Method for generating minority class maintenance data samples for regional power grids based on conditional diffusion models (CDMS)

This paper proposes a comprehensive method for generating maintenance data samples for regional power grids by integrating Conditional Diffusion Models (CDMs). The method involves analyzing the power grid topology and load flow calculation results to generate high-quality maintenance data samples, providing foundational data support for the transient stability verification of power grid maintenance methods. The research content includes the formulation of maintenance plans, the generation of maintenance data base samples, the construction of adjustable component sets, and the generation of maintenance data sub-samples. By invoking the PSASP load flow core calculation program, massive load flow calculations are realized, offering diversified data samples for power grid maintenance. Additionally, the paper explores the application of CDMs for minority class sample generation, which significantly improves the balance and diversity of the maintenance data samples. Experimental results indicate that this method can effectively generate high-quality data samples that meet the actual needs of power grids, enhancing the efficiency and accuracy of power grid maintenance work.