Design and Implementation of Intelligent Substation Intelligent Operation and Maintenance System Based on Internet of Things Technology
With the intelligent development of power system, the importance of intelligent substation in power grid operation is becoming more and more prominent. However, the traditional substation operation and maintenance method has problems such as high manual dependence, insufficient real-time, limited fault prediction ability, etc., which is difficult to meet the modern power grid's demand for efficient, safe, and intelligent operation and maintenance. Based on IoT technology, this paper designs and implements an intelligent O&M system for smart substations, and builds an end-edge-cloud cooperative system architecture to improve the real-time data collection, the accuracy of analysis, and the automation level of O&M. The system integrates key technologies such as sensor network, wireless communication, big data analysis and edge computing, and realizes core functions such as real-time monitoring of equipment status, fault diagnosis and prediction, and remote intelligent operation and maintenance. In this paper, an experimental platform was built to test and optimize the performance of the system, and the results show that the system can effectively improve the operation and maintenance efficiency and safety of intelligent substations. The research results provide technical support for the construction of a more intelligent and efficient power operation and maintenance system, and lay the foundation for the further development of intelligent substations in the future.
Authors
- Yuchao Chen (ORCID: https://orcid.org/0009-0008-9676-7770)
- Yaru Zhang (ORCID: https://orcid.org/0009-0009-9180-5894)
- Zijie Wang (ORCID: https://orcid.org/0000-0001-6333-8445)
- Xiaotian Pan (ORCID: https://orcid.org/0000-0002-9473-320X)
- Sijia Tan
Institutions
- J-Power (Japan) (JP)
- State Grid Corporation of China (China) (CN)
Publication Details
- Published
- 2025-03-26
- DOI
- https://doi.org/10.1109/edpee65754.2025.00256
- Citations
- 1
- Primary Topic
- Smart Grid and Power Systems
- Type
- article
- Field-Weighted Citation Impact
- 0.73