Internet of Things-Based Risk Warning System for Distribution Grid Operation State

Author:

He Jinding1,Cai Baorui1,Yan Wenlin1,Zhang Bin1,Zhang Rongkui2

Affiliation:

1. Yunnan Electric Power Dispatching Control Center, Kunming 650011, Yunnan, P. R. China

2. Yunnan Yundian Tongfang Technology Co. Ltd., Kunming 650217, Yunnan, P. R. China

Abstract

The distribution network is the most terminal part of the power system. The safety and stability of its operation directly affect the power supply reliability of the power system. The operation status of the equipment has always been the focus of researchers. Once a problem occurs in the operation of the distribution network, it will have a significant impact on public safety and social development. Therefore, real-time monitoring of the operation status of the distribution network is very important. In order to meet the needs of real-time intelligent detection of the operation status of the distribution network, this paper introduces a risk warning system based on the Internet of Things for the operation status of the distribution network. This paper firstly analyzes the weight of risk indicators to determine the detection indicators of the risk early warning system, and secondly, through the analysis of the risk early warning indicator system of the distribution network operation status and the establishment of the risk early warning evaluation model, to determine the operation status of the distribution network the following risk early warning detection needs, and finally the Internet of Things technology is used in the design of the risk early warning system of the operating state of the distribution network, and then the system is tested. The test shows that the real-time detection data error of the system is less than 5%.

Publisher

World Scientific Pub Co Pte Ltd

Subject

Computer Networks and Communications

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Risk detection method for power grid equipment operation based on deep Q-learning algorithm;Second International Conference on Informatics, Networking, and Computing (ICINC 2023);2024-04-04

2. Power Grid Resource Early Warning Strategy Based on Mutation Identification;2024 7th International Conference on Advanced Algorithms and Control Engineering (ICAACE);2024-03-01

3. Smart Beta and Risk Factors Based on IoTs;Alternative Data and Artificial Intelligence Techniques;2022

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