Fast Cycle Structure Detection for Power Grids Based on Graph Computing

The cycle structure in a power grid may lower the stability of the network; thus, it is of great significance to accurately and timely detect cycles in power grid networks. However, detecting possible cycles in a large-scale network can be highly time consuming and computationally intensive. In addition, since the power grid's topology changes over time, cycles can appear and disappear, and it can be difficult to monitor them in real time. In traditional computing systems, cycle detection requires considerable computational resources, making real-time cycle detection in large-scale power grids an impossible task. Graph computing has shown excellent performance in many areas and has solved many practical graph-related problems, such as power flow calculation and state estimation. In this article, a cycle detection method, the Paton method, is implemented and optimized on a graph computing platform. Two cases are used to test its performance in an actual power grid topology scenario. The results show that the graph computing-based Paton method reduces the time consumption by at least 60% compared to that of other methods.