摘要
火电厂分散控制系统(DCS)数据异常直接影响机组安全稳定与经济运行。本文针对DCS数据异常问题,系统梳理了常见异常类型,构建了融合规则引擎、统计过程控制与机器学习的多层次识别方法体系。重点阐述了检测、确认、处理、验证、记录的标准化闭环处理流程,强调运行干预、仪表校验与系统维护的作用,为火电厂智能监盘与运行优化提供关键技术支撑。研究对保障机组安全、提升经济效益及支撑“双碳”目标具有重要实践意义。
Data anomalies in the Distributed Control System(DCS)of thermal power plants directly affect the safe,stable,and economic operation of generating units.This paper systematically categorizes common types of DCS data anomalies and establishes a multi-layered identification methodology integrating rule engines,statistical process control,and machine learning techniques.It emphasizes a standardized closed-loop process encompassing anomaly detection,confirmation,handling,verification,and recording,and highlights the importance of operational intervention,instrument calibration,and system maintenance.The study provides essential technical support for intelligent monitoring and operational optimization in thermal power plants.Its findings have significant practical value in ensuring unit safety,enhancing economic performance,and supporting the realization of the“dual carbon”goals.
作者
杨洪芳
YANG Hongfang(Guizhou Panjiang Xinguang Power Generation Co.,Ltd.,Panzhou 553507,China)
出处
《锅炉制造》
2025年第5期62-64,共3页
Boiler Manufacturing
