基于LabVIEW的水与水蒸汽物性参数计算软件开发研究

对水蒸汽物性计算区域IF97及计算公式模型进行了详细阐述,基于MALTAB编写了水与水蒸汽物性参数的计算程序,建立了水与水蒸汽的物性参数动态链接库。基于LabVIEW开发了水与水蒸汽物性参数计算软件界面。对比结果分析表明,该软件计算的结果与2007年国际水与水蒸汽表中的数据完全吻合,说明此软件具有很高的计算准确性,可应用于工程实际中的水与水蒸汽物性参数计算。     

锅炉热力计算中传热系数类库的实现

通过对烟气、空气和水蒸汽的物性参数进行拟合，直接采用烟气、蒸汽侧对流传热系数公式计算对流换热系数，进一步实现锅炉对流受热面的传热系数类库编制，从而可以不再采用传统的查图求取的方法，提高计算的精度和效率。     

烟风介质中水蒸汽露点温度确定方法及水露点计算公式的探讨

在燃烧系统热力计算中通常需按湿空气图表来查取烟风介质的水蒸汽露点温度，这是不够方便的，而且在低气压等不同条件下的精确性也存在疑点。本文在湿空气理论和饱和水蒸汽温压特性拟合方程的基础上，推导提出了通过解析方程直接求解烟风介质中水蒸汽露点温度的新方法，其适用范围优于现行方法且精确度达到99.9%以上。     

大型火电机组加热器动态学模型的研究

以在大型火电机组中广泛使用的三段式加热器为研究对象，充分考虑了加热器结构参数的工质物性参数对传热过程的影响以及静态计算与动态教学模型间的差别，建立了加热器动态数学模型。仿真实验与工程应用证明该模型具有较高的静态计算精度和良好的动态响应特性，具有较广泛适用性和一定的工程应用价值。     

水蒸汽凝结过程的■分析

对于不同压力下饱和水蒸汽的凝结过程,利用饱和水蒸汽的焓值等价代替导热过程定义的所对应的热容量,推出了水蒸汽凝结过程中的变化的计算表达式,得到了在不同压力下饱和水蒸汽凝结过程中■变的变化趋势及■变值达到最大时对应的压力,为利用水蒸汽冷凝放热的热力设备优化提供依据。     

高水分烟气的热物理性质

在燃用废弃物的特种锅炉中,烟气的水分容积含量ｒＨ２Ｏ达到或超过了常用锅炉热力计算标准２５％ 的上限,需要对原有的计算方法及公式进行检验和修正。本文使用线性规则和混合物规则分别计算了不同水分含量的气体混合物的热物理性质以及相应的对流换热特性,得到了物性随温度及水蒸汽容积含量变化的曲线。分析表明,在锅炉尾部低温区的对流换热受热物性影响较大,对高水分的烟气需进行修正。     

600MW机组热耗率可视化计算程序的开发与应用

根据国家标准GB8117—87与国际最新水和水蒸汽热力性质计算公式,采用反平衡方法推导国产600MW汽轮机组热耗率的计算关系式,并以此编制可视化程序进行了计算,计算结果与汽轮机厂家提供的热力特性参数吻合较好,对电厂热力测试具有较强的实用价值。     

板坯质量诊断模型重要钢种热物性参数的计算方法

对板坯质量在线诊断分析预测模型CISDI_SQDS ONLINER2011中耦合考虑的几个重要钢种热物性参数的冶金物理意义和数值计算方法进行了详细讨论,包括线性热膨胀系数、临界应变、临界应力和凝固潜热,模型投入实际运行后计算结果合理、计算过程可靠,表明模型在进行理论分析时所引入的热物性参数计算方法具有合理性和可靠性。     

水和水蒸汽物理性质拟合多项式

水和水蒸汽热力性质图表是从事热力工程设计和研究不可缺少的基础资料。它包括水和水蒸汽的状态参数(压力、温度、比容、焓、熵)和物理性质参数(动力粘度、导热系数、比热、普朗特数)。工程上常用近似的数学表达式来描述这些图表参数,以适应电子计算机计算的要求。根据汽轮机行业当前使用的需要和我国电子计算机容量的现况,选取了适中的图表使用范围,利用最小二乘法原理通过计算机自动拟合计算优选出以二元多项式表示的水和水蒸汽热力性质方程,拟合的原始数据取自水和水蒸汽热力性质图表(文1)。数年前拟合所得的水和水蒸汽状态方程和根据实用需要随后拟合的若干补充方程曾     

