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.