文献类型：期刊文献

英文题名：Fault Diagnosis of Power IoT System Based on Improved Q-KPCA-RF Using Message Data

作者：Jiang, Haoyu[1];Chen, Kai[2];Ge, Quanbo[3];Wang, Yun[2];Xu, Jinqiang[1];Li, Chunxi[2]

机构：[1]Guangdong Ocean Univ, Sch Elect & Informat Engn, Zhanjiang 524088, Guangdong, Peoples R China;[2]Shanghai Maritime Univ, Coll Logist Engn, Shanghai 200135, Peoples R China;[3]Tongji Univ, Sch Elect & Informat Engn, Shanghai 200092, Peoples R China

年份：2021

卷号：8

期号：11

起止页码：9450

外文期刊名：IEEE INTERNET OF THINGS JOURNAL

收录：SCI-EXPANDED(收录号：WOS:000652798400057)、、EI(收录号：20210809935901)、Scopus(收录号：2-s2.0-85100841122)、WOS

基金：The work was supported in part by the National Natural Science Foundation of China under Grant 61803136.

语种：英文

外文关键词：Communication message; power Internet of Things (IoT) system; Q learning; random forest (RF)

外文摘要：As the power system develops from informatization to intelligence. Research on data services based on the Internet of Things (IoT) focuses more on application functions, but the research on the data quality of the IoT itself is insufficient. Long-term continuous operation of the big data IoT system has the risk of performance degradation or even partial fault, which leads to a decrease in the availability of collected data for intelligent analysis. In this article, based on the power IoT message data, the characteristics are established through a variety of improved detection methods, and then the abnormal data type is obtained through Q learning and fusion of the random forest (RF) identification features. Finally, the topology of the specific power user IoT system is combined with kernel principal component analysis (KPCA) + improved RF algorithm getting the abnormal location of the IoT. The results show that the research method has a significantly higher positioning accuracy (from 61% to 97%) than the traditional RF method, and the combination method has more advantages in parameter adjustment and classification accuracy than directly using a multilayer perceptron (MLP).