Transformer‐Driven Reliability Assessment for Modern Distribution Networks With Distributed Generation

ABSTRACT The variable and unpredictable output from distributed generation (DG) like wind and solar creates new reliability concerns for distribution networks. Integrating DG on a large scale can unbalance the power supply and compromise quality, making accurate reliability assessment essential. This paper puts forward a new assessment method using a Transformer network. The proposed framework integrates physical modeling with deep learning. First, an improved minimum path algorithm is employed to theoretically evaluate system reliability, specifically modeling the load restoration capability of islanded microgrids. The resulting reliability indices are then discretized into specific intervals to construct a labeled dataset. Subsequently, the Transformer network is innovatively applied to learn the mapping between the stochastic output characteristics of DG and these reliability intervals. By transforming the difficult prediction challenge into a classification task, this method effectively overcomes the problem of non‐smoothness in reliability data caused by discrete load restoration. We demonstrate the method's effectiveness on Feeder 4 of the IEEE RBTS 6‐node test system. The proposed framework achieves fast online prediction, enabling dynamic monitoring, and proactive warnings against operational risks in the grid.