本文關(guān)鍵詞： 輸油管道泄漏檢測 混沌特性 關(guān)聯(lián)維 雙樹復(fù)小波變換 遞歸圖　出處：《燕山大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：近年來,油氣產(chǎn)品已經(jīng)對(duì)我們的生活越來越重要。隨著我國管道事業(yè)的發(fā)展,部分輸油管道使用的時(shí)間越來越長,施工過程的缺陷以及自然腐蝕等原因,造成輸油管道發(fā)生泄漏,特別是人為的破壞、盜油等行為更是給人民的安全、國家的財(cái)產(chǎn)、自然環(huán)境帶來了很大的威脅。因此當(dāng)輸油管道發(fā)生泄漏時(shí),能夠及時(shí)的發(fā)現(xiàn)泄漏并確定泄漏點(diǎn)位置變得越來越重要。論文闡述了輸油管道泄漏檢測與定位的研究現(xiàn)狀,分析了常用的輸油管道泄漏檢測與定位方法。針對(duì)單一參數(shù)研究輸油管道泄漏可能引起誤報(bào)警或是定位準(zhǔn)確率較差等缺點(diǎn),論文提出基于數(shù)據(jù)采集與監(jiān)控系統(tǒng)(SCADA),以流量及壓力雙參數(shù)為處理對(duì)象,引入混沌特性分析及遞歸圖方法對(duì)管道泄漏檢測與定位方法進(jìn)行分析,主要內(nèi)容包括:首先分析了輸油管道泄漏時(shí)流量及壓力的變化情況,提出以流量及壓力雙參數(shù)為處理對(duì)象,采用混沌特性分析及遞歸圖方法實(shí)現(xiàn)管道泄漏檢測與定位的方案。給出負(fù)壓波定位公式,并在實(shí)現(xiàn)定位過程中對(duì)負(fù)壓波波速進(jìn)行了改進(jìn)。然后以輸油管道進(jìn)站口流量信號(hào)為處理對(duì)象,基于混沌特性分析識(shí)別管道泄漏情況。首先對(duì)系統(tǒng)動(dòng)力學(xué)特性進(jìn)行相空間重構(gòu),利用微分熵法同時(shí)求取延遲時(shí)間和嵌入維數(shù),避免了分開求取的不一致性,然后采用Wolf算法求取Lyapunov指數(shù),確定系統(tǒng)的混沌特性,最后通過GP算法計(jì)算關(guān)聯(lián)維數(shù),分析系統(tǒng)的混沌特性。最后根據(jù)SCADA系統(tǒng)采集到的負(fù)壓波信號(hào),采用雙樹復(fù)小波變換對(duì)其進(jìn)行處理,提取特征信號(hào),判斷輸油管道是否發(fā)生泄漏,然后利用遞歸圖平均灰度法進(jìn)行特征點(diǎn)定位,確定特征時(shí)刻,根據(jù)另一端確定的特征時(shí)刻即可確定負(fù)壓波傳輸?shù)绞啄﹥啥说臅r(shí)間差,最后結(jié)合定位公式及負(fù)壓波波速確定泄漏點(diǎn)具體位置。
[Abstract]:In recent years, oil and gas products have become more and more important to our daily life. With the development of pipeline industry in China, some oil pipelines have been used longer and longer, the defects in construction process and natural corrosion and other reasons. Oil pipeline leakage, especially man-made damage, oil theft and other acts to the safety of the people, national property, natural environment has brought a great threat. It is becoming more and more important to find leakage and determine the location of leak point in time. This paper describes the current situation of oil pipeline leak detection and location. The common leak detection and location methods of oil pipeline are analyzed. According to the single parameter, the leakage of oil pipeline may cause false alarm or poor location accuracy and so on. In this paper, based on the data acquisition and monitoring system (SCADAA), the flow and pressure parameters are taken as the processing objects, and the chaos characteristic analysis and recursive graph method are introduced to analyze the pipeline leakage detection and location method. The main contents are as follows: firstly, the variation of flow rate and pressure during oil pipeline leakage is analyzed, and the double parameters of flow and pressure are proposed as the processing object. The scheme of pipeline leakage detection and location is realized by using chaos characteristic analysis and recursive diagram method. The negative pressure wave localization formula is given. The negative pressure wave velocity is improved in the process of positioning. Then the flow signal at the inlet of oil pipeline is taken as the processing object. Firstly, the phase space reconstruction of system dynamics is carried out, and the delay time and embedding dimension are obtained simultaneously by differential entropy method, which avoids the inconsistency of separation. Then the Wolf algorithm is used to calculate the Lyapunov exponent, and the chaos characteristic of the system is determined. Finally, the correlation dimension is calculated by GP algorithm. Finally, according to the negative pressure wave signal collected by the SCADA system, the double tree complex wavelet transform is used to process the chaotic characteristics of the system, to extract the characteristic signal, and to judge whether the oil pipeline leak or not. Then the recursive image average gray level method is used to locate the feature points, and the time difference between the two ends of the negative pressure wave transmission can be determined according to the characteristic time of the other end. Finally, combined with the localization formula and the wave velocity of negative pressure, the specific location of leak point is determined.
【學(xué)位授予單位】：燕山大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TE973.6

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