發(fā)布時(shí)間：2018-03-09 21:54

  本文選題：熱網(wǎng)　切入點(diǎn)：Flowmaster　出處：《內(nèi)蒙古科技大學(xué)》2014年碩士論文　論文類型：學(xué)位論文

【摘要】：當(dāng)代城市的快速發(fā)展，使集中供暖系統(tǒng)成為我國北方城市重點(diǎn)建設(shè)的系統(tǒng)。因?yàn)楣┡到y(tǒng)具有非線性、多變量、大滯后的特點(diǎn)。并且熱負(fù)荷有時(shí)變性與不確定性的特點(diǎn)，所以供暖系統(tǒng)是一個(gè)不容易控制的系統(tǒng)。怎樣節(jié)約能源，降低損耗的同時(shí)對熱網(wǎng)系統(tǒng)進(jìn)行有效的控制，，是供熱企業(yè)面臨的一個(gè)重要問題。 論文首先從供熱管網(wǎng)模型著手，由于以往根據(jù)熱網(wǎng)機(jī)理建立的熱網(wǎng)模型，為了簡化傳遞函數(shù)和減少熱網(wǎng)的計(jì)算量忽略了一些熱網(wǎng)的相關(guān)參數(shù)，這樣建立的模型和真實(shí)的熱網(wǎng)系統(tǒng)存在一些差距，同時(shí)搭建的模型通用性不強(qiáng)，只能針對特定的熱網(wǎng)系統(tǒng)。本論文使用一維流體模擬軟件Flowmaster搭建可視化的集中供暖系統(tǒng)仿真模型，根據(jù)實(shí)際的集中供暖系統(tǒng)搭建具有熱源、熱力站、熱用戶三大部分的模型，其中熱力站的數(shù)目是十四個(gè)，是一個(gè)比較大型的集中供暖系統(tǒng)。搭建的模型能夠在多種模擬環(huán)境下進(jìn)行仿真，其需要設(shè)置的參數(shù)種類多，能夠比較貼近真實(shí)的系統(tǒng)。由于集中供熱系統(tǒng)的熱負(fù)荷和大氣溫度的不確定性，需要建立短期預(yù)測模型，作為熱量分配的約束條件。本文選用基于神經(jīng)網(wǎng)絡(luò)的預(yù)測模型對各個(gè)熱力站的熱負(fù)荷滾動(dòng)預(yù)測，該方法使用動(dòng)態(tài)K均值聚類算法與遞歸最小二乘法（ROLS）改良RBF神經(jīng)網(wǎng)絡(luò)，用逐漸刷新歷史數(shù)據(jù)的辦法實(shí)現(xiàn)了對熱力站短期熱負(fù)荷預(yù)測。針對一次網(wǎng)的優(yōu)化控制問題，設(shè)計(jì)一個(gè)熱源生產(chǎn)最少熱量的目標(biāo)函數(shù)。采用粒子群優(yōu)化（ParticleSwarmOptimization，PSO）算法，對目標(biāo)函數(shù)尋優(yōu)，分別找到各時(shí)刻總供熱量然后按照各個(gè)熱力站預(yù)測的熱負(fù)荷變化趨勢給模型分配熱量。通過PSO的尋優(yōu)計(jì)算，可以使供暖系統(tǒng)提供最少的熱量滿足最多的用戶，達(dá)到節(jié)約能源的目的。 論文通過仿真實(shí)驗(yàn)的方法驗(yàn)證了RBF神經(jīng)網(wǎng)絡(luò)對各個(gè)熱力站熱負(fù)荷預(yù)測值作為分配總熱量的依據(jù)，然后使用PSO優(yōu)化算法和Flowmaster搭建的集中供熱系統(tǒng)仿真模型進(jìn)行聯(lián)合仿真。通過對比仿真后的模型數(shù)據(jù)曲線可以發(fā)現(xiàn)有控制算法的模型能夠按照熱用戶的熱量需求變化趨勢進(jìn)行供熱，對實(shí)際系統(tǒng)節(jié)能降耗有一定的指導(dǎo)意義。
[Abstract]:With the rapid development of modern cities, central heating system has become the key construction system in northern cities of China. Because the heating system has the characteristics of nonlinear, multi-variable, large lag, and the heat load is sometimes variable and uncertain, Therefore, heating system is not easy to control, how to save energy, reduce losses while effectively control the heating network system, is an important problem facing heating enterprises. Firstly, the paper starts with the heat supply network model. Because of the previous heat network model established according to the heat network mechanism, in order to simplify the transfer function and reduce the calculation amount of the heat network, some related parameters of the heat network are ignored. There are some gaps between the established model and the real heat network system, and the model built at the same time is not universal. This paper uses one-dimensional fluid simulation software Flowmaster to build a visual central heating system simulation model. According to the actual central heating system, the model has three parts: heat source, thermal station and heat user. The number of thermal stations is 14, which is a relatively large central heating system. The built model can be simulated in a variety of simulation environments, and there are many kinds of parameters that need to be set up. Because of the heat load of central heating system and the uncertainty of atmospheric temperature, it is necessary to establish short-term forecasting model. As the constraint condition of heat distribution, this paper selects the neural network-based prediction model to predict the thermal load rolling of each thermal station. The dynamic K-means clustering algorithm and the recursive least square method are used to improve the RBF neural network. The short-term heat load forecasting of thermal station is realized by gradually refreshing the historical data. In view of the optimization control problem of primary network, an objective function for heat source production is designed. Particle swarm optimization (PSO) algorithm is used to optimize the objective function. Find out the total heat supply at each time and assign the heat to the model according to the change trend of heat load predicted by each thermal station. Through the optimization calculation of PSO, the heating system can provide the least heat to satisfy the most users. To save energy. The RBF neural network is used to predict the thermal load of each thermal station as the basis of the total heat distribution. Then the simulation model of central heating system built by PSO optimization algorithm and Flowmaster is combined. By comparing the model data curve after simulation, we can find that the model with control algorithm can change according to the heat demand of heat users. The trend of heating, It has certain guiding significance to the actual system energy saving and consumption reduction.
【學(xué)位授予單位】：內(nèi)蒙古科技大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2014
【分類號】：TU833;TP13

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