摘要
针对华电铁岭电厂6~#机组DCS系统中的负荷升降速率低、关键参数波动大及系统不能很好适应煤种变化等实际问题,通过有机融合预测控制技术、神经网络学习技术及自适应控制技术,将INFIT系统投入AGC优化控制系统中。投入INFIT系统后,经过多次反复升降试验,使得机组的主汽压力、过热度、主汽温度的控制平稳程度相比投入INFIT系统前有了非常明显的改善。
The original DCS control performance of the 6~# unit in Huadian Tieling Power Plant is very poor such as slowload regulating rate,serious critical parameters fluctuation and poor response to the coal type changing. The INFIT system was put into AGC optimal system through rational fusion predictive control technology,neural network learning and adaptive control technology. Experimental results showed that the steady-state of steam temperature control system and dynamic performance were significantly improved.
出处
《沈阳工程学院学报（自然科学版）》
2017年第2期185-189,共5页
Journal of Shenyang Institute of Engineering:Natural Science
基金
沈阳工程学院科技基金项目(LGXS-1515)
