| Title | An Early Warning Analysis Model of Metering Equipment Based on Federated Hybrid Expert System |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Wenqi, Huang, Lingyu, Liang, Xin, Wang, Zhengguo, Ren, Shang, Cao, Xiaotao, Jiang |
| Conference Name | 2022 15th International Symposium on Computational Intelligence and Design (ISCID) |
| Keywords | Analytical models, early warning of power metering equipment, expert system, expert systems, fault diagnosis, federated learning, Fitting, Human Behavior, Information security, Neural networks, pubcrawl, resilience, Resiliency, Scalability, security, Training |
| Abstract | The smooth operation of metering equipment is inseparable from the monitoring and analysis of equipment alarm events by automated metering systems. With the generation of big data in power metering and the increasing demand for information security of metering systems in the power industry, how to use big data and protect data security at the same time has become a hot research field. In this paper, we propose a hybrid expert model based on federated learning to deal with the problem of alarm information analysis and identification. The hybrid expert system can divide the metering warning problem into multiple sub-problems for processing, which greatly improves the recognition and prediction accuracy. The experimental results show that our model has high accuracy in judging and identifying equipment faults. |
| DOI | 10.1109/ISCID56505.2022.00055 |
| Citation Key | wenqi_early_2022 |
An Early Warning Analysis Model of Metering Equipment Based on Federated Hybrid Expert System