An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems
In this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. An IEEE 34 distribution bus system is used as a case study for this resea...
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| Format: | Article |
| Language: | English |
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Wiley
2020-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2020/8824988 |
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| author | Neda Hantash Tamer Khatib Maher Khammash |
| author_facet | Neda Hantash Tamer Khatib Maher Khammash |
| author_sort | Neda Hantash |
| collection | DOAJ |
| description | In this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. An IEEE 34 distribution bus system is used as a case study for this research. A new equation of weight inertia is proposed so as to improve the performance of the PSO conventional algorithm. This development is done by controlling the inertia weight which affects the updating velocity of particles in the algorithm. Matlab codes are developed for the adapted electrical power system and the improved PSO algorithm. Results show that the proposed PSO algorithm successfully finds the optimal size and location of the desired DG unit with a capacity of 1.6722 MW at bus number 10. This makes the voltage magnitude of the selected bus equal to 1.0055 pu and improves the status of the electrical power system in general. The minimum value of fitness losses using the applied algorithm is found to be 0.0.0406 while the average elapsed time is 62.2325 s. In addition to that, the proposed PSO algorithm reduces the active power losses by 31.6%. This means that the average elapsed time is reduced by 21% by using the proposed PSO algorithm as compared to the conventional PSO algorithm that is based on the liner inertia weight equation. |
| format | Article |
| id | doaj-art-e349b2d4d1fb4d8b992f903eb4f1ef5a |
| institution | DOAJ |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-e349b2d4d1fb4d8b992f903eb4f1ef5a2025-08-20T03:21:07ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322020-01-01202010.1155/2020/88249888824988An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power SystemsNeda Hantash0Tamer Khatib1Maher Khammash2Faculty of Graduate Studies, An-Najah National University, 97300 Nablus, State of PalestineDepartment of Energy Engineering and Environment, An-Najah National University, 97300 Nablus, State of PalestineDepartment of Electrical Engineering, An-Najah National University, 97300 Nablus, State of PalestineIn this paper, an improved particle swarm optimization method (PSO) is proposed to optimally size and place a DG unit in an electrical power system so as to improve voltage profile and reduce active power losses in the system. An IEEE 34 distribution bus system is used as a case study for this research. A new equation of weight inertia is proposed so as to improve the performance of the PSO conventional algorithm. This development is done by controlling the inertia weight which affects the updating velocity of particles in the algorithm. Matlab codes are developed for the adapted electrical power system and the improved PSO algorithm. Results show that the proposed PSO algorithm successfully finds the optimal size and location of the desired DG unit with a capacity of 1.6722 MW at bus number 10. This makes the voltage magnitude of the selected bus equal to 1.0055 pu and improves the status of the electrical power system in general. The minimum value of fitness losses using the applied algorithm is found to be 0.0.0406 while the average elapsed time is 62.2325 s. In addition to that, the proposed PSO algorithm reduces the active power losses by 31.6%. This means that the average elapsed time is reduced by 21% by using the proposed PSO algorithm as compared to the conventional PSO algorithm that is based on the liner inertia weight equation.http://dx.doi.org/10.1155/2020/8824988 |
| spellingShingle | Neda Hantash Tamer Khatib Maher Khammash An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems Applied Computational Intelligence and Soft Computing |
| title | An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems |
| title_full | An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems |
| title_fullStr | An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems |
| title_full_unstemmed | An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems |
| title_short | An Improved Particle Swarm Optimization Algorithm forOptimal Allocation of Distributed Generation Units in Radial Power Systems |
| title_sort | improved particle swarm optimization algorithm foroptimal allocation of distributed generation units in radial power systems |
| url | http://dx.doi.org/10.1155/2020/8824988 |
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