火电机组节能在线分析与智能运行优化方法研究
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摘要
厂级监控信息系统(SIS)在电力企业信息化建设的进程中得到了迅速的推广和应用,同时也给生产和科研创造了新的发展契机。机组性能在线分析与运行优化是领域的研究核心热点之一。本文拓展了热力系统热经济状态方程分析法;基于已有性能在线系统应用及运行数据进行了大量统计分析,明确了机组运行优化的节能潜力空间;首次提出将基于案例的推理和基于概率因果推理两种智能方法应用于机组运行优化。
深入研究矩阵结构与热力系统结构的关系,结合传统理论的假设,引入控制理论中线性定常系统的状态空间表达法,揭示出热力系统的汽水分布方程本质上就是多输入多输出系统状态方程的稳定态形式,状态空间表达法是对热力系统向更一般系统的抽象。将辅助汽水看作是施加到主系统上的扰动输入,提出了热扰动向量的概念及其构造方法,推导出了分析系统输出变化的统一计算公式,丰富了电厂节能理论,极大地简化了系统节能分析过程。
在线模型的可靠性是基于模型决策的基础。由准筛选的出机组性能评价有效数据分析得出,单一连续稳定运行工况的在线数据性能计算指标的不确定度约为2%;在相近的外界边界条件下,由于运行方式不同或运行参数偏离等,其不确定度约为6%。即运行优化的可行区间约为4%(约12g/kW·h)。由全局灵敏度分析得出了影响模型输出不确定的关键变量,为模型优化及系统维护提供依据。
基于机组运行工况能耗率的统计分布特征,提出了基于案例推理的运行优化指导方法,解决了特征环节和特征参数的选择等几个关键问题。同时提出了基于“三工况”运行决策思想及决策因子计算方法,通过决策因子的优先级排列,增强了系统的辅助决策能力。较之于以往各种给定运行优化目标值的方法,该方法的新颖之处在于其信息的完整性和可操作性,是一种非常有效的“粗调”指导方法。
由于机组热力系统结构复杂,子系统及设备的运行参数间存在较强的耦合性,在机组设备无故障假设下,机组性能依然可能会处于一种亚健康状态。提出了基于概率因果模型的深层次机组运行优化参数诊断方法。建立了机组系统级变量的因果依赖模型,该模型由现场数据参数化后可直接用于机组参数偏离的诊断。提出了基于相对信息熵的运行优化决策方法,其实质通过概率推理来寻找出机组运行参数间的最优组合模式。
智能决策支持系统是今后电力企业深化信息化建设的必然选择。本文给出了机组智能运行优化决策支持系统的体系结构框架设计,并基于文中所采用的智能模型,讨论了多模型融合技术,提出了决策支持报警的系统设计思路。
Supervisory Information System (SIS) has a rapid application in the process of informationalization of electric power enterprises and stimulates the interest of researchers at same time. The online performance assessment and operation optimization of power unit are the key issuses. The thermo-economic state equations-based system analysis method is developed in this thesis. The reliability of online performance model is investigated basing on the operation data and two artificial intelligence methods are imported to cope with the new problems from the established applications.
The relationship between matrix equation and system structure is deduced in the paper. Under the same assumption with theses traditional methods, the state space expression of linear time invariant systems is imported. The steam-water distribution equation is revealed as the stable form of the state equation. The concept of thermal disturbance vector and its construction rules are proposed by regarding the auxiliary steam/water as the disturbances imposed on the main system. Then, the uniform formula is obtained for the calculation of system output, which simplifies the system analysis greatly. State space expression abstracts the thermal system to a general system. The study discovered the nature of matrix based analysis, and the essential of the local quantitative analysis of thermal system is its structure analysis.
The reliability of the online performance model is investigated basing on the abundant operation data. Statistic analysis proceeds on the available data satisfying the performance test approximately. The uncertainty is 2% for a continuous working condition and 6% for the conditions under similar boundary parameters (load and inlet temperature of circle water). Thus, the possible scope for energy saving through optimal operation is 4%, which is encouraging for further research. However, the unexpected uncertainty of a continuous working condition brings the new challenge. The important inputs of online model are worked out by global sensitivity analysis, which can be referenced for system maintenance.
The CBR is used for the operation advice. Those key issues are settled, such as, the selection of characteristic process and parameters. A decision factor is proposed for ranking operation priority basing on three working conditions (current condition, similar condition and optimal condition). The unsteady working condition will go to the similar condition without adjustment, so those parameters whose value in similar condition lapse from the value in the optimal condition. The new character of optimal operation value by CBR is its integrity and feasibility compared with current methods.
Due to the complicated structure, strong coupling among sub-systems and changeable properties of matter, the performance of power unit maybe still takes on a sub-health condition with fault-free devices. This needs the deep diagnosis and decision. A probabilistic casual reasoning method is proposed for the diagnosis of optimal operation parameters. A causal model (Bayesian networks) is constructed using system-level properties of power unit. The model parameterized by operational data can be used for parameters deviation diagnosis on the actual power unit directly. A relative entropy based operation support method is proposed. Its essential is to search an optimal combination of parameters under the current constraints by probabilistic reasoning.
