基于遗传算法和神经网络的锅炉汽水系统模型参数优化
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摘要
火电厂仿真机在培训集控运行值班员、减少误操作和被迫停机的次数、提高电厂运行安全性和经济性方面发挥了重要作用。电站仿真机数学模型一般采用机理建模方法,其定性结论合理,但是通常不够准确,建模人员需要花费大量时间和精力反复手工调整模型参数。针对这一状况,本文研究了遗传算法和人工神经网络在火电厂机理数学模型参数优化中的应用。主要工作如下:
研究300MW单元机组汽包锅炉汽水系统主要受热面的机理数学模型,将其参数划分为静态特性参数和动态特性参数。对于静态参数,研究其变化对出口参数稳态数值的影响;对于动态特性参数,研究其变化对扰动条件下过渡过程的影响。从单相介质换热器模型的出口焓值计算公式进行分析,指出跷跷板效应的产生原因,给出判定准则和工程解决方法。
以高温过热器数学模型为例,采用遗传算法进行参数优化。对于稳态误差和动态特性,分别建立对应的目标函数,选取相关的模型优化参数。经过优化的高温过热器模型,其稳态误差和动态特性误差都达到规定的要求。以锅炉厂的热力计算汇总数据为依据,对锅炉汽水系统的其他受热面模型,针对其稳态误差进行了参数优化。
基于神经网络的非线性映射能力和快速计算能力,采用BP神经网络保存遗传算法的优化结果。将模型主要输入、输出和优化参数组成数据集合,作为学习样本对神经网络进行训练,训练完成后的BP网络神经网络直接输出优化结果。针对汽水系统的各个换热模块,分别建立对应的优化神经网络,组成汽水系统模型的神经网络优化库。
将神经网络优化库与遗传算法结合使用,对汽水系统模型进行系统优化。首先使用神经网络进行单模块初步优化,再以汽水系统模型关键参数的误差最小作为目标,使用遗传算法进行优化,使汽水系统整体模型误差达到规定的要求。
以控制论观点分析数学建模和仿真过程,提出集成化和智能化模型结构IIMS(integrated and intelligence model structure)。它将设备原理和结构、结构参数、运行数据、控制规则和最终模型绑定在一起,具有对象特性的显式表示与可叠加性、优化算法开放性等特点。IIMS将模型优化模式由人工参数调整转变为程序优化,降低调试工作量;资深建模人员的调试经验以程序的形式固定下来,供初学者使用;它还可以提高模型重用性,便于模型的维护与更新。本文提出的遗传算法和人工神经网络结合的模型优化方法,具有通用性,适用于各种不同类型的电站仿真机进行参数优化。
Power plant simulator plays an important role in training operators, reduceing misoperation and unscheduled shutdown to enhance safety and economy. The model of power plant is usually built in mechanism method. Though its qualitative character is reasonable, its accuracy is unsatisfied. Model engineer has to spend a great many of hours to regulate model parameters manually. Focused on this problem, genetic algorithm and atrifical neural network are studied to optimize mechanism model parameters. The following research work has been done.
The mechanism models of steam and water system are analysed, which are a part of drum boiler in a 300MW power unit. The model parameters are divided into two groups: stable characteristics parameter and dynamic characteristics parameter. The relation between stable characteristic parameters and model output errors is studied. Meanwhile, the influence of dynamic characteristic parameters variety is also studied under the condition of the disturbance,. The output enthalpy formula of single phase heater is analyzed to find out the cause of see-saw effect. Then the criterion to avoid it and engineering solving method are put forward.
Genetic algorithm is applied to optimize high temperature superheater model. The two target functions for minimizing stable errors and dynamic characteristics are constructed seperately. The optimization parameters corresponding to the target functions are seleted. The errors of high temperature superheater model optimized by GA are less than stated value. According to thermal data summary given by boiler manufacturer, GA is applied to optimize other heater models of steam and water system to minimize their stable errors.
Based on the nonlinear mapping and fast computing ability of aritfical neural network(ANN), BP neural network is adopted to save optimization result by GA. The model imputs, outputs and optimized parameters make up learning sample set. After training, BP neural network calculates model optimization parameters directly. The BP neural network for each heater model of steam and water system are built and trained. All of them constitute ANN optimization library.
ANN optimization library and GA are combined to optimize steam and water system model in the global space. At first, ANN optimization library is used to optimize single module. Then aiming at minimizing key parameter errors of the model, GA is applied to optimize model parameters in the global space to make its errors lesst than stated value.
The integrated and intelligent model structure (IIMS) is put forward on the basis of mathematics modeling process study in the viewpoint of cybernetics. IIMS binds equipment principle and structure, strcutre parameters, running data, control algorithm and final model. It has the characteristic of explicit expression and additivity of simulation object property, and openness of optimization algorithm. The parameter optimization mode is changed from manully adjustment to algorithm optimization to cut down debugging workload due to IIMS. The debugging experience of senior engineer can be fixed in the program and be reused by beginner. It is benefit to model reusability, maintenance and update. Because the combination of genetic algorithm and atrifical neural network in the paper is a general frame, it is suitable for model optimization of power plant unit of different type.
