基于数据挖掘的大型燃煤发电机组节能诊断优化理论与方法研究
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摘要
建立经济、高效、稳定的能源供应体系是保证国民经济和社会稳定发展的基本要求。我国的资源禀赋决定了在能源供应体系中煤炭为主要一次能源,大型燃煤发电机组在我国的能源供应体系中占有举足轻重的地位,开展大型燃煤发电机组能耗分析与节能诊断的理论与方法研究对我国工业节能与低碳经济目标的实现具有重要意义。
大型燃煤发电机组具有鲜明的宽广工质热力学状态跨度、高流量和大热流密度、设备超大规模化等特点,系统、过程和单元设备不同层次上能量的转换和能量品质耗散的非线性尺度效应非常明显,发电过程能耗与环境、资源、负荷之间存在强烈的依变关系。针对大型燃煤发电机组上述特点,本文将数据挖掘理论和方法应用于大型燃煤发电机组的能耗分析和节能诊断中,采用数据挖掘方法处理机组实时在线监测数据和大量历史数据,并利用机组的设计参数和现场试验数据,研究了适用于复杂多变外部条件的节能诊断与优化理论方法,形成了融混杂数据预处理、复杂热力系统建模、能耗决策规则与知识提取、实际可达优化目标值确定、能耗离线分析与在线诊断应用框架于一体的,比较完整的大型燃煤发电机组节能诊断与优化的创新方法学体系。
本文针对大型燃煤发电机组能耗数据集数据量大、数据维数高、具有复杂非线性及强耦合性、噪声污染多等特点导致的不确定性问题,引入模糊粗糙集理论建立了机组能耗特征变量选择理论方法学；采用启发式属性约简算法有效解决了机组关键能耗特征变量选择及变量重要度分析问题。
发展了基于机组历史数据驱动的大型燃煤发电机组能耗特性建模理论与方法。以提取到的关键能耗特征变量为输入,首次提出并验证了耦合输入特征与决策变量之间依赖度的支持向量回归能耗特性模型建模方法；建立了可准确揭示机组能耗与多变的外部资源环境、设备特性以及运行控制水平之间内在联系的大型燃煤发电机组精确能耗特性模型。
开展了基于数据挖掘方法,在可比历史边界条件下寻优确定机组实际可达优化目标值的方法研究。基于模糊粗糙集决策表约简算法,提出了大型燃煤发电机组性能优化与节能诊断知识库的构建方法,确定了具有快速、智能自适应性、高度复现性和可动态调整优化等特性的机组实际可达优化目标工况。
系统阐释了基于数据挖掘的大型燃煤发电机组节能诊断优化理论与方法。对关键能耗特征变量进行了可控性分类解析,提取了各类实际可达优化目标值和各类可控能量损耗；建立了对应于机组当前运行边界、设备特性和运行工况的各类损失及其详细分布的诊断模型；首次提出了基于数据挖掘的同类机组对标诊断优化方法,进一步丰富和完善了基于数据挖掘的大型燃煤发电机组节能诊断优化理论,有效避免了单机历史工况性能局限导致的“目标工况优化天花板”问题的出现；提出了机组在线运行优化与节能诊断的概念模型；完成了基于数据挖掘的机组能耗特性仿真与节能诊断优化试验平台设计
It is essential to build an economic, efficient and stable energy supply system for the development of national economy. Considering the resource structure and distribution, coal is definitely the major primary energy in China and the large coal-fired power units are dominant in the whole power and energy supply system. It is of great significance to strengthen the research related to energy conservation of large coal-fired power units for the accomplishment of both industrial energy conservation and low-carbon economy.
The large coal-fired power units characterize as wide thermodynamic scale, huge equipment, large flow and mass, which results in distinct nonlinear feature in energy transmission, conversion and dissipation for specific equipment, system and process. There's high-dimensional nonlinear correlation between the energy consumption in power generation and the external environment, resources and load demand. For this, several advanced data mining theories and methods were introduced in this research for the energy-consumption analysis and energy-saving diagnosis of large coal-fired power units. Large volume of historian and online real-time monitoring operation data were processed by data mining method to find the potential and useful patterns as well as the knowledge relevant to the energy-consumption features of power units. Based on this, the advanced energy-saving diagnosis and optimization methodology was proposed in this research, which can reflect the complicated and diverse operational conditions and constraints. It is a comparatively complete and new methodology of energy-saving diagnosis and performance optimization covering the hybrid of data processing, complex thermal power system modeling, energy-saving decision making, realizable optimal target determining, off-line energy-consumption analyzing, online energy-consumption diagnosing and optimizing etc..