At last, a machine learning-based intelligence decision support system prototype is design for the operation optimization. The multi-model fusion techniques are discussed. Based on the characteristic of the two AI methods used, an idea of decision support alarm is proposed.
深入研究矩阵结构与热力系统结构的关系,结合传统理论的假设,引入控制理论中线性定常系统的状态空间表达法,揭示出热力系统的汽水分布方程本质上就是多输入多输出系统状态方程的稳定态形式,状态空间表达法是对热力系统向更一般系统的抽象。将辅助汽水看作是施加到主系统上的扰动输入,提出了热扰动向量的概念及其构造方法,推导出了分析系统输出变化的统一计算公式,丰富了电厂节能理论,极大地简化了系统节能分析过程。
在线模型的可靠性是基于模型决策的基础。由准筛选的出机组性能评价有效数据分析得出,单一连续稳定运行工况的在线数据性能计算指标的不确定度约为2%;在相近的外界边界条件下,由于运行方式不同或运行参数偏离等,其不确定度约为6%。即运行优化的可行区间约为4%(约12g/kW·h)。由全局灵敏度分析得出了影响模型输出不确定的关键变量,为模型优化及系统维护提供依据。
基于机组运行工况能耗率的统计分布特征,提出了基于案例推理的运行优化指导方法,解决了特征环节和特征参数的选择等几个关键问题。同时提出了基于“三工况”运行决策思想及决策因子计算方法,通过决策因子的优先级排列,增强了系统的辅助决策能力。较之于以往各种给定运行优化目标值的方法,该方法的新颖之处在于其信息的完整性和可操作性,是一种非常有效的“粗调”指导方法。
由于机组热力系统结构复杂,子系统及设备的运行参数间存在较强的耦合性,在机组设备无故障假设下,机组性能依然可能会处于一种亚健康状态。提出了基于概率因果模型的深层次机组运行优化参数诊断方法。建立了机组系统级变量的因果依赖模型,该模型由现场数据参数化后可直接用于机组参数偏离的诊断。提出了基于相对信息熵的运行优化决策方法,其实质通过概率推理来寻找出机组运行参数间的最优组合模式。
智能决策支持系统是今后电力企业深化信息化建设的必然选择。本文给出了机组智能运行优化决策支持系统的体系结构框架设计,并基于文中所采用的智能模型,讨论了多模型融合技术,提出了决策支持报警的系统设计思路。
Supervisory Information System (SIS) has a rapid application in the process of informationalization of electric power enterprises and stimulates the interest of researchers at same time. The online performance assessment and operation optimization of power unit are the key issuses. The thermo-economic state equations-based system analysis method is developed in this thesis. The reliability of online performance model is investigated basing on the operation data and two artificial intelligence methods are imported to cope with the new problems from the established applications.
The relationship between matrix equation and system structure is deduced in the paper. Under the same assumption with theses traditional methods, the state space expression of linear time invariant systems is imported. The steam-water distribution equation is revealed as the stable form of the state equation. The concept of thermal disturbance vector and its construction rules are proposed by regarding the auxiliary steam/water as the disturbances imposed on the main system. Then, the uniform formula is obtained for the calculation of system output, which simplifies the system analysis greatly. State space expression abstracts the thermal system to a general system. The study discovered the nature of matrix based analysis, and the essential of the local quantitative analysis of thermal system is its structure analysis.
The reliability of the online performance model is investigated basing on the abundant operation data. Statistic analysis proceeds on the available data satisfying the performance test approximately. The uncertainty is 2% for a continuous working condition and 6% for the conditions under similar boundary parameters (load and inlet temperature of circle water). Thus, the possible scope for energy saving through optimal operation is 4%, which is encouraging for further research. However, the unexpected uncertainty of a continuous working condition brings the new challenge. The important inputs of online model are worked out by global sensitivity analysis, which can be referenced for system maintenance.
The CBR is used for the operation advice. Those key issues are settled, such as, the selection of characteristic process and parameters. A decision factor is proposed for ranking operation priority basing on three working conditions (current condition, similar condition and optimal condition). The unsteady working condition will go to the similar condition without adjustment, so those parameters whose value in similar condition lapse from the value in the optimal condition. The new character of optimal operation value by CBR is its integrity and feasibility compared with current methods.
Due to the complicated structure, strong coupling among sub-systems and changeable properties of matter, the performance of power unit maybe still takes on a sub-health condition with fault-free devices. This needs the deep diagnosis and decision. A probabilistic casual reasoning method is proposed for the diagnosis of optimal operation parameters. A causal model (Bayesian networks) is constructed using system-level properties of power unit. The model parameterized by operational data can be used for parameters deviation diagnosis on the actual power unit directly. A relative entropy based operation support method is proposed. Its essential is to search an optimal combination of parameters under the current constraints by probabilistic reasoning.
At last, a machine learning-based intelligence decision support system prototype is design for the operation optimization. The multi-model fusion techniques are discussed. Based on the characteristic of the two AI methods used, an idea of decision support alarm is proposed.
引文
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