Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/21490
Title: A parallel evolutionary programming based optimal power flow algorithm and its implementation
Authors: Lo, CH
Chung, CY 
Nguyen, DHM
Wong, KP
Keywords: Convergence
Electricity supply industry
Evolutionary computation
Load flow
Optimisation
Parallel programming
Workstation clusters
Issue Date: 2004
Publisher: IEEE
Source: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004, v. 4, p. 2543-2548 How to cite?
Journal: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 26-29 August 2004 
Abstract: This paper develops a parallel evolutionary programming based optimal power flow solution algorithm. The proposed approach is less sensitive to the choice of starting points and types of generator cost curves. To improve the robustness and speed of convergence of the algorithm, population and gradient acceleration techniques are incorporated. The developed algorithm is implemented on a thirty-six-processor Beowulf cluster. The proposed approach has been tested on the IEEE 118-bus system under master-slave, dual-direction ring and 2D-mesh topologies. Computational speedup and generation costs for each parallel topology with different number of processors are then compared to those of the sequential EP approach.
URI: http://hdl.handle.net/10397/21490
ISBN: 0-7803-8403-2
DOI: 10.1109/ICMLC.2004.1382232
Appears in Collections:Conference Paper

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