Mechanism and Data-Driven Dual Estimation of Coupling Hydraulic–Thermal States for Steam Heating Networks Considering Multi-Time-Scale Characteristics
针对蒸汽供热管网中水力和热力过程的不同时间尺度特性，提出一种机理与数据驱动相结合的双重估计方法，在快速水力状态估计中同时捕捉慢速热力动态，并通过节点变换矩阵的分布式交互策略提升大规模系统的计算效率。
For a steam heating network (SHN), which is one of the most important parts of the integrated energy systems in an industrial park, it is essential to provide accurate and reliable estimation of hydraulic and thermal states, to ensure its operational safety and economy. Considering two different time-scale characteristics of the hydraulic and thermal processes in the SHN, this article proposes a mechanism and data-driven dual estimation method for the coupling hydraulic-thermal dynamic states. Due to the fact that the existing coupled estimation methods with one time scale may suffer from heavy computational burden, a data-driven dual sequential acrlong DSE method of the SHN is proposed based on the two-time scales, in which the dynamics of slow thermal states can also be captured when performing the acrlong SE of the fast hydraulic process. Furthermore, to improve the computational and communicational efficiency, a distributed interaction strategy based on the nodal transformation matrix is designed for large-scale steam systems. To verify the effectiveness of the proposed method, a single pipeline system and two real-world industrial superheated steam networks are employed. Compared to other state-of-the-art methods, the proposed method achieves the best tradeoff between the estimation accuracy and computational efficiency.