Considering the characteristics of large volume, multiple dimension, hybrid category, highly nonlinear and coupling in the operation data of large coal-fired power units, fuzzy rough sets (FRS) theory was introduced in data processing and feature selection. By means of FRS-based attribute reduction algorithm, the key energy-consumption variables and corresponding significant degree were determined, which is important for the modeling of energy-consumption characteristics of power units.
The modeling theory and methodology for complex thermal power system was developed by means of a historian operation data-driven model. By taking account of the dependence between the input features and decision index, an improved support vector regression (SD-SVR) modeling algorithm was proposed to model the energy-consumption characteristics of large coal-fired power units. The resultant model is convenient and accurate to illustrate the correlation between the energy consumption and external operation constraints, equipment features and control conditions of power units.
The data mining-based dynamic determination method of realization optimizing targets was proposed, which is adaptive to the similar or comparable operation conditions. For this purpose, based on the FRS decision table reduction algorithm, the knowledge base of performance optimization and energy-saving diagnosis was built for large coal-fired power units. The proposed method is fast, adaptive, recurrent and automatically adjustable for the determination of optimizing targets in different operation conditions of power units.
The theory and methodology of data mining-based energy-saving diagnosis and optimization was presented in this work. By categorizing the key energy-consumption variables, the features of specific target values, controllable energy losses in power generation were specified. Based on this, the energy-saving diagnosis model was proposed to determine the energy losses and distribution for the given operation conditions, external constraints and equipment features. A new concept of contrastive optimization and diagnosis was proposed to improve the energy-saving diagnosis for the power units of the same kind and breakthrough the bottlenect of high-level energy consumption resulted from the improper operation of power units. The experimental and simulating platform of such data mining-based energy-saving diagnosis and performance optimization was built and the conceptual models were proposed related to online operation optimization and energy-saving diagnosis.
大型燃煤发电机组具有鲜明的宽广工质热力学状态跨度、高流量和大热流密度、设备超大规模化等特点,系统、过程和单元设备不同层次上能量的转换和能量品质耗散的非线性尺度效应非常明显,发电过程能耗与环境、资源、负荷之间存在强烈的依变关系。针对大型燃煤发电机组上述特点,本文将数据挖掘理论和方法应用于大型燃煤发电机组的能耗分析和节能诊断中,采用数据挖掘方法处理机组实时在线监测数据和大量历史数据,并利用机组的设计参数和现场试验数据,研究了适用于复杂多变外部条件的节能诊断与优化理论方法,形成了融混杂数据预处理、复杂热力系统建模、能耗决策规则与知识提取、实际可达优化目标值确定、能耗离线分析与在线诊断应用框架于一体的,比较完整的大型燃煤发电机组节能诊断与优化的创新方法学体系。
本文针对大型燃煤发电机组能耗数据集数据量大、数据维数高、具有复杂非线性及强耦合性、噪声污染多等特点导致的不确定性问题,引入模糊粗糙集理论建立了机组能耗特征变量选择理论方法学；采用启发式属性约简算法有效解决了机组关键能耗特征变量选择及变量重要度分析问题。
发展了基于机组历史数据驱动的大型燃煤发电机组能耗特性建模理论与方法。以提取到的关键能耗特征变量为输入,首次提出并验证了耦合输入特征与决策变量之间依赖度的支持向量回归能耗特性模型建模方法；建立了可准确揭示机组能耗与多变的外部资源环境、设备特性以及运行控制水平之间内在联系的大型燃煤发电机组精确能耗特性模型。
开展了基于数据挖掘方法,在可比历史边界条件下寻优确定机组实际可达优化目标值的方法研究。基于模糊粗糙集决策表约简算法,提出了大型燃煤发电机组性能优化与节能诊断知识库的构建方法,确定了具有快速、智能自适应性、高度复现性和可动态调整优化等特性的机组实际可达优化目标工况。
系统阐释了基于数据挖掘的大型燃煤发电机组节能诊断优化理论与方法。对关键能耗特征变量进行了可控性分类解析,提取了各类实际可达优化目标值和各类可控能量损耗；建立了对应于机组当前运行边界、设备特性和运行工况的各类损失及其详细分布的诊断模型；首次提出了基于数据挖掘的同类机组对标诊断优化方法,进一步丰富和完善了基于数据挖掘的大型燃煤发电机组节能诊断优化理论,有效避免了单机历史工况性能局限导致的“目标工况优化天花板”问题的出现；提出了机组在线运行优化与节能诊断的概念模型；完成了基于数据挖掘的机组能耗特性仿真与节能诊断优化试验平台设计
It is essential to build an economic, efficient and stable energy supply system for the development of national economy. Considering the resource structure and distribution, coal is definitely the major primary energy in China and the large coal-fired power units are dominant in the whole power and energy supply system. It is of great significance to strengthen the research related to energy conservation of large coal-fired power units for the accomplishment of both industrial energy conservation and low-carbon economy.
