming method of Matlab and C # is proposed. The interface and program of off-line security early warning system are optimized, and the application generalization of the system is improved.
【學(xué)位授予單位】：北京建筑大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2017
【分類號】：TU996

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 HAO Xuejun;LIU Qiang;YANG Guobin;DU Yi;;Using the EMD Method to Determine Fault Criterion for Medium-low Pressure Gas Regulators[J];Journal of Thermal Science;2015年06期

2 焦衛(wèi)東;林樹森;;整體改進(jìn)的基于支持向量機(jī)的故障診斷方法[J];儀器儀表學(xué)報(bào);2015年08期

3 岳明;張乃方;陳浩;李軍;;城鎮(zhèn)燃?xì)庹{(diào)壓器靜特性的檢驗(yàn)及分析[J];煤氣與熱力;2015年01期

4 蔡鑫;南新元;孔軍;;DIWO-SVM在除塵風(fēng)機(jī)故障診斷中的應(yīng)用[J];自動化儀表;2014年08期

5 劉希亮;趙學(xué)勝;陸鋒;孫文彬;;基于GA-SVM的露天礦拋擲爆破拋擲率預(yù)測[J];煤炭學(xué)報(bào);2012年12期

6 朱永利;尹金良;;組合核相關(guān)向量機(jī)在電力變壓器故障診斷中的應(yīng)用研究[J];中國電機(jī)工程學(xué)報(bào);2013年22期

7 張乃方;翟軍;岳明;陳浩;李軍;王洪林;皮洋;;信號管設(shè)置對直接作用調(diào)壓器靜態(tài)特性的影響[J];煤氣與熱力;2013年03期

8 萬鵬;王紅軍;徐小力;;局部切空間排列和支持向量機(jī)的故障診斷模型[J];儀器儀表學(xué)報(bào);2012年12期

9 尹金良;朱永利;;支持向量機(jī)參數(shù)優(yōu)化及其在變壓器故障診斷中的應(yīng)用[J];電測與儀表;2012年05期

10 陳華;章兢;張小剛;胡義函;;一種基于Parzen窗估計(jì)的魯棒ELM燒結(jié)溫度檢測方法[J];自動化學(xué)報(bào);2012年05期

相關(guān)碩士學(xué)位論文 前5條

1 楊國斌;中低壓燃?xì)庹{(diào)壓器故障診斷與安全預(yù)警系統(tǒng)構(gòu)建[D];北京建筑大學(xué);2014年

2 王帆;電動燃?xì)庹{(diào)壓器氣壓檢測的信息融合研究[D];重慶大學(xué);2014年

3 曹玲;基于BP神經(jīng)網(wǎng)絡(luò)的保險(xiǎn)業(yè)產(chǎn)業(yè)安全預(yù)警實(shí)證研究[D];北京交通大學(xué);2011年

4 劉天舒;BP神經(jīng)網(wǎng)絡(luò)的改進(jìn)研究及應(yīng)用[D];東北農(nóng)業(yè)大學(xué);2011年

5 胡婧;神經(jīng)網(wǎng)絡(luò)算法在赤潮災(zāi)害智能預(yù)警系統(tǒng)中的應(yīng)用[D];山東大學(xué);2009年

，

本文編號：1732877