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Mobile Information Systems
Volume 2017, Article ID 1897476, 10 pages
https://doi.org/10.1155/2017/1897476
Research Article

Implementation and Optimization of GPU-Based Static State Security Analysis in Power Systems

1Services Computing Technology and System Lab, Cluster and Grid Computing Lab, Big Data Technology and System Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China
2China Electric Power Research Institute, Beijing 100192, China
3Global Energy Interconnection Development and Cooperation Organization, Beijing 100031, China

Correspondence should be addressed to Ran Zheng; nc.ude.tsuh@renarhz

Received 23 September 2016; Accepted 5 January 2017; Published 15 March 2017

Academic Editor: Beniamino Di Martino

Copyright © 2017 Yong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Static state security analysis (SSSA) is one of the most important computations to check whether a power system is in normal and secure operating state. It is a challenge to satisfy real-time requirements with CPU-based concurrent methods due to the intensive computations. A sensitivity analysis-based method with Graphics processing unit (GPU) is proposed for power systems, which can reduce calculation time by 40% compared to the execution on a 4-core CPU. The proposed method involves load flow analysis and sensitivity analysis. In load flow analysis, a multifrontal method for sparse LU factorization is explored on GPU through dynamic frontal task scheduling between CPU and GPU. The varying matrix operations during sensitivity analysis on GPU are highly optimized in this study. The results of performance evaluations show that the proposed GPU-based SSSA with optimized matrix operations can achieve a significant reduction in computation time.