约束(计算机辅助设计)
模式(计算机接口)
航程(航空)
过热
过热蒸汽
功能(生物学)
功率(物理)
控制理论(社会学)
计算机科学
理论(学习稳定性)
火力发电站
机制(生物学)
工程类
锅炉(水暖)
控制(管理)
人工智能
机械工程
操作系统
机器学习
物理
认识论
哲学
航空航天工程
进化生物学
量子力学
废物管理
凝聚态物理
生物
作者
Peng Wang,Fengqi Si,Yue Cao,Zhuang Shao,Shaojun Ren
标识
DOI:10.1016/j.applthermaleng.2021.117899
摘要
The stability of superheated steam temperature (SST) is severely challenged by the adjustment of thermal power plants under a wide-load range. Accurate and efficient prediction of SST plays an important role in the control of superheat system. To this end, an SST prediction model based on a multi-mode integrated method is proposed in this paper. Firstly, conservation of energy, as an equality constraint, is introduced into the loss function of the data-driven model based on Long Short-Term Memory (LSTM) architecture. Subsequently, the physical relationship between SST and the spray water flow, as an inequality constraint, is introduced into the above loss function. Finally, an individual hybrid model for each operating mode is developed and integrated with multi-mode switching strategies based on attention mechanism. Operating data with a wide-load range is sampled from a supervisory information system (SIS) to validate the superiority of the proposed method. The comparison results demonstrated that the predictive effect of the hybrid model with physics-based loss function is not only generalizable but also scientifically consistent with the dynamic characteristics of the step response. Furthermore, the results of the multi-mode integrated model prove the effectiveness of the mode switching strategy based on the attention mechanism.
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