超超临界机组非线性控制模型研究
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摘要
深入分析超超临界机组特性,建立适用于超超临界机组控制器设计的模型,准确获取相关状态变量信号是对超超临界机组进行控制优化的前提。控制模型要精度足够,具有便于分析计算的解析形式。有些控制优化所需的状态变量信号是难以采用传统检测方式准确获取的。对于这些信号,可以通过构造其与其它可测变量之间的关系间接得到。构造的状态变量信号要易于实现,有足够精度。围绕这2个目标,以建立控制模型为主线,本文主要进行了以下工作：
提出了基于复合建模方法的超超临界机组控制模型建立方案。采用单一建模方法会造成模型复杂度高,模型精度不够或通用性低的问题,而复合建模方法将两种基本方法结合起来,可以充分发挥各自优势并且互相弥补不足。运用复合建模方法建模时,存在模型结构不合适造成的数据信息过补偿或欠补偿问题。为了能够在精度要求范围内最大限度地利用从数据中提取的信息,简化模型结构,定义了最佳模型结构,将控制模型建立问题转化为最佳模型结构建立问题。
建立了包含最多有效信息的直流炉机组最简控制模型结构。基于合理的简化假设建立包含最多机组有效信息的最简控制模型结构是找到最佳模型结构的关键。当采用分区集总方法建立直流炉模型时,为了有效规避相变点位置问题研究并使划分区段数合理地降至为最小值,运用理论和数据分析为包含两相区的固定边界区段选择了合适的集总参数并对减温水环节进行了简化处理。考虑了工质的吸热膨胀过程对过热器差压特性的影响,建立了过热器差压与汽水分离器压力之间的函数关系。在保证有效信息含量的前提下,得到了最简控制模型结构。在此基础上增加划分区段数,并建立了较高阶次的直流炉机组通用非线性模型结构。
建立了具有最简结构的超超临界机组控制模型。提出了一种基于机理模型结构的开环辨识数据选择方法,并证明了火电机组的闭环可辨识性。基于此以泰州1000MW和平海1000MW超超临界机组为应用对象,确定了模型参数,并验证了模型的准确性。对具有不同结构复杂度的模型进行精度比较表明,具有最简结构的模型精度和复杂度比最高,具有最佳结构,是要建立的控制模型。从而实现了最简模型结构向最佳模型结构转化,最佳模型结构向控制模型结构转化。
运用多源信息融合技术和机理分析方法,基于本文控制模型中重构的2个重要状态变量信号——主蒸汽流量和热量信号,构造了适用于超超临界机组的再热蒸汽流量、燃煤发热量和氧量信号,以用于机组控制优化。
Prerequisites for control optimization of ultra-supercritical unit are to analyze the characteristics of ultra-supercritical unit in depth, build the model for control and obtain accurate signals. The model for control should be simple, and possess favorable accuracy. Some state variable signals needed by control optimization cannot be obtained with traditional methods. Those signals could be gotten through constructing their relationships with other known signals. The constructed signals should be accurate and easy to realize. To achieve the goals and take modeling as paramount, the following works have been carried out in this thesis.
A scheme based on hybrid modeling method for control model of ultra-supercritical unit is proposed. Models built with single modeling method always have high complexity, low accuracy or low commonality. However, the hybrid modeling method could take advantage of both methods. When using the hybrid modeling method, there is overcompensation or undercompensation of data information for model structure. In order to get the maximum use out of data information, and simplify the structure of control model to the fullest extent as well, the best model structure was defined and control model built problem was therefore transferred to the best structure built problem.
The simplest model structure containing the most effective information is given which is critical for obtention of the best model structure. In order to evade determination of the transition point position in an effective way and minimize the possible number of compartment with fixed boundary when deriving a general lumped compartmental model structure that respects the conservation of energy, momentum, and mass, suitable lumped parameters are chosen for the compartment of the boiler including two-phase region. And then another lumped model structure was developed with the sub-minimal number of compartment in the same way.
The control model of ultra-supercritical unit possessing the most simplified model structure is built. A data selection method for open-loop identification based on model structure was developed and close-loop identifiability of power unit was proved as well. Parameters of models were determined with close-loop operation data of Taizhou1000MW ultra-supercritical unit and open-loop experimental data of Pinghai1000MW ultra-supercritical unit. The models were verified by data comparison experiments. Models with different structures were compared in accuracy and the ratio of accuracy and structure complexity of each model was analyzed. Results indicate that the model with the most simplified structure has the highest ratio of accuracy and structure complexity and it can be used as control model.
