Research on the Impacts of Distribution Network Automation on the Reliability of Power Supply of Distribution Networks
As the social economy grows swiftly and the need for electricity escalates, the dependability of the power supply within the distribution network has garnered increasing interest. The deployment of distribution network automation technology, which leverages the combined power of smart devices, communication networks, and automated control systems, enables continuous surveillance and governance of the network's operational status, thereby significantly enhancing the stability and security of the power supply infrastructure. The communication network, as a bridge for data transmission and information exchange, is crucial for achieving seamless connection between intelligent devices and control systems. Initially, this paper examines the current state of distribution automation technology and deliberates on its precise influence on the reliability of power supply, encompassing aspects such as fault identification, load regulation, and scheduling optimization. Subsequently, the contribution of distribution automation to the enhancement of power supply reliability is further quantified by introducing pertinent mathematical expressions. In conclusion, the paper suggests directions for optimizing the existing distribution automation system. The aim is that the findings of this paper might offer some insights and serve as a reference for the future intelligent evolution of the power system and assist in the advancement of related domains.
Authors
- Yun Fang (ORCID: https://orcid.org/0000-0001-9217-8858)
- Qing Jiang (ORCID: https://orcid.org/0000-0003-0660-596X)
- Yiqun Chen (ORCID: https://orcid.org/0000-0003-4218-084X)
- Zheng Pengcheng
- Gu Yubin
Institutions
- Kaili University (CN)
- China Southern Power Grid (China) (CN)
Publication Details
- Published
- 2024-10-25
- DOI
- https://doi.org/10.1109/ccnis64984.2024.00041
- Citations
- 1
- Primary Topic
- Power Systems and Technologies
- Type
- article
- Field-Weighted Citation Impact
- 0.21