机制和数据驱动的双重估计合液压热状态的蒸汽加热网络考虑到多时间尺度的特征
IEEE transactions on cybernetics
|September 25, 2023
概括
本研究引入了蒸汽加热网络 (SHN) 的双估计方法,可以准确地捕获液压和热状态. 该方法提高了工业能源系统的计算效率.
科学领域:
- 能源系统工程 能源系统工程
- 过程控制 过程控制
- 计算流体动力学的流体动力学.
背景情况:
- 蒸汽供热网络 (SHN) 是工业园区能源系统的关键组成部分,需要精确的状态估计,以确保安全和经济的运行.
- 由于单个时间尺度的考虑,现有的合估计方法经常面临计算挑战.
研究的目的:
- 提出一种新的双重估计方法,以准确有效地评估SHN的合液压热动态状态.
- 解决SHN分析中单个时间尺度估计方法的局限性.
主要方法:
- 开发了一种数据驱动的双顺序估计 (DSE) 方法,利用水力和热过程的两个不同的时间尺度.
- 使用节点转换矩阵实现分布式交互策略,以提高大规模系统的效率.
主要成果:
- 拟议的双估计方法在快速的液压工艺估计过程中有效捕捉缓慢的热动态.
- 在单一管道和两个工业超热蒸汽网络上进行验证,证明了卓越的性能.
- 与现有技术相比,该方法在估计准确性和计算效率之间实现了最佳平衡.
结论:
- 双顺序估计方法在SHN状态监测中提供了显著的进步.
- 这种方法通过提高估计准确性和效率来提高工业能源系统的运行安全性和经济可行性.
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