适应大数据时代要求的电力设备异常状态检测方法
Anomaly detection methods for electrical equipment in the big data era
章日华 1梅彦 1周丹 1肖治中1
作者信息
- 1. 国网上海市电力公司市区供电公司,上海200080
- 折叠
摘要
随着电力设备状态监测技术的发展,可收集的状态监测数据日益增多,如何从这些海量数据中提取有用信息,提升对设备异常状态的检测能力,以便及时处理问题设备、防患于未然,提高系统供电的可靠性正成为人们日益关注的问题.总结了趋势分析法和动态建模法两类方法,为适应大数据时代的电力设备异常状态检测方法指明了发展方向.
Abstract
An increasing number of condition monitoring data are accumulated with the development of condition monitoring techniques for electrical equipment.Methods for effective extracting useful information from a large pool of data are becoming a concern nowadays.By taking those methods the capability of anomaly detection for equipment can be enhanced so as to ensure the reliability of power supplying.The paper summarizes two types of methods,namely trend analysis and dynamic modeling,based on which the direction for anomaly detection methods in the big data era is then presented.
关键词
大数据/异常状态检测/趋势分析/动态建模Key words
big data/anomaly detection/trend analysis/dynamic modeling引用本文复制引用
出版年
2014
NETL
NSTL
万方数据