Syahputra, Ramadoni and Soesanti, Indah (2021) An Optimization of Power Distribution Network Configuration with Distributed Generator Integration Using Genetic Algorithm. In: 2021 1st International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS), 15-16 October 2021, Yogyakarta.
Full text not available from this repository. (Request a copy)Abstract
The limitations of fossil fuels make renewable energy system increasingly popular. The power plant is usually integrated into an electric power distribution network called a distributed generator (DG). The integration of DG in the distribution network makes the network scheme change. We need to do some re-planning with the presence of DG to improve distribution network performance. This paper discusses applying the genetic algorithm (GA) method for optimization to improve the network performance. The presence of DG makes the distribution network more dynamic. The GA method with the ability to avoid local minima is the answer to the existing problems. The system test was carried out on an IEEE 69-bus network model. The results showed that the GA method was able to produce distribution network optimization with a significant reduction in power losses while at the same time increasing the quality of the bus voltage. © 2021 IEEE.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 1 |
Uncontrolled Keywords: | Electric network analysis; Electric network parameters; Fossil fuels; Genetic algorithms; Network performance; Renewable energy resources; Algorithm methods; Distributed generators; Electric power distribution networks; Existing problems; Local minimums; Network scheme; Optimisations; Power distribution network configurations; Powerloss; Re-planning; Distributed power generation |
Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
Divisions: | Faculty of Engineering > Electronics Engineering Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 07 Oct 2024 23:08 |
Last Modified: | 07 Oct 2024 23:08 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8689 |