Optimizing power flow of AC–DC power systems using artificial bee colony algorithm
•ORP problem of AC–DC power systems is solved by ABC algorithm.•The real transformer representation is used for the transformers in this study.•The results of ABC algorithm are compared to those of GA and a numerical method.•ABC algorithm is faster than GA and a numerical method.•The costs and the l...
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| Published in | International journal of electrical power & energy systems Vol. 53; pp. 592 - 602 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.12.2013
Elsevier |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0142-0615 1879-3517 |
| DOI | 10.1016/j.ijepes.2013.05.036 |
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| Summary: | •ORP problem of AC–DC power systems is solved by ABC algorithm.•The real transformer representation is used for the transformers in this study.•The results of ABC algorithm are compared to those of GA and a numerical method.•ABC algorithm is faster than GA and a numerical method.•The costs and the losses obtained by ABC algorithm is less than that of others.
Optimal power flow (OPF) is one of the known problems of the power systems. Many numerical and heuristic methods were used to solve this problem so far. As seen from these studies in literature, heuristic methods are more effective and faster than numerical methods. This case is to make more attractive and mandatory the using of heuristic methods in optimal power flow solution of high voltage direct current (HVDC) systems. In this study, the optimal power flow solution of alternating current–direct current (AC–DC) power systems is firstly accomplished by using the artificial bee colony (ABC) algorithm that is one of the heuristic methods. The proposed method is tested on two different test systems. The obtained results are compared to that of genetic algorithm (GA) and a numerical method in literature. In this study, the real transformer representation is also used for the transformers in the power systems. |
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| Bibliography: | ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 |
| ISSN: | 0142-0615 1879-3517 |
| DOI: | 10.1016/j.ijepes.2013.05.036 |