研究300MW单元机组汽包锅炉汽水系统主要受热面的机理数学模型,将其参数划分为静态特性参数和动态特性参数。对于静态参数,研究其变化对出口参数稳态数值的影响;对于动态特性参数,研究其变化对扰动条件下过渡过程的影响。从单相介质换热器模型的出口焓值计算公式进行分析,指出跷跷板效应的产生原因,给出判定准则和工程解决方法。
以高温过热器数学模型为例,采用遗传算法进行参数优化。对于稳态误差和动态特性,分别建立对应的目标函数,选取相关的模型优化参数。经过优化的高温过热器模型,其稳态误差和动态特性误差都达到规定的要求。以锅炉厂的热力计算汇总数据为依据,对锅炉汽水系统的其他受热面模型,针对其稳态误差进行了参数优化。
基于神经网络的非线性映射能力和快速计算能力,采用BP神经网络保存遗传算法的优化结果。将模型主要输入、输出和优化参数组成数据集合,作为学习样本对神经网络进行训练,训练完成后的BP网络神经网络直接输出优化结果。针对汽水系统的各个换热模块,分别建立对应的优化神经网络,组成汽水系统模型的神经网络优化库。
将神经网络优化库与遗传算法结合使用,对汽水系统模型进行系统优化。首先使用神经网络进行单模块初步优化,再以汽水系统模型关键参数的误差最小作为目标,使用遗传算法进行优化,使汽水系统整体模型误差达到规定的要求。
以控制论观点分析数学建模和仿真过程,提出集成化和智能化模型结构IIMS(integrated and intelligence model structure)。它将设备原理和结构、结构参数、运行数据、控制规则和最终模型绑定在一起,具有对象特性的显式表示与可叠加性、优化算法开放性等特点。IIMS将模型优化模式由人工参数调整转变为程序优化,降低调试工作量;资深建模人员的调试经验以程序的形式固定下来,供初学者使用;它还可以提高模型重用性,便于模型的维护与更新。本文提出的遗传算法和人工神经网络结合的模型优化方法,具有通用性,适用于各种不同类型的电站仿真机进行参数优化。
Power plant simulator plays an important role in training operators, reduceing misoperation and unscheduled shutdown to enhance safety and economy. The model of power plant is usually built in mechanism method. Though its qualitative character is reasonable, its accuracy is unsatisfied. Model engineer has to spend a great many of hours to regulate model parameters manually. Focused on this problem, genetic algorithm and atrifical neural network are studied to optimize mechanism model parameters. The following research work has been done.
The mechanism models of steam and water system are analysed, which are a part of drum boiler in a 300MW power unit. The model parameters are divided into two groups: stable characteristics parameter and dynamic characteristics parameter. The relation between stable characteristic parameters and model output errors is studied. Meanwhile, the influence of dynamic characteristic parameters variety is also studied under the condition of the disturbance,. The output enthalpy formula of single phase heater is analyzed to find out the cause of see-saw effect. Then the criterion to avoid it and engineering solving method are put forward.
Genetic algorithm is applied to optimize high temperature superheater model. The two target functions for minimizing stable errors and dynamic characteristics are constructed seperately. The optimization parameters corresponding to the target functions are seleted. The errors of high temperature superheater model optimized by GA are less than stated value. According to thermal data summary given by boiler manufacturer, GA is applied to optimize other heater models of steam and water system to minimize their stable errors.
Based on the nonlinear mapping and fast computing ability of aritfical neural network(ANN), BP neural network is adopted to save optimization result by GA. The model imputs, outputs and optimized parameters make up learning sample set. After training, BP neural network calculates model optimization parameters directly. The BP neural network for each heater model of steam and water system are built and trained. All of them constitute ANN optimization library.
ANN optimization library and GA are combined to optimize steam and water system model in the global space. At first, ANN optimization library is used to optimize single module. Then aiming at minimizing key parameter errors of the model, GA is applied to optimize model parameters in the global space to make its errors lesst than stated value.
The integrated and intelligent model structure (IIMS) is put forward on the basis of mathematics modeling process study in the viewpoint of cybernetics. IIMS binds equipment principle and structure, strcutre parameters, running data, control algorithm and final model. It has the characteristic of explicit expression and additivity of simulation object property, and openness of optimization algorithm. The parameter optimization mode is changed from manully adjustment to algorithm optimization to cut down debugging workload due to IIMS. The debugging experience of senior engineer can be fixed in the program and be reused by beginner. It is benefit to model reusability, maintenance and update. Because the combination of genetic algorithm and atrifical neural network in the paper is a general frame, it is suitable for model optimization of power plant unit of different type.
引文
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