1000MW煤电机组调峰中汽温控制策略研究  

Research on Steam Temperature Control Strategy in Peak Regulation of 1000MW Coal Power Unit

作　　者：闫修峰 宗珂 何修年 高林[2] 秦斌 王明坤 惠文涛 YAN Xiufeng;ZONG Ke;HE Xiunian;GAO Lin;QIN Bin;WANG Mingkun;HUI Wentao(Huadian Zouxian Power Corporation Ltd.,Zoucheng 273522,Shandong Province,China;Xi'an Thermal Power Research Institute Co.,Ltd.,Xi’an 710054,Shaanxi Province,China)

机构地区：[1]华电邹县发电有限公司,山东省邹城市273522 [2]西安热工研究院有限公司,陕西省西安市710054

出　　处：《发电技术》2022年第3期518-522,共5页Power Generation Technology

基　　金：国家重点研发计划项目(2019YFB1505400)。

摘　　要：在燃煤机组快速调峰、调频的响应过程中,将汽温快速地控制在合理的范围区间是自动控制领域面临的难点之一。由于汽温具有大惯性、大延迟特性,采用常规的串级控制器往往达不到较好的控制效果。同时依赖模型精度的预测控制与神经网络等高级算法,受汽温控制对象非线性、测量不确定性以及时变特性的影响,限制了其在实际控制中的应用。为此,提出了一种基于串级与局部Smith预估的混合控制模型,通过参数辨识获得局部Smith预估的精确模型,构建了混合结构的汽温控制策略,并于某1000 MW超超临界锅炉减温水系统中进行应用,结果表明:该方法高效、易用、稳定,可有效地改善汽温控制性能,达到良好的控制效果。In the response process of rapid peak regulation and frequency regulation of coal-fired units,quickly controlling the steam temperature within a reasonable range is one of the difficulties in the field of automatic control.Due to the large inertia and large delay characteristics of steam temperature,the conventional cascade control often fails to achieve better control results.Meanwhile,advanced algorithms such as model prediction and neural networks are affected by non-linearity,measurement uncertainty and time varying characteristics of temperature object,which limits their application in actual control.This paper proposed a hybrid control model based on cascade and local Smith prediction.The accurate local Smith prediction model was obtained through parameter identification method,and the steam temperature control strategy based on the hybrid structure was constructed and applied in the 1000 MW ultra supercritical boiler desupercritical water system.The results show that the method is efficient,easy to use and stable,which can effectively improve the performance of steam temperature control and achieve good control effects.

关 键 词：1000MW超超临界锅炉 燃煤机组 汽温控制 局部Smith预估 调峰 调频 

分 类 号：TK223[动力工程及工程热物理—动力机械及工程] TM621[电气工程—电力系统及自动化]