A DNR Using Evolutionary PSO for Power Loss Reduction
The total power losses in distribution network system can be minimized by network configuration. In this area of research, most of the researchers have used multiple types of optimization technique to determine the optimal problem solving. In this paper, an efficient hybridization of heuristic metho...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
2013
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| Subjects: | |
| Online Access: | http://eprints.utem.edu.my/id/eprint/10616/1/2013_Journal_JTEC.pdf http://eprints.utem.edu.my/id/eprint/10616/ http://jtec.utem.edu.my/index.php?option=com_docman&task=cat_view&gid=110&Itemid=49 |
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| Summary: | The total power losses in distribution network system can be minimized by network configuration. In this area of research, most of the researchers have used multiple types of optimization technique to determine the optimal problem solving. In this paper, an efficient hybridization of heuristic method which is called as Evolutionary Particle Swarm Optimization (EPSO) is introduced to identify the open and closed switching operation plans for feeder network reconfiguration. The main objective is to reduce the power losses in the distribution network system and improve the voltage profile in the overall system meanwhile minimizing the computational time. The proposed combination of Particle Swarm Optimization (PSO) and Evolutionary Programming (EP) is introduced to make it faster to find the optimal solution. The proposed method is applied and its impact on the network reconfiguration for real power loss and voltage profiles is investigated. In network reconfiguration, the network topologies change through On/Off of the sectionalizing and tie switches in order to optimize network operation parameters. The aim is to find the best configuration which consists of switches that will contribute to a lower loss in the distribution network system. The method was tested on a IEEE 33-bus system to show the effectiveness of the EPSO method over the traditional PSO and EP method.
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