Power Grid Scenario Generation Method Based on a Prior Knowledge Embedded Conditional Generative Adversarial Network

This paper addresses the challenges of scarce high-risk scenario samples in power grid operation and the difficulty of traditional methods to balance overall distribution rationality with specific feature requirements. A power grid scenario generation method based on a prior knowledge embedded conditional generative adversarial network is proposed. The method encodes operational risk features such as node overvoltage and line power flow overload as conditional variables. A feature-aware loss function is constructed to embed physical constraints into the training objective of generative adversarial networks. This approach achieves organic integration of data-driven learning and knowledge-driven guidance. Case studies demonstrate that the proposed method significantly improves the generation ratio of risk scenarios at designated locations and types while maintaining the reasonableness of overall data distribution. This provides data support with both physical interpretability and computational efficiency for power grid security analysis, risk assessment, and intelligent dispatching.