锅炉单相受热面动_静态特性通用计算方法

在流体微远追踪计算方法的基础上,提出了锅炉单相受热面热力过程动,静态特性的通用计算方法。利用该算法模型既可以进行锅炉单相受热面的动态过程分析,又能够完成锅炉单相受热面热力特性的静态校核计算,并能十分方便地确定出锅炉单相受热面热力参数的动,静态分布情况。     

Identification of armature, field, and saturated parameters of alarge steam turbine-generator from operating data

This paper presents a step by step identification procedure of armature, field and saturated parameters of a large steam turbine-generator from real time operating data. First, data from a small excitation disturbance is utilized to estimate armature circuit parameters of the machine. Subsequently, for each set of steady state operating data, saturable mutual inductances L_ads and L_aqs are estimated. The recursive maximum likelihood estimation technique is employed for identification in these first two stages. An artificial neural network (ANN) based estimator is used to model these saturated inductances based on the generator operating conditions. Finally, using the estimates of the armature circuit parameters, the field winding and some damper winding parameters are estimated using an output error method (OEM) of estimation. The developed models are validated with measurements not used in the training of ANN and with large disturbance responses     

An artificial-neural-network method for the identification ofsaturated turbogenerator parameters based on a coupledfinite-element/state-space computational algorithm

An artificial neural network (ANN) is used in the identification of saturated synchronous machine parameters under diverse operating conditions. The training data base for the ANN is generated by a time-stepping coupled finite-element/state-space (CFE-SS) modeling technique which is used in the computation of the saturated parameters of a 20-kV, 733-MVA, 0.85 PF (lagging) turbogenerator at discrete load points in the P-Q capability plane for three different levels of terminal voltage. These computed parameters constitute a learning data base for a multilayer ANN structure which is successfully trained using the backpropagation algorithm. Results indicate that the trained ANN can identify saturated machine-reactances for arbitrary load points in the P-Q plane with an error less than 2% of those values obtained directly from the CFE-SS algorithm. Thus, significant savings in computational time are obtained in such parameter computation tasks     

Inverse neural network for optimal performance in polygeneration systems

In this paper, inverse neural network (ANNi) is applied to optimization of operating conditions or parameters in energy processes. The proposed method ANNi is a new tool which inverts the artificial neural network (ANN), and it uses a Nelder-Mead optimization method to find the optimum parameter value (or unknown parameter) for a given required condition in the process. In order to accomplish the target, first, it is necessary to build the artificial neural network (ANN) that simulates the output parameters for a polygeneration process. In general, this class of ANN model is constituted of a feedforward network with one hidden layer to simulate output layer, considering well-known input parameters of the process. Normally, a Levenberg–Marquardt learning algorithm, hyperbolic tangent sigmoid transfer-function, linear transfer-function and several neurons in the hidden layer (due to the complexity of the process) are considered in the constructed model. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown input parameter using the Nelder-Mead algorithm. ANNi results on three different applications in energy processes showed that ANNi is in good agreement with target and calculated input data. Consequently, ANNi is applied to determine the optimal parameters, and it already has applications in different processes with a very short elapsed time (seconds). Therefore, this methodology can be useful for the controlling of engineering processes.     