Finally, based on multi-sensors information fusion technique and mechanism analysis, reheat steam flow signal, coal calorific value signal and oxygen content of exhaust gas signal are constructed for control optimization of ultra-supercritical unit using two important state signals constructed in the control model which are main steam flow signal and boiler heat signal. The constructed signals were verified with operation data.
提出了基于复合建模方法的超超临界机组控制模型建立方案。采用单一建模方法会造成模型复杂度高,模型精度不够或通用性低的问题,而复合建模方法将两种基本方法结合起来,可以充分发挥各自优势并且互相弥补不足。运用复合建模方法建模时,存在模型结构不合适造成的数据信息过补偿或欠补偿问题。为了能够在精度要求范围内最大限度地利用从数据中提取的信息,简化模型结构,定义了最佳模型结构,将控制模型建立问题转化为最佳模型结构建立问题。
建立了包含最多有效信息的直流炉机组最简控制模型结构。基于合理的简化假设建立包含最多机组有效信息的最简控制模型结构是找到最佳模型结构的关键。当采用分区集总方法建立直流炉模型时,为了有效规避相变点位置问题研究并使划分区段数合理地降至为最小值,运用理论和数据分析为包含两相区的固定边界区段选择了合适的集总参数并对减温水环节进行了简化处理。考虑了工质的吸热膨胀过程对过热器差压特性的影响,建立了过热器差压与汽水分离器压力之间的函数关系。在保证有效信息含量的前提下,得到了最简控制模型结构。在此基础上增加划分区段数,并建立了较高阶次的直流炉机组通用非线性模型结构。
建立了具有最简结构的超超临界机组控制模型。提出了一种基于机理模型结构的开环辨识数据选择方法,并证明了火电机组的闭环可辨识性。基于此以泰州1000MW和平海1000MW超超临界机组为应用对象,确定了模型参数,并验证了模型的准确性。对具有不同结构复杂度的模型进行精度比较表明,具有最简结构的模型精度和复杂度比最高,具有最佳结构,是要建立的控制模型。从而实现了最简模型结构向最佳模型结构转化,最佳模型结构向控制模型结构转化。
运用多源信息融合技术和机理分析方法,基于本文控制模型中重构的2个重要状态变量信号——主蒸汽流量和热量信号,构造了适用于超超临界机组的再热蒸汽流量、燃煤发热量和氧量信号,以用于机组控制优化。
Prerequisites for control optimization of ultra-supercritical unit are to analyze the characteristics of ultra-supercritical unit in depth, build the model for control and obtain accurate signals. The model for control should be simple, and possess favorable accuracy. Some state variable signals needed by control optimization cannot be obtained with traditional methods. Those signals could be gotten through constructing their relationships with other known signals. The constructed signals should be accurate and easy to realize. To achieve the goals and take modeling as paramount, the following works have been carried out in this thesis.
A scheme based on hybrid modeling method for control model of ultra-supercritical unit is proposed. Models built with single modeling method always have high complexity, low accuracy or low commonality. However, the hybrid modeling method could take advantage of both methods. When using the hybrid modeling method, there is overcompensation or undercompensation of data information for model structure. In order to get the maximum use out of data information, and simplify the structure of control model to the fullest extent as well, the best model structure was defined and control model built problem was therefore transferred to the best structure built problem.
The simplest model structure containing the most effective information is given which is critical for obtention of the best model structure. In order to evade determination of the transition point position in an effective way and minimize the possible number of compartment with fixed boundary when deriving a general lumped compartmental model structure that respects the conservation of energy, momentum, and mass, suitable lumped parameters are chosen for the compartment of the boiler including two-phase region. And then another lumped model structure was developed with the sub-minimal number of compartment in the same way.
The control model of ultra-supercritical unit possessing the most simplified model structure is built. A data selection method for open-loop identification based on model structure was developed and close-loop identifiability of power unit was proved as well. Parameters of models were determined with close-loop operation data of Taizhou1000MW ultra-supercritical unit and open-loop experimental data of Pinghai1000MW ultra-supercritical unit. The models were verified by data comparison experiments. Models with different structures were compared in accuracy and the ratio of accuracy and structure complexity of each model was analyzed. Results indicate that the model with the most simplified structure has the highest ratio of accuracy and structure complexity and it can be used as control model.
Finally, based on multi-sensors information fusion technique and mechanism analysis, reheat steam flow signal, coal calorific value signal and oxygen content of exhaust gas signal are constructed for control optimization of ultra-supercritical unit using two important state signals constructed in the control model which are main steam flow signal and boiler heat signal. The constructed signals were verified with operation data.
引文
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