Visible to the public On-line transient stability analysis using high performance computing

TitleOn-line transient stability analysis using high performance computing
Publication TypeConference Paper
Year of Publication2014
AuthorsSmith, S., Woodward, C., Liang Min, Chaoyang Jing, Del Rosso, A.
Conference NameInnovative Smart Grid Technologies Conference (ISGT), 2014 IEEE PES
Date PublishedFeb
KeywordsComputational modeling, control center, Dynamic security assessment, EPRI extended transient midterm simulation program, ETMSP, File systems, High performance computing, high performance computing machine, input-output bottleneck, large-scale contingency analysis, Large-scale systems, local disk, local file system, message passing, message passing interface, MPI, on-line transient stability analysis, parallelization, power engineering computing, power system stability, power system transient stability, real-time environment, real-time simulation, Real-time Systems, sparse linear solve, Stability analysis, SuperLU_MT library, system dynamics phenomena, Transient analysis, transient stability, ultrafast transient stability analysis
Abstract

In this paper, parallelization and high performance computing are utilized to enable ultrafast transient stability analysis that can be used in a real-time environment to quickly perform "what-if" simulations involving system dynamics phenomena. EPRI's Extended Transient Midterm Simulation Program (ETMSP) is modified and enhanced for this work. The contingency analysis is scaled for large-scale contingency analysis using Message Passing Interface (MPI) based parallelization. Simulations of thousands of contingencies on a high performance computing machine are performed, and results show that parallelization over contingencies with MPI provides good scalability and computational gains. Different ways to reduce the Input/Output (I/O) bottleneck are explored, and findings indicate that architecting a machine with a larger local disk and maintaining a local file system significantly improve the scaling results. Thread-parallelization of the sparse linear solve is explored also through use of the SuperLU_MT library.

DOI10.1109/ISGT.2014.6816438
Citation Key6816438