Power Grid Data Monitoring and Analysis System based on Edge Computing

Abstract

With the continuous accumulation of large-scale power grid data, the traditional centralized data analysis method is more and more expensive for data transmission. Based on this, we designed a grid big data monitoring and analysis system and transferred the computation process to the edge node close to the data source through an edge computing strategy. On the one hand, data processing and data analysis algorithms are encapsulated by container technology, and the algorithm is mirrored to the edge nodes of the power network through the system to complete the computation. On the other hand, the computing clusters are deployed at the edge nodes of the power network, which is responsible for the scheduling, execution, and status monitoring of computing tasks. Computing tasks can be flexibly managed in a cluster by extending user-defined resources. Through the reserved parameters, users can intervene in task execution policies, and tasks can be configured. The edge node sends the calculation result or early warning information to the central monitoring service through the asynchronous message. Compared with the traditional centralized data analysis system, the proposed method relieves the problem of the overhead of massive data transmission in the network, reduces the application cost, helps to apply the data analysis to more edge side nodes, and fully excavates the potential value of grid data.