文献类型：期刊文献

中文题名：基于大数据技术的电厂设备状态评估和预警应用研究

英文题名：State assessment and early warning application for power plant equipment based on big data technology

作者：胡杰[1];唐静[2,3];谢仕义[1];王云[4]

机构：[1]广东海洋大学数学与计算机学院,广东湛江524088;[2]北京石油化工学院信息工程学院,北京102617;[3]远光软件股份有限公司,广东珠海519085;[4]华电环保系统工程有限公司,北京100070

年份：2020

卷号：42

期号：2

起止页码：1

中文期刊名：华电技术

外文期刊名：Huadian Technology

基金：广东省教育厅创新强校项目(Q18286);中海油能源发展股份有限公司科技项目(CY-2J-19-2C-005)。

语种：中文

中文关键词：多元状态评估;大数据技术;故障诊断;贡献率分析;制粉系统

外文关键词：multivariate state estimation;big data technology;fault diagnosis;contribution rate analysis;pulverizer system

中文摘要：为了避免电厂设备在运行期间出现异常状态直接或间接导致机组停机增加维护成本,提出了一种基于大数据技术的设备状态评估和预警方法。多元状态估计技术是该方法实现设备故障诊断和健康管理(PHM)的可行技术之一,它的实现依赖海量健康数据的训练学习。基于大数据技术对历史状态数据离线学习并训练健康状态评估模型,针对目标设备实时分析相关参数的残差值变化,通过滑动窗口残差统计法自动检测偏差情况,实现目标设备异常状态的在线监测。以某电厂火电机组的制粉系统为例进行状态评估和健康诊断研究,引入参数贡献率来表征引起异常的强弱因素,进一步推进了对设备状态和故障问题的分析,试验结果表明该方法能够有效地进行电厂设备状态评估和设备故障预警。

外文摘要：In order to avoid the increase of maintenance cost in power plants directly or indirectly resulting from the abnormal conditions and shutdown of equipment during operation,a state assessment and early warning application for power plant equipment based on big data technology is proposed.Multivariate state assessment is one of the feasible technologies to realize equipment Prognostic and Health Management(PHM),and its implementation relies on training and learning of massive health data.Offline training of historical status data is made to establish health state assessment model based on big data technology.Making real-time analysis on the changes of related parameter residual values of the targeted equipment and taking automatic detection by sliding window residual statistics method can realize online monitoring on abnormal status of targeted equipment.Taking the state assessment and health diagnosis of the pulverizing system in a thermal power plant as an example,the parameter contribution rate is introduced to characterize the strength and weakness of factors leading to the anomalies,which is helpful in making further analysis on equipment status and fault.The experimental results show that this method can effectively evaluate the state and make fault warning for power plant equipment.