摘要
大容量、高参数、低能耗的百万千瓦超超临界机组是燃煤发电领域的重大装备,已成为全国电力工业发展的主流方向,其安全可靠运行对推动发电企业转型升级具有重要意义.本文从分析以百万千瓦超超临界机组为代表的燃煤发电装备的本质特性出发,揭示了其变负荷深度调峰导致的非平稳运行特性和全流程复杂耦合特性,总结了燃煤发电过程区别于一般连续过程的问题,指出了研究燃煤发电装备运行工况监控算法的必要性.进而,基于这些特性,我们对面向燃煤发电装备工况监控的数据驱动算法近30年的发展进行了回顾和分析,展示了算法发展的不同阶段.在此基础上,梳理了目前燃煤发电装备工况监控中存在的问题,并进一步介绍了燃煤发电装备工况监控未来可能的发展方向.
As major equipment in coal-fired power generation,1000 MW ultra-supercritical unit has advantages of large capacity,high parameter and low energy consumption,which has become the mainstream of the development of the power industry in China.Its safety and reliability in operation are of great significance to promote the transformation and upgrading of power generation enterprises.Starting from the analysis of essential characteristics of coal-fired power generation equipment,this article revealed the nonstationary operation characteristics caused by the variable load,deep peak shaving,and the complex correlation characteristics of the whole process.Then,it summarized the problems that the coal-fired power generation process is different from general continuous processes,and points out the necessity of studying monitoring algorithms for coal-fired power generation equipment.Furthermore,based on these characteristics,it reviewed and analyzed the development of the data-driven algorithms for coal-fired power generation equipment monitoring in the past 30 years,showing different stages of algorithm development.On this basis,this article presented the current problems in operation monitoring of coal-fired power generation equipment,and further introduced the possible development direction in the future.
作者
赵春晖
胡赟昀
郑嘉乐
陈军豪
ZHAO Chun-Hui;HU Yun-Yun;ZHENG Jia-Le;CHEN Jun-Hao(State Key Laboratory of Industrial Control Technology,College of Control Science and Engineering,Zhejiang University,Hangzhou 310027)
出处
《自动化学报》
EI
CAS
CSCD
北大核心
2022年第11期2611-2633,共23页
Acta Automatica Sinica
基金
国家自然科学基金−浙江省两化融合基金(U1709211)
国家自然科学基金杰出青年基金(62125306)
国家自然科学基金重点项目(62133003)
工业控制技术国家重点实验室自主课题(ICT2021A15)资助。
关键词
燃煤发电装备
变负荷
非平稳
工况监控
机器学习
Coal-fired power generation equipment
varying load
nonstationary
condition monitoring
machine learning
