城市燃?xì)夤芫W(wǎng)事故工況模擬及調(diào)度分析
發(fā)布時間:2018-05-10 05:27
本文選題:燃?xì)夤芫W(wǎng) + 事故工況 ; 參考:《哈爾濱工業(yè)大學(xué)》2015年碩士論文
【摘要】:隨著我國天然氣工業(yè)的迅猛發(fā)展,城市燃?xì)庖蔡幱谘杆侔l(fā)展時期,燃?xì)夤芫W(wǎng)規(guī)模越來越大,作為城市重要的基礎(chǔ)設(shè)施,在生活和生產(chǎn)中發(fā)揮著不可或缺的作用。隨著經(jīng)濟(jì)的建設(shè)和社會的進(jìn)步,城市供氣安全問題越來越引起社會關(guān)注,然而頻發(fā)的管網(wǎng)事故,為城市供氣系統(tǒng)的安全性帶來了嚴(yán)峻的挑戰(zhàn)�,F(xiàn)有的城市燃?xì)庵械蛪汗芫W(wǎng),除氣源處外,其它位置的監(jiān)測點數(shù)量極為有限,如何掌握整個管網(wǎng)在事故發(fā)生后的水力工況,對事故的評估和管網(wǎng)的調(diào)度意義重大,也是本文要解決的問題。本文主要基于遺傳算法解決事故工況水力分析及氣源優(yōu)化調(diào)度問題。本文對燃?xì)夤芫W(wǎng)事故后的水力工況進(jìn)行模擬和評估�;凇皦毫︱�(qū)動型”水力模型模擬燃?xì)夤芫W(wǎng)的事故工況,將各節(jié)點實際供氣量與節(jié)點壓力相關(guān)聯(lián),建立節(jié)點實際供氣量的分段函數(shù),運(yùn)用遺傳算法求解管網(wǎng)事故工況水力計算模型,得到各節(jié)點流量和壓力分布情況,分析管網(wǎng)事故的影響范圍和程度,該方法有別于傳統(tǒng)管網(wǎng)水力計算中的事故工況分析。針對多氣源燃?xì)夤芫W(wǎng),建立管網(wǎng)在某個氣源事故下的失效程度評價模型,對氣源事故的影響進(jìn)行綜合評估。運(yùn)用管網(wǎng)事故工況水力分析結(jié)果指導(dǎo)管網(wǎng)調(diào)度問題。以提高氣源事故下管網(wǎng)總供氣量、提高重點用戶供氣保證系數(shù)和盡量降低氣源出口壓力為優(yōu)化目標(biāo),以管網(wǎng)水力計算基本方程組的要求、各氣源對總供氣量的限制和管網(wǎng)可承受最大壓力的限制為約束條件,以各氣源出口壓力為決策變量,建立了氣源事故下燃?xì)夤芫W(wǎng)的優(yōu)化調(diào)度模型,并采用改進(jìn)的非支配排序遺傳算法NSGA-Ⅱ?qū)δP瓦M(jìn)行求解,得到某個氣源事故下最優(yōu)的調(diào)度策略。本文編寫了城市燃?xì)夤芫W(wǎng)水力計算軟件,自動讀取Auto CAD管網(wǎng)拓?fù)湫畔?進(jìn)行正常工況的管網(wǎng)水力計算,分析事故發(fā)生后管網(wǎng)的水力工況,自動輸出管網(wǎng)水力計算簡圖、管網(wǎng)節(jié)點壓力分布圖,能夠進(jìn)行工程量統(tǒng)計、結(jié)果的報表輸出和圖像輸出,實現(xiàn)了管網(wǎng)水力工況的可視化功能,經(jīng)對比分析,精度達(dá)到要求。
[Abstract]:With the rapid development of natural gas industry in China, urban gas is also in a period of rapid development. The scale of gas pipeline network is becoming larger and larger. As an important infrastructure of the city, it plays an indispensable role in life and production. With the development of economy and society, more and more attention has been paid to the safety of urban gas supply. However, the frequent network accidents have brought severe challenges to the safety of urban gas supply system. The number of monitoring points in other places is very limited except for the gas source. How to master the hydraulic condition of the whole pipeline network after the accident is of great significance to the assessment of the accident and the regulation of the pipe network. It is also the problem to be solved in this paper. In this paper, the genetic algorithm is used to solve the problem of hydraulic analysis and gas source optimization. This paper simulates and evaluates the hydraulic condition of gas pipe network after accident. Based on the "pressure-driven" hydraulic model to simulate the accident condition of the gas pipe network, the actual gas supply volume of each node is correlated with the node pressure, and the piecewise function of the actual gas supply rate of the node is established. Using genetic algorithm to solve the hydraulic calculation model of pipe network accident condition, the distribution of flow and pressure at each node is obtained, and the influence range and degree of pipe network accident are analyzed. This method is different from the accident condition analysis in traditional hydraulic calculation of pipe network. A model for evaluating the failure degree of multi-source gas network under a gas source accident is established, and the influence of gas source accident is evaluated synthetically. The problem of pipe network scheduling is guided by the result of hydraulic analysis on the accident condition of pipe network. In order to improve the total gas supply of the pipeline network under the gas source accident, to increase the gas supply guarantee coefficient of the key users and to reduce the outlet pressure of the gas source as far as possible, the requirements of the basic equations for hydraulic calculation of the pipe network are taken as the optimization goal. The limitation of the total gas supply and the maximum pressure of the pipe network are the constraint conditions. Taking the outlet pressure of each gas source as the decision variable, the optimal dispatching model of the gas pipeline network under the gas source accident is established. An improved non-dominated sorting genetic algorithm NSGA- 鈪,
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