数字孪生是“碳达峰、碳中和”目标下火电机组节能减排和提质增效的重要途径，也是支撑电力系统低碳转型的前沿技术。本文围绕火电机组数字孪生开发中的关键技术，研究了以数据协调为基础的火电机组热力系统在线测量数据筛选与处理方法和热力系统的高精度建模与仿真方法。准确的在线测量数据和热力计算是火电机组实现数字孪生的基础。在火电机组的运行区间加宽、运行工况日益复杂的背景下，本文研究了适用于运行全工况范围的热力系统数据协调方法。可以用于确定热力计算的基准流量，完成机组运行全工况范围的热力计算，降低关键测量数据和关键未测参数估算的不确定度，提高热力计算的精度，为火电机组的高精度建模与仿真奠定基础。针对火电机组汽轮机侧在线测量数据的显著误差难以检验的问题，本文提出了应用不等式约束检验显著误差的数据协调方法，建立了基于数据协调的汽轮机侧显著误差检验的框架。在案例研究中，所提出的方法能够有效检验汽轮机侧测量数据的显著误差。在剔除显著误差后，所建立的数据协调模型进一步提高了机组热力计算的精度。热力系统高精度的建模和仿真是火电机组数字孪生开发的关键。本文基于结合物理机理和运行数据的混合建模方法，提出了火电机组热力系统的全工况建模框架。在案例研究中，基于所建立的热力系统模型可以实现与机组实际运行数据高度匹配的仿真，能够定量预测关键部件特性变化后热力系统的运行状态，为火电机组数字孪生的开发提供了模型支撑。基于热力系统模型的仿真方法存在误差累积、中间运行变量的模型仿真值与在线测量值不一致和难以有效利用在线测量数据等局限性。针对这些局限性，本文提出了基于数据协调的在线仿真思想和方法。其核心思想是将热力系统模型的仿真结果视为一类具有不确定度的估计，与机组的在线测量数据在热力系统层面进行数据协调，得到更准确的仿真结果。在案例研究中，基于热力系统全工况模型得到的关键特性参数被视为虚拟测量值，与机组的在线测量数据一起进行热力系统层面的数据协调，降低了热力系统和关键部件特性参数估计的不确定度，提高了仿真精度。这为火电机组数字孪生的开发提供了一种新的建模仿真思路。
Digital twin plays an important role to save energy, reduce emissions, improve the quality and efficiency of thermal power plants under the goals of "carbon peak and carbon neutral". It is also a cutting-edge technology to support the low-carbon transition of power systems. Focusing on the key technologies for the digital twin development of thermal power plants, online measured data screening and processing methods of thermal power plants based on data reconciliation, and high-precision modelling and simulation methods of thermal power plant systems are studied.Accurate online measured data and thermal calculation are the basis for the digital twin development of thermal power plants. In the context of widening of the working range and increasingly complex working conditions of thermal power plants, a data reconciliation method of thermal system applicable to the full working range is studied, which can be used to determine the reference flow rate of thermal calculation, complete the thermal calculation under the full working range, reduce the uncertainty of key measured data and the estimation of key unmeasured parameters, and improve the accuracy of thermal calculation. It lays the foundation for high-precision modelling and simulation of thermal power plants.In order to solve the problem that it is difficult to detect the gross error of the online measured data of the steam turbine system in thermal power plants, a method of applying inequality constraints to detect gross errors is proposed, and a framework for gross error detection of the steam turbine system based on data reconciliation is established. In the case study, the proposed method can effectively detect the gross error in the measured data of the steam turbine system. After removing the gross error, the proposed data reconciliation model further improves the thermal calculation accuracy of the power plant.High-precision modelling and simulation of thermal system is the key to the digital twin development of thermal power plants. In this dissertation, a full working-range modelling framework for the thermal system of thermal power plants based on the hybrid modelling method that combines physical mechanisms and operation data is proposed. In the case study, the simulation can be highly matched with the actual operation of the power plant based on the established thermal system model, and the operation state of the thermal system after characteristic changes of key components can be quantitatively predicted. This provides the model support for the digital twin development of thermal power plants.The simulation method based on thermal system models has limitations including error accumulation, inconsistency between model simulation values and online measured values of intermediate operation variables and insufficient use of online measured data. In order to solve these limitations, an online simulation concept and method based on data reconciliation are proposed. The key concept is to treat simulation results of the thermal system model as a kind of estimation with uncertainty, and reconcile them with online measured data of the power plant at the thermal system level to obtain more accurate simulation results. In the case study, key characteristic parameters obtained from the full working-range model of the thermal system are regarded as virtual measurements, which are reconciled with the online measured data of the power plant at thermal system level, reducing the estimation uncertainty of the characteristic parameters of the thermal system and key components, and improving the simulation accuracy. This provides a new concept of modelling and simulation for the digital twin development of thermal power plants.
