摘要
为提高电厂监盘智能化水平,以大数据技术为支撑,依托各类先进的机器学习算法,提出一种基于大数据挖掘与规则引擎的智慧辅助监盘系统。该系统建立发电企业各类系统和设备的预警模型,能精准发现参数与设备状态异常,降低运行人员监盘压力;在设备故障早期进行预警,并对所发生预警信息进行故障识别与故障处理指导。测试结果表明,该系统有利于实现对电厂设备状态的全面监测,进一步提高机组运行水平和设备健康水平。
With the purpose of improving the intelligent level of power plant monitoring,this paper introduces an intelligent auxiliary monitoring system based on big data mining and rule engine,supported by big data technology and relying on various advanced machine learning algorithms.The system establishes the early warning model of all kinds of systems and equipment in power generation enterprises,which can accurately detect abnormal status of parameters and equipment,and reduce the monitoring pressure of operators.Early warning should be carried out in the early stage of equipment failure,and fault identification and troubleshooting guidance should be given to the early warning.The test results show that the system is beneficial to realize more comprehensive monitoring of equipment status in power plant,and it can further improve the operation level of units and the health level of equipment.
作者
周慧
王瑶
罗仁强
党俊榜
肖振馨
赵航
ZHOU Hui;WANG Yao;LUO Renqiang;DANG Junbang;XIAO Zhenxin;ZHAO Hang(Hunan Datang Xianyi Technology Co.,Ltd,Changsha 410004,China;Datang Shaanxi Power Generation Co.,Ltd.Yan'an Thermal Power Plant,Yan'an 716004,China)
出处
《湖南电力》
2023年第3期77-81,共5页
Hunan Electric Power
关键词
大数据技术
机器学习
智慧辅助监盘
故障识别与处理指导
big data technology
machine learning
intelligent auxiliary monitoring
fault identification and troubleshooting guidance
作者简介
通讯作者:周慧(1990),女,工程师,从事智慧电厂相关产品开发工作。
