期刊名称：International Journal on Electrical Engineering and Informatics
印刷版ISSN：2085-6830
出版年度：2018
卷号：10
期号：3
页码：585-614
DOI：10.15676/ijeei.2018.10.3.10
出版社：School of Electrical Engineering and Informatics
摘要：This piece of work deals with implementing a new meta-heuristic algorithmsymbiotic organisms search to address multi-objective optimal power flow (OPF) problemsin power systems considering several operational constraints. The algorithm has beenimplemented on IEEE 30 and IEEE 118 bus test systems for various single objective andbi-objective functions to assess its efficacy in solving the OPF problem and its ability tohandle large systems. A comparative study of the results, predominantly considering thoseobtained using quasi oppositional teaching learning optimization(QOTLBO), teachinglearning optimization (TLBO), multiobjective harmony search algorithm (MOHS), nondominatedsorting genetic algorithm II (NSGA-II) from the literature are detailed in thispaper. Investigation of the results reveal that the algorithm is successful in producingsuperior results for both the systems and its performance is also encouraging in solvingconflicting objectives.
关键词：L-index; multi-objective optimization; optimal power flow; symbiotic organisms;search.
