【作者】 乔弘；

【导师】 韩璞； 翟永杰；

【作者基本信息】 华北电力大学（北京） ， 热能工程， 2009， 博士

【摘要】 我国火力发电的能源利用率相对较低,主要影响因素之一是许多重要的技术参数和经济参数难以在线实时测量,如与锅炉效率密切相关的烟气含氧量、飞灰含碳量、球磨机负荷等热工参数。软测量技术是解决这些参数测量问题的有效方法之一,它利用一些易于测量的变量通过在线分析来估计这些不可测或难测变量。本文围绕热工参数软测量的几个关键技术问题,展开了以下研究:1.全面分析火电厂监测对象及热工参数的特点,讨论影响热工参数软测量精度的主要因素为:数据预处理、辅助变量选取、建模算法、模型结构。2.从数据预处理及辅助变量选取两个关键技术问题出发,研究提高火电厂热工参数软测量精度和可行性的方法:数据预处理方面,充分考虑现场数据的误差分析与处理;辅助变量选取方面,引入灰色理论进行优化选取,提高模型的精度和可行性。3.建模算法支持向量机,其自身参数对建模精度有很大影响。本文提出基于训练数据的参数自适应支持向量回归建模方法,减少参数选择过程中的人为因素影响和精度不确定性问题。使用实际现场数据对烟气含氧量进行软测量建模,验证了该方法的有效性。4.将具有快速收敛特性的序列最小优化(SMO)算法与粒子群(PSO)算法相结合,提出PSO-SMO软测量建模方法,以基于训练数据得到的算法参数为初始值,应用双层优化方法进一步优化,提高建模精度及模型收敛速度。5.针对火电厂热工参数监测的实时性要求,将以上所提出的建模算法在典型的DCS现场控制站硬件配置条件下和在QNX实时操作系统环境中编程实现。应用现场数据进行建模,实际运行结果满足工业现场的要求,具有很好的工程实践意义。6.为使软测量模型能够更好地适应现场工况变化和测量对象的动态特性,对软测量模型的结构进行研究,提出多模型动态软测量建模方法。应用火电厂多工况历史数据,建立基于多支持向量机模型(M-SMO)的烟气含氧量和飞灰含碳量软测量模型,结果表明多模型建模方法能够满足热工过程变工况条件下的精度要求。更多还原

【Abstract】 The energy utilization efficiency of thermal power generation is relatively low,one of the main influence factors is that lots of important technical parameters and economic parameters are difficult to be real-time measured online,such as oxygen content in flue gas,carbon content in fly ash,ball mill load and other thermal parameters related to boiler efficiency closely.Soft sensor technology is one of the effective ways to solve the problem of measuring these parameters,utilizing some parameters liable to be measured through on-line analysis to estimate these variables unable or difficult to be measured.The following researches are expanded focusing on several key technical problems of soft sensor for thermal parameters:1.This paper comprehensively analyses the characteristics of monitoring objects and thermal parameters in power plant as well as discusses main factors which affect the accuracy of soft sensor for thermal parameters.The results of the discussion were summarized as follows:data preprocessing,auxiliary variables selection,modeling algorithm and model structure.2.Two key technical problems are put forward-data preprocessing and auxiliary variables selection.This paper researches on the methods of improving the accuracy and feasibility of soft sensor for thermal parameters in power plant:adequately considering the error analysis and processing of field data in one aspect of data preprocessing;introducing grey theory to make optimal selection in order to improve the accuracy and feasibility of the models in another aspect of auxiliary variables selection.3.Modeling algorithm-supports vector machine-and its own parameters have a great impact on modeling accuracy.This paper presents a modeling method of parameter self-adaptive support vector machine based on training data which can reduce the impact of human factors and uncertainty of the accuracy in the process of parameter selection.The method has been applied to soft sensor modeling of oxygen content in flue gas using actual field data and its effectiveness is proved.4.Combining the sequential minimal optimization(SMO) algorithm that has the characteristics of fast convergence with particle swarm optimization(PSO) algorithm,the modeling method of soft sensor based on PSO-SMO algorithm is proposed in this paper.The new model gets the algorithm parameters used as initial values from training data,using double-layer optimization method to get further optimization,thus improves modeling accuracy and convergence speed of the model. 5.According to the real-time requirements of thermal parameter monitoring in power plant,the modeling algorithm mentioned above is realized by programming under the conditions of typical hardware configuration of a distributed control unit in DCS and in the environment of real-time operating system of QNX.Modeling with field data,actual results meet the requirements of the industrial field,with great engineering practice significance.6.To adapt better to the changes of field conditions and dynamic characteristics of measure object,this paper studies on the structure of soft sensor model and puts forward the modeling method of multi-model dynamic soft sensor.The method has been applied to soft sensor modeling of oxygen content in flue gas and carbon content in fly ash based on M-SMO with historical data under multiple load conditions in power plant.The result indicates that multi-model modeling method can meet the requirements of accuracy under variable conditions in the thermal process.更多还原