An optimal allocation method for power distribution network partitions based on improved spectral clustering algorithm

Distribution network nodes are numerous and monitoring devices are widely distributed. All monitoring data are uploaded to the cloud master for centralized processing may cause serious problems, such as network congestion, information delay and high computational complexity. The edge computing can provide a good solution, which requires reasonable distribution network partition. This paper proposes a Monte Carlo optimized spectral clustering (MCOSC) distribution network partition method for edge server configuration. Firstly, the objective function of distribution network partition number is constructed with economic and real-time communication indexes, which are more suitable for edge computing than electrical distance and voltage sensitivity indexes. Then the optimal number of partitions is obtained by particle swarm optimization (PSO) with the constraints of communication reliability. Secondly, to solve the problem that the traditional spectral clustering is easy to fall into the local optimal solution, a Monte Carlo optimized spectral clustering method is proposed to make the distribution network partition results more reasonable. Finally, the performance of the proposed method is evaluated by IEEE 33 bus and 69 bus systems distribution network models. The results indicate that the Monte Carlo optimization partition method has better accurate, robustness and convergence speed than traditional spectral clustering method.