【摘要】 随着新能源发电的不断并网，火电厂对灵活运行的需求日益增加。然而，灵活性的提升往往会导致经济性的下降。为提高火电机组的运行经济性，建立了机理结合数据驱动的直接空冷机组冷端数字孪生模型，利用实时数据辨识孪生模型中的关键动态参数并提出了一种基于孪生模型的最优背压计算方法。该方法依托高精度的孪生模型，通过历史数据回归技术修正背压寻优过程中的关键参数，借助关联度分析，预估不同条件下最优风机转速的存在区间，并利用区间自适应粒子群算法进行净功率适应度函数寻优，从而实现了基于物理实体动作规律的最优背压和相应风机转速的快速寻优。所提出的最优背压计算方法已应用于600 MW直接空冷机组。研究结果表明：数字孪生技术的应用可提升机理模型的准确度，与传统拟合模型参数方法相比，输出结果的平均相对误差下降至2%以内，Pearson相关系数达到0.9以上。根据以上方法得到的最优背压及相应风机转速可为电厂提供运行指导，有助于降低燃料消耗，提高机组经济性，预计每天可节约标准煤6.82 t。更多还原

【Abstract】 As renewable energy generation continues to integrate into the grid, the demand for flexible operation in coal-fired power plants has significantly increased. However, increasing flexibility often results in decreased economic efficiency. In order to improve the operational economy of coal-fired power plants, a mechanism-driven, data-driven digital twin model for direct air-cooled units was established. Real-time data was utilized to identify key dynamic parameters in the twin model, and an optimal backpressure calculation method was proposed based on this model. This method leveraged a high-precision twin model to correct key parameters in the backpressure optimization process through historical data regression. Correlation analysis was employed to estimate the existing range of optimal fan speeds under different conditions, and an adaptive particle swarm optimization algorithm was used to optimize the net power fitness function. This enabled rapid optimization of optimal backpressure and corresponding fan speed based on the physical action laws of the system. The proposed optimal backpressure calculation method was applied to a 600 MW direct air-cooled unit. Research results show that the application of digital twin technology enhances model accuracy, reducing the average relative error of output results to below 2% compared to traditional fitting methods, with a Pearson correlation coefficient exceeding 0.9. It is concluded that the optimal backpressure and corresponding fan speed obtained through the above methods provide operational guidance for power plants, contributing to reduced fuel consumption and improved economic efficiency of the units. It is estimated that 6.82 tons of standard coal are saved daily.更多还原