The large coal-fired power units characterize as wide thermodynamic scale, huge equipment, large flow and mass, which results in distinct nonlinear feature in energy transmission, conversion and dissipation for specific equipment, system and process. There's high-dimensional nonlinear correlation between the energy consumption in power generation and the external environment, resources and load demand. For this, several advanced data mining theories and methods were introduced in this research for the energy-consumption analysis and energy-saving diagnosis of large coal-fired power units. Large volume of historian and online real-time monitoring operation data were processed by data mining method to find the potential and useful patterns as well as the knowledge relevant to the energy-consumption features of power units. Based on this, the advanced energy-saving diagnosis and optimization methodology was proposed in this research, which can reflect the complicated and diverse operational conditions and constraints. It is a comparatively complete and new methodology of energy-saving diagnosis and performance optimization covering the hybrid of data processing, complex thermal power system modeling, energy-saving decision making, realizable optimal target determining, off-line energy-consumption analyzing, online energy-consumption diagnosing and optimizing etc..
Considering the characteristics of large volume, multiple dimension, hybrid category, highly nonlinear and coupling in the operation data of large coal-fired power units, fuzzy rough sets (FRS) theory was introduced in data processing and feature selection. By means of FRS-based attribute reduction algorithm, the key energy-consumption variables and corresponding significant degree were determined, which is important for the modeling of energy-consumption characteristics of power units.
The modeling theory and methodology for complex thermal power system was developed by means of a historian operation data-driven model. By taking account of the dependence between the input features and decision index, an improved support vector regression (SD-SVR) modeling algorithm was proposed to model the energy-consumption characteristics of large coal-fired power units. The resultant model is convenient and accurate to illustrate the correlation between the energy consumption and external operation constraints, equipment features and control conditions of power units.
The data mining-based dynamic determination method of realization optimizing targets was proposed, which is adaptive to the similar or comparable operation conditions. For this purpose, based on the FRS decision table reduction algorithm, the knowledge base of performance optimization and energy-saving diagnosis was built for large coal-fired power units. The proposed method is fast, adaptive, recurrent and automatically adjustable for the determination of optimizing targets in different operation conditions of power units.
The theory and methodology of data mining-based energy-saving diagnosis and optimization was presented in this work. By categorizing the key energy-consumption variables, the features of specific target values, controllable energy losses in power generation were specified. Based on this, the energy-saving diagnosis model was proposed to determine the energy losses and distribution for the given operation conditions, external constraints and equipment features. A new concept of contrastive optimization and diagnosis was proposed to improve the energy-saving diagnosis for the power units of the same kind and breakthrough the bottlenect of high-level energy consumption resulted from the improper operation of power units. The experimental and simulating platform of such data mining-based energy-saving diagnosis and performance optimization was built and the conceptual models were proposed related to online operation optimization and energy-saving diagnosis.
引文
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