基于大数据挖掘的发电设备状态监测与故障诊断系统的开发与应用  

Development and Application of Power Generation Equipment Condition Monitoring and Fault Diagnosis System Based on Big Data Mining

作　　者：卞韶帅 朱懿灏 刘峻[2] 叶颖俊[2] 倪东升 黄新 李存霖 蒋欢春 BIAN Shaoshuai;ZHU Yihao;LIU Jun;YE Yingjun;NI Dongsheng;HUANG Xing;LI Cunlin;JIANG Huanchun(Shanghai Minghua Electric Power Science&Technology Co.,Ltd.,Shanghai 200090,China;Shanghai Waigaoqiao Power Generation Co.,Ltd.,Shanghai 200137,China)

机构地区：[1]上海明华电力科技有限公司,上海200090 [2]上海外高桥发电有限责任公司,上海200137

出　　处：《电力与能源》2020年第5期571-574,581,共5页Power & Energy

摘　　要：为实现对发电设备故障及运行方式的预警和分析,探索发电集团技术中心技术服务模式优化与创新。通过融合工业互联网、大数据等技术,将各电厂设备的运行数据进行采集、清洗、存储,建立了各类数据挖掘模型、设备分析模型,开发了基于大数据挖掘的发电设备状态监测与故障诊断系统。该系统覆盖了发电企业的主设备、重要辅机及相关工艺;建立了以国产工业实时数据库为数据存储核心的百万点大数据库;实现了8个电厂、22台机组、40多万点数据的整理、采集及存储;开发并应用了火电厂冷端设备诊断分析、锅炉燃烧热偏差监测及诊断、设备劣化趋势预警等模块,为电厂安全、环保、经济运行提供了有力支撑。In order to realize the early warning and diagnosis of power generation equipment fault and the operation mode,the optimization and innovation of technical service mode of power generation group technology center are explored.Through the integration of industrial internet,big data and other technologies,the equipment operation data of each power plant is collected,cleaned and stored,established various data mining models and equipment analysis models,developed a power generation equipment condition monitoring and fault diagnosis system based on big data mining.The system covers the main equipment,important auxiliary equipment and related process;We set up megapoint database with domestic industrial real-time database as data storage core;We made it that the data of 8power plants,22units and over 400,000data points were sorted,gathered and stored;We developed and applied the modules of diagnosis and analysis of cold end equipment in thermal power plant,monitoring and diagnosis of boiler combustion thermal deviation,and early warning of equipment deterioration trend.It provides strong support for power plant to safe,environmental and economic operation.

关 键 词：大数据 发电设备 状态监测 故障诊断 技术服务模式 

分 类 号：TP277[自动化与计算机技术—检测技术与自动化装置]