Network reconfiguration and optimal distributed generations allocation with whale optimizer algorithm
Power distribution networks have become more interested in Distributed Generations (DG) due to their potential for reducing power loss and improving system dependability. Discovering the optimal site, size, and reconfiguration strategy for a DG-based distribution network using a metaheuristic algor...
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| Main Author: | |
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| Format: | Article |
| Language: | English |
| Published: |
OICC Press
2025-03-01
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| Series: | Majlesi Journal of Electrical Engineering |
| Subjects: | |
| Online Access: | https://oiccpress.com/mjee/article/view/10854 |
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| Summary: | Power distribution networks have become more interested in Distributed Generations (DG) due to their potential for reducing power loss and improving system dependability. Discovering the optimal site, size, and reconfiguration strategy for a DG-based distribution network using a metaheuristic algorithm is the main goal of this study. The multi-objective and multi-constrained feature of the Whale Optimization Algorithm (WOA) makes it a useful optimization technique for network reconfiguration. In this paper, the forward-backward load flow technique is employed due to its easy implementation, quick and reliable convergence. The recommended approach is validated through two different test systems. Four different scenarios are considered. Improvements in power loss reduction and voltage profile illustrate the effectiveness of the proposed technique. The obtained results showed that DG allocation after network reconfiguration resulted in a greater reduction of power losses and refinement of the voltage profile of the network. Also, a comparison is employed with other optimization methods, it can be seen that the suggested method’s performance is clearly superior, as shown by the numerical data. Losses were reduced by 67.8% and 63.21% on IEEE 33 and 69 bus systems, respectively, when using the suggested strategy. All the simulations are conducted through MATLAB.
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| ISSN: | 2345-377X 2345-3796 |