基于大数据挖掘的火电厂辅机状态评估系统  

Power Plant Auxiliary State Assessment System Based on Big Data Mining

作　　者：张苏闽 邓敏强 朱静 邓艾东 Zhang Sumin;Deng Minqiang;Zhu Jing;Deng Aidong(China Energy Jiangsu Power Co.,Ltd,Nanjing 210036,China;National Engineering Research Center of Turbo-Generator Vibration,School of Energy and Environment,Southeast University,Nanjing 210096,China)

机构地区：[1]国家能源江苏电力有限公司,南京210036 [2]东南大学能源与环境学院火电机组振动国家工程研究中心,南京210096

出　　处：《信息化研究》2020年第1期64-69,共6页INFORMATIZATION RESEARCH

基　　金：国家能源集团总部科技项目(GJNY-18-01)。

摘　　要：设备的智能化状态评估和预测维修是构建智慧能源的核心要素之一。以转子动力学、现代信号处理和人工智能技术为基础,开发了基于历史大数据挖掘和人工智能算法的火电厂辅机状态评估系统。该系统采用分布式数据采集器构建辅机状态监测网,通过多源信息深度融合分析技术,实现了火电厂主辅机运行状态的智能评估,现已成功应用于国内某大型火电厂。Intelligent state assessment and predictive maintenance of equipment is one of the core elements of building a smart energy system.Based on rotor dynamics,modern signal processing and artificial intelligence technology,a state evaluation system for thermal power plant auxiliary machinery based on historical big data mining and artificial intelligence algorithm was developed.This system uses distributed data collector to construct auxiliaries condition monitoring network.Through multi-source information deep fusion analysis technology,it realizes intelligent evaluation of main and auxiliaries in thermal power plants.It has been successfully applied to a large thermal power plant in China.

关 键 词：状态评估 趋势预测 智慧能源 大数据 智慧电厂 

分 类 号：TK38[动力工程及工程热物理—热能工程]