Search

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. [ABSTRACT FROM AUTHOR]

In this article, we introduce the Intever public database for arcing event detection (IAED) along with novel feature extraction methods and an end-to-end deep neural network. First, we describe the details of arc simulation process and database specifications. Second, we handcraft a set of 29 efficient arcing features, including novel nonlinear dynamic features extracted from reconstructed phase space. Third, we propose a customized multiscale convolutional neural network (Multi-Scale CNN) model. Finally, we present the benchmark testing results on six IAED cohorts and 25 load settings and analyze the time complexity of each algorithm. Experimental results show that the highest $F1$ scores on high-resolution cohort and low-resolution cohort are achieved by Multi-Scale CNN at 94.16% and 87.59%, respectively. Also, Multi-Scale CNN gives superior results on various challenging load settings, such as induction cooker connected with air purifier in high resolution and microwave connected with refrigerator in low resolution, and is proved run faster than traditional arcing detection methods. [ABSTRACT FROM AUTHOR]

Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The proposed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow. [ABSTRACT FROM PUBLISHER]

In many power system applications, such as N–x static security analysis and Monte-Carlo-simulation-based probabilistic power flow (PF) analysis, it is a very time-consuming task to analyze massive number of PFs on identical or similar network topology. This letter presents a novel GPU-accelerated batch LU-factorization solver that achieves higher level of parallelism and better memory-access efficiency through packaging massive number of LU-factorization tasks to formulate a new larger-scale problem. The proposed solver can achieve up to 76 times speedup when compared to KLU library and lays a critical foundation for massive-PFs-solving applications. [ABSTRACT FROM PUBLISHER]