【作者】 雷翠红；

【导师】 邹平华；

【作者基本信息】 哈尔滨工业大学 ， 供热、供燃气、通风及空调工程， 2010， 博士

【摘要】 现代化大型供热系统是一个复杂的技术系统,其元部件数量巨大,工况复杂,其中热网是供热系统中最薄弱的环节,随着集中供热系统规模的扩大和运行年代的增长,各地热网故障不断发生,其中以泄漏故障最为常见。作为热网可靠性的一个重要组成部分,进行热网的泄漏故障诊断是保证热网经济、安全运行和提高热网管理水平的有效手段。对供热管网泄漏故障诊断算法的研究和开发,是供热企业和研究人员共同面临的一个新课题。本文针对热水供热管网的泄漏问题确定了相应的研究内容,包括热水供热管网的故障调查与统计、基于空间结构的热水供热管网泄漏水力工况模拟以及热水管网泄漏故障诊断的方法研究。首先对黑龙江省12家热力公司的一级热水供热管网的故障情况进行调查和统计,内容主要包括热水供热管网管道和元部件的故障以及事故维修队伍的情况等。对调研情况进行总结和分析,发现供热管网绝大部分的事故并不是突然出现的,而是由于供热管道或者阀门、补偿器等的腐蚀而逐渐形成的,在一段时期内是一个渐变、缓慢的过程,即大事故的发生是由热网的小泄漏作为前兆的,如果能够在事故前期发现这些小的泄漏,及时排除故障,就可以避免由小泄漏发展成大事故,而各热力公司目前多采取的人工检漏方法易受到各方面因素的影响,很可能贻误排除故障的时机,增加故障的危害和影响,因此对热水供热管网中的小泄漏进行诊断有很重要的实用价值。针对供热管网在泄漏工况所造成的供、回水管网的参数空间非对称和拓扑结构空间非对称的问题,应用图论理论描述了泄漏工况下供热管网的空间拓扑结构,并建立了空间供热管网泄漏工况水力计算模型。应用该模型分别研究了枝状热水供热管网和环状热水供热管网,同一节点漏水率不同以及不同节点漏水率相同的情况下,热网循环水泵的运行参数、热网的节点压力、管段流量及热用户资用压差和流量的变化规律。发现枝状管网发生泄漏后,管网上各种参数都有一定的变化规律,而对于较复杂的环状管网,故障发生后,尽管也能发现一些规律,但由于其特殊的结构,管网的泄漏水力工况变化更加复杂。将热水供热管网中的一条管段及该管段上所有的元部件看作一个管段单元,从而把热水供热管网抽象成为由管段单元构成的系统。在此基础上将BP神经网络的方法应用于热水管网的管段泄漏故障诊断中,建立了非线性模式识别模型。该模型可以根据热网上压力监测点的压力变化来识别发生泄漏的管段。分别利用一个枝状热水供热管网和一个环状热水供热管网对模型进行验证,表明该模型能够可靠地检测出泄漏所在的管段。具有复杂结构的环状热水供热管网,对神经网络的结构及训练精度方面都有更高的要求。将该方法应用于实际系统中,可以提高供热管网应对事故的能力。在热水供热管网管段泄漏故障诊断模型——即热水管网一级泄漏故障诊断模型的基础上,引入了层次化、模块化的思想,建立了热水供热管网二级泄漏故障诊断模型来预测发生泄漏的点的具体位置及泄漏量,并分别采用BP神经网络方法和支持向量机方法进行建模,为热水供热管网的泄漏故障诊断提供更丰富的解决途径。结合算例发现,这两种方法都可以用来解决非线性辨识问题,而且采用支持向量机方法建立的模型具有更强的泛化能力,预测效果更好。将非线性函数拟合支持向量机理论引入了热水供热管网的泄漏故障诊断研究中,建立了基于支持向量机的空间热水供热管网泄漏状态回归模型,该模型可以根据已知监测点的压力变化值回归出热网上其它节点的压力变化值。利用该方法,可以扩充泄漏状态下热水供热管网的信息量,从而有效地克服供热管网中监测点数量不足或监测点设备发生故障的情况,为建立供热管网泄漏故障诊断模型提供必要的保障。最后,针对已有历史事故资料记录不完备、数据量较少这一情况,建立了泄漏故障的试验模拟方法。并以供热管网多功能试验平台为例,进行了泄漏故障模拟。结合实测数据对本文提出的热水管网泄漏两级诊断模型及泄漏工况回归模型进行了检验。结果表明本文提出的方法具有较强的实用性,可以实现热水管网泄漏故障的诊断。更多还原

