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).

中文翻译：

---

基于改进Q-KPCA-RF的消息数据的电力物联网系统故障诊断

随着电力系统从信息化发展到智能化。基于物联网（IoT）的数据服务的研究更多地集中在应用程序功能上，但是对IoT本身的数据质量的研究还不够。大数据IoT系统的长期连续运行可能会导致性能下降甚至部分故障的风险，从而导致用于智能分析的收集数据的可用性下降。本文基于强大的IoT消息数据，通过多种改进的检测方法来建立特征，然后通过以下方法获得异常数据类型 $ Q $ 学习和融合随机森林（RF）识别功能。最后，将特定电力用户物联网系统的拓扑与内核主成分分析（KPCA）+改进的RF算法相结合，以获取物联网的异常位置。结果表明，该研究方法比传统的射频方法具有更高的定位精度（从61％到97％），并且与直接使用多层感知器（MLP）相比，组合方法在参数调整和分类精度方面更具优势。