Neural network based modeling of a large steam turbine-generatorrotor body parameters from on-line disturbance data

A novel technique to estimate and model rotor-body parameters of a large steam turbine-generator from real time disturbance data is presented. For each set of disturbance data collected at different operating conditions, the rotor body parameters of the generator are estimated using an output error method (OEM). Artificial neural network (ANN) based estimators are later used to model the nonlinearities in the estimated parameters based on the generator operating conditions. The developed ANN models are then validated with measurements not used in the training procedure. The performance of estimated parameters is also validated with extensive simulations and compared against the manufacturer values     

Thermodynamic analysis of a tri-generation system based on micro-gas turbine with a steam ejector refrigeration system

In the present work, performance of new configuration of Micro-gas turbine cogeneration and tri-generation systems, with a steam ejector refrigeration system and Heat recovery Steam Generator (HRSG) are studied. A micro-gas turbine cycle produces 200 KW power and exhaust gases of this micro-gas turbine are recovered in an HRSG. The main part of saturated steam in HRSG is used through a steam ejector refrigeration system to produce cooling in summer. In winter, this part of saturated steam is used to produce heating. In the first part of this paper, performance evaluation of this system with respect to Energy Utilization Factor (EUF), Fuel Energy Saving Ratio (FESR), thermal efficiency, pinch point temperature difference, net power to evaporator cooling load and power to heat ratio is carried out. It has been shown that by using the present cogeneration system, one can save fuel consumption from about 23% in summer up to 33% in winter in comparison with separate generation of heating, cooling and electricity.     

Modeling of air compressor for proton exchange membrane fuel cell

空压机的性能对燃料电池的性能有很大影响,为准确建立空压机数学模型,使用等效电路结构方法,建立关于转速、流量、压力这三个变量的非线性函数。对空压机等效电路数学模型参数和空压机性能参数数据进行拟合,并根据拟合效果依次采用基于最大流量偏差和基于出口压力加权两种方法改进拟合方法,实现对静态模型较高精度的拟合。     

Two-Step Method for Identification of Nonlinear Model of Induction Machine

A two-step method is applied for parameter identification of a saturated electromechanical model of an induction machine (IM). The k-factor cross saturation technique is used to account for iron saturation. Balanced and unbalanced short-circuits armature current waveforms of this machine are less sensitive to variations in the mechanical parameters. Conversely, any change in these parameters has a strong impact on the start-up test. Accordingly, in the proposed estimation method, the online double-line-to-neutral short-circuit test is performed to estimate the electrical parameters of the machine and the results obtained are then used to compute the mechanical parameters from the starting test. The good agreement of the estimated results with actual data attests to the effectiveness and suitability of the proposed algorithm in computing the electromechanical parameters of these machines.     

超超临界汽轮机转子系统的稳定性计算

考虑汽流激振的超超临界汽轮机转子系统的稳定性分析是个较新的课题。采用有限元法,对超超临界汽轮机轴系建模,将转子和不转动的轴承一基础系统作为一个耦合整体处理,开发了耦合计算程序,能够计入汽流激振,使计算更符合机组运行工况。开发的程序可以计算转子各阶模态振型与临界转速,进行稳定性的分析。算例计算的结果表明,方法和开发的程序正确合理,可以为超超临界汽轮机轴系稳定性的判断、结构与参数的设计起到一定作用。     

垃圾焚烧发电项目余热锅炉中参数和高参数的对比分析

垃圾焚烧发电厂蒸汽参数的选取直接影响垃圾焚烧发电厂的发电效率和收益,提高蒸汽参数成为提高发电效率的重要途径。根据对国内主流垃圾焚烧发电项目投建方和设备厂家的调研及理论分析,对中参数和高参数项目在技术特点、设备投资、维护成本、经济收益等方面进行了比较研究。采用高参数余热锅炉的项目,其设备成本和运营维护成本均较高,但发电量收入增加显著;项目的处理规模越大,采用高参数的经济收益越明显,设备投资静态回收期越短,经济优势越明显。     

多参数耦合的蒸汽动力系统运行稳定性评估方法

为解决多参数耦合情况下蒸汽动力系统整体运行稳定性评估的问题,在基于时间序列分析的单参数运行稳定性评估的基础上,提出了一种蒸汽动力系统整体运行稳定性评估方法。该方法通过分析蒸汽动力系统运行参数的相关性和作用机制,选取相关状态参数建立系统的整体运行稳定性评估指标体系;然后通过引入连接函数（Copula函数）建立稳定性指标计算模型,实现系统在多参数耦合情况下的联合失稳概率和整体运行稳定性指数计算。经实例验证,该方法能够依据监测参数历史数据,相对精确地预测出系统未来一段时间的运行状态,预测精度达到秒级。