@inproceedings{19e2e258dcaf4cc882fb071194c69f2c,

title = "A parallel evolutionary programming based optimal power flow algorithm and its implementation",

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.",

keywords = "Evolutionary programming, Optimal power flow, Optimization, Parallel programming",

author = "Lo, {C. H.} and Chung, {C. Y.} and Nguyen, {D. H.M.} and Wong, {K. P.}",

year = "2004",

language = "English",

isbn = "0780384032",

series = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

pages = "2543--2548",

booktitle = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics",

note = "Proceedings of 2004 International Conference on Machine Learning and Cybernetics ; Conference date: 26-08-2004 Through 29-08-2004",

}