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

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%.

Risk detection method for power grid equipment operation based on deep Q-learning algorithm

“…In the risk warning network established in this paper, if the consistency index is ≥ 0.95, it is determined as strong consistency in risk warning data mining; If the consistency index is ≤ 0.85, it is determined as weak consistency in risk warning data mining. Under other conditions that are consistent, after using the methods of reference [2] and reference [3], the consistency index of risk warning data changes in the range of 0.836 to 0.902, indicating weak consistency in risk warning data mining. After using the method proposed in this paper, the consistency index of risk warning data varies between 0.999 and 1.000, indicating strong consistency in risk warning data mining.…”

“…Based on the fault rate curve and Weibull distribution function of the power grid fault rate model, establish a fault risk analysis model for the power grid communication link, and complete the fault risk assessment of the power grid communication link. Reference [3] proposes a distribution network operation status risk warning system based on the Internet of Things. Analyzed the weights of risk indicators, determined the detection indicators of the risk warning system, and established a risk warning evaluation model by analyzing the risk warning indicator system of the operation status of the distribution network.…”