A Novel GPU-Accelerated Strategy for Contingency Screening of Static Security Analysis

Abstract

Graphics processing unit (GPU) has been applied successfully in many computation and memory intensive realms due to its superior performances in float-pointing calculation, memory bandwidth and power consumption, and has great potential in power system applications. Contingency screening is a major time consuming part of contingency analysis. In the absence of relevant existing research, this paper is the first of its kind to propose a novel GPU-accelerated algorithm for direct current (DC) contingency screening. Adapting actively unique characteristics of GPU software and hardware, the proposed GPU algorithm is optimized from four aspects: data transmission, parallel task allocation, memory access, and CUDA (Compute Unified Device Architecture) stream. Case studies on a 3012-bus system and 8503-bus system have shown that the GPU-accelerated algorithm, in compared with its counterpart CPU implementation, can achieve about 20 and 50 times speedup respectively. This highly promising performance has demonstrated that carefully designed performance tuning in conjunction with GPU programing architecture is imperative for a GPU-accelerated algorithm. The presented performance tuning strategies can be applicable to other GPU applications in power systems.

Department(s)

Electrical and Computer Engineering

Keywords and Phrases

Algorithms; Computer Graphics; Computer Graphics Equipment; Computer Hardware Description Languages; Electric Power Systems; Memory Architecture; Parallel Processing Systems; Program Processors; Security Systems; Accelerated; Contingency Screening; CUDA; CUDA (compute Unified Device Architecture); Graphics Processing Unit; Power System Applications; Software and Hardwares; Static Security Analysis; Computer Hardware; GPU; Parallel Computing

International Standard Serial Number (ISSN)

0142-0615

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2016 Elsevier, All rights reserved.

Publication Date

01 Dec 2016

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