Design of Power Outage Monitoring and Warning System Based on Internet of Things

Abstract

With the rapid development of distribution network and the high popularity of distributed generation, the research on risk assessment of distribution network is accelerated. When quantifying the degree of harm caused by abnormal situations, it is necessary to consider the severity of the harm and the impact on society to measure the severity of power failure and reduce the loss caused by power risk. To provide data support for the fine perception of the situation and its development trend, the monitoring system constructed by the power microservice further improves the ability and accuracy of sudden fault prediction. In this paper, through the monitoring system information to explore the transmission line fault early warning, the data analysis model is established. The particle swarm optimization algorithm and BP neural network are used to build the early warning model to predict the potential fault situation and cause analysis of the power grid under various indicators in a certain period in the future, which is to provide more scientific and reasonable risk management and control for the power grid. Through the statistical analysis of all kinds of information data collected by the power microservice, a power monitoring system based on early warning of power outage risk is established to enhance the operation adaptability of the power grid system under uncertain conditions and prevent the occurrence of tripping blackouts so that this study has an important function of intelligent decision-making. The experimental results show the feasibility and high efficiency of this study. The comparison of algorithm results shows that the performance of this method is improved by more than 10%. The stability is improved by 17%. The response time to various situations is shortened by 32% to judge the abnormal operation state of the power grid more efficiently. It can make emergency preparations in time, reduce the damage caused by accidents to the power grid and provide practical tools for the power grid to further enhance the disaster prevention ability of transmission equipment and effectively resist risk events.