【Abstract】 The large-scale modern heating system is a complex technical system with abundant components and complex operation conditions. The fast-expanding and aging heating supply network is its weakest part with increasing reported faults and breakdowns. Leakage is commonest problem. Leakage diagnosis, an essential component of the reliability of the heating system, guarantees efficiently the heating network’s economical and secures operation and improves efficaciously the heating network’s management. Studying and exploring leakage diagnosis methods is a new question to heating supply enterprises and researchers.The paper probes into the leakage of the hot water supply network, attending particularly to the investigation and statistical records of hot water supply network, simulation of the hydraulic leakage conditions based on spatial network, and the leakage diagnosis method.First, an investigation is conducted among the first stage hot water supply networks of 12 heating power companies in Heilongjiang Provinces, into the faults of pipelines and components, and the quality of maintenance personnel. The investigation finds that most of the faults are not accidental but the results of gradual erosion in the pipelines, valves or compensators. Severe faults can be forecasted by little leakages which if are well taken care of will be removed and never deteriorate. However, the current leakage detection, mainly conducted by manpower, is subject to all kinds of factors, which bungles the best chances of repair and increases the hazards. As a result, it is valuable to diagnose the little and moderate leakages of the hot water supply networks.Against the space asymmetry of parameter and topological structure in the heating networks of supply and return water pipelines, which is caused by the leakage condition, the graph theory is used to depict the space topological structure of the hot water supply network, and to establish a hydraulic computation model of the leakage condition for the spatial hot water supply network. The model is applied to analyzing the operational parameters of circulating pumps, pressures of nodes, flow of pipelines, available head and flow of heat users, considering respectively the leakage amounts on the same node, and the same leakage amount on different nodes. It finds that the parameters of the branch-shaped heating network change regularly with leakage, and that the circumstances of the loop-shaped heating network are more complicated due to its unique structure, though some patterns could still be found.One pipeline and all its components are taken as one unit and the hot water supply network is abstracted into a system consisting of pipeline units. Thereupon, the BP neural network is applied to diagnose the pipeline leakage and thus a nonlinear identification model is built, which identifies the leakage pipeline according to the changes of hydraulic pressures of the monitoring points. Verified by a branch-shaped heating network and a loop-shaped heating network, the model can detect reliably the leakage pipeline, but stricter demands for neural network structure and training approximation are needed for the loop-shaped heating network. Applied to real heating systems, this model will improve the capabilities of problem solution.Base on the pipeline leakage diagnosis model, which is also called the first-stage leakage diagnosis model, hierarchy and modularization is introduced to the leakage diagnosis, which produces the second-stage diagnosis model for the hot water supply network to predict the particular location of leakage and its amount. BP neural network and support vector machine are used respectively to the construction of the second-stage leakage diagnosis model. Computed results show that the two methods can both solve the problems of nonlinear identification and that the model based on the support vector machine is more generalizable and predictable.A leakage condition regression model of the spatial hot water supply network is established on the basis of support vector machine with nonlinear function fit. With the pressure changes of the monitoring points, the model can work out the pressure changes of other nodes on the heating network. With this method, more information on the heating network is gained when leakage happens, which will effectively solve the problems of insufficient monitoring points and malfunctioning monitoring equipment, and guarantee the establishment of the leakage diagnosis model of the heating network.An empirical simulation method is provided against the incomplete faults records and insufficient data reserved, and is simulated under leakage conditions on a multi-function experiment platform. The two-stage leakage fault diagnosis model and leakage condition regression model are verified with observed data. It finds that the methods proposed in this paper have high practicality and can fulfil the needs of diagnosing leakage.更多还原