A modified PSO technique for electrical engineering applications | ||||
| The International Conference on Electrical Engineering | ||||
| Article 4, Volume 8, 8th International Conference on Electrical Engineering ICEENG 2012, May 2012, Page 1-12 PDF (301.29 K) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/iceeng.2012.30570 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Magdi El-Saadawi; Ahmed Hassan; Mohammed Saeed | ||||
| Electrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, Egypt. | ||||
| Abstract | ||||
| PSO is a promising optimization technique proved a high performability as an evolutionary algorithm. Sometimes the conversion of that algorithm is not faster enough because the PSO initial setting values of that algorithm are chosen randomly and varies as long as the domain of application is varied. By means of classic PSO does not care about the limits of those random initial values which impacts the computation time. This paper presents a proposed approach that could be used to modify PSO technique which adapts the PSO initial values and number of particles for any optimization problem. The proposed modification makes the PSO technique faster and more applicable for electrical online applications. In most electrical applications, time factor is very important especially for on line applications. Protective devices coordination problem is a tedious and a time consuming task. To tackle that point of research, one of two cases has to be taken. First case is to redesign the coordination system according to any system changes. The second one is to maintain the old coordination system unchanged up to a specific DG penetration level. The new approach is used to solve the protective devices coordination problem in presence of DG in the two cases. A comparison between the classic PSO and the proposed modified one shows that the new approach gives fast results and shortens the computation time. | ||||
| Keywords | ||||
| Distributed generation; particle swarm optimization (PSO); coordination | ||||
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