Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/29681
Title: Fault tolerant differential evolution based optimal reactive power flow
Authors: Su, S
Chung, CY 
Wong, KP
Fung, YF
Yeung, DS
Keywords: Differential Evolution Algorithm
Fault Tolerance
Grid Computing
Premature Convergence
Issue Date: 2006
Publisher: IEEE
Source: 2006 International Conference on Machine Learning and Cybernetics, 13-16 August 2006, Dalian, China, p. 4083-4088 How to cite?
Abstract: Differential evolution (DE) is a new branch of evolutionary algorithms (EAs) and has been successfully applied to solve the optimal reactive power flow (ORPF) problems in power systems. Although DE can avoid premature convergence, large population is needed and the application of DE is limited in large-scale power systems. Grid computing, as a prevalent paradigm for resource-intensive scientific application, is expected to provide a computing platform with tremendous computational power to speed up the optimization process of DE. When implanting DE based ORPF on grid system, fault tolerance due to unstable environment and variation of grid is a significant issue to be considered. In this paper, a fault tolerant DE-based ORPF method is proposed. In this method, when the individuals are distributed to the grid for fitness evaluation, a proportion of individuals, which returns from the grid slowly or fails to return, are replaced with new individuals generated randomly according to some specific rules. This approach can deal with the fault tolerance and also maintain diversity of the population of DE. The superior performance of the proposed approach is verified by numerical simulations on the ORPF problem of the IEEE 118-bus standard power system
URI: http://hdl.handle.net/10397/29681
ISBN: 1-4244-0061-9
DOI: 10.1109/ICMLC.2006.258865
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