It is very important for the safe operation of the power grid to be able to achieve real-time online monitoring, but along with the expansion of the smart grid, powerful computing power is required to achieve online monitoring, which cannot be loaded by ordinary servers. Due to the limited computing and storage resources of the server, it is difficult to fully meet the real-time requirements of online power line monitoring. Therefore, this research proposes a smart grid real-time monitoring and early warning system based on edge computing, which can reasonably allocate computing tasks according to the effective use of edge node resources. It can also collect and process information in real time, and can monitor the power grid online, which improves the fault identification efficiency of the power grid, effectively reduces the economic cost, and relieves the computing pressure of the online monitoring equipment on the cloud computing server. Meanwhile, considering the burst problem in allocation queue optimization, a multipriority allocation queue algorithm is proposed, and an improved greedy algorithm allocation model is used to solve the optimal scheduling problem between bursts. Simulation analysis shows that the scheduling scheme proposed in this paper can effectively reduce the monitoring delay of the power line monitoring system, improve the overall adaptability and compatibility of the system, meet the market demand, and facilitate promotion.
展开▼
