Paper
5 June 2024 State monitoring method of automation equipment in independent and controllable substation based on grey prediction model
Lixiang Ruan, Xingjun Cui, Xinyu Li, Yifei Shen
Author Affiliations +
Proceedings Volume 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024); 131633C (2024) https://doi.org/10.1117/12.3030750
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 2024, Xi'an, China
Abstract
In the independent and controllable intelligent substation, the reliable operation of the automation equipment is the premise and guarantee of the stable operation of the substation. Independent and controllable automation equipment uses a large number of domestic chip products to replace imported chips. Compared with imported chips, application time of domestic chip products is short, the operation experience is very insufficient. This paper first analyzes the data that can be monitored by the independent and controllable chip and the characteristics of the corresponding data. Then, the algorithm characteristics of gray-scale prediction model GM (1,1) and its applicability and superiority in chip monitoring are analyzed, so as to construct the chip-level state monitoring system of the independent and controllable automation equipment combining gray-scale model and expert system. Finally, combined with the actual operation data of the equipment, the accuracy of the gray model prediction data and the practicability of the equipment monitoring system are verified. The results show that this method is effective in equipment state monitoring.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Lixiang Ruan, Xingjun Cui, Xinyu Li, and Yifei Shen "State monitoring method of automation equipment in independent and controllable substation based on grey prediction model", Proc. SPIE 13163, Fourth International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2024), 131633C (5 June 2024); https://doi.org/10.1117/12.3030750
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KEYWORDS
Data modeling

Instrument modeling

Automation

Control systems

Machine learning

Data processing

Modeling

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