GPU-Accelerated Online Short-Circuit Interrupting Capacity Scan Based on Unified Modeling

With the increasing of transmission density and short-circuit current level, interruption capacity scanning has become an important approach for the online assessment of the circuit breaker. The short-circuit calculation scale and the expected failures significantly increase under the demand of unified analysis of the whole grid, which cause the considerable challenges for online assessment. In this study, we propose a GPU-accelerated short-circuit interruption capacity scanning algorithm for two different unified models, that is, GPU-based 2D and 3D expansion models. For both models, the redundant configuration of the fault network matrices is designed to reduce the cost of GPU computation process. Then, the batch node voltage analysis and the batch branch current calculation are derived using the considered model. In addition, the differences between two models are discussed in detail. Simulations show that the proposed algorithm with GPU-based 3D-expand model has a 3.45× speedup compared with the traditional algorithm on a 9241-bus system with 8-core CPU. Therefore, the proposed algorithms have the potential to handle the full short-circuit current scan under the unified analysis scenario.