A study of evolutionary based optimal power flow techniques
A study of evolutionary based optimal power flow techniques
This paper explores the use of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving multi objective optimal power flow problems. The algorithms are tested on the IEEE 30 bus system by simultaneously minimizing cost, power losses and voltage deviation under operational constraints. The Pareto optimal and fuzzy methods are employed to identify the best compromise for conflicting objectives; the solutions provided by GA and PSO are compared with each other, as well as against other optimization methods.
Fuzzy Decision Making, Genetic Algorithm, MATPOWER, Multi Objective Optimal Power Flow, Pareto Optimal Front, Particle Swarm Optimization
Deng, Zhida
e059baea-8a79-4e08-ad14-fa30d4feba68
Rotaru, Mihai D.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, Jan K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
2 July 2016
Deng, Zhida
e059baea-8a79-4e08-ad14-fa30d4feba68
Rotaru, Mihai D.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, Jan K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
Deng, Zhida, Rotaru, Mihai D. and Sykulski, Jan K.
(2016)
A study of evolutionary based optimal power flow techniques.
In Proceedings - 2016 51st International Universities Power Engineering Conference, UPEC 2016.
vol. 2017-January,
IEEE.
6 pp
.
(doi:10.1109/UPEC.2016.8114071).
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Conference or Workshop Item
(Paper)
Abstract
This paper explores the use of Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) for solving multi objective optimal power flow problems. The algorithms are tested on the IEEE 30 bus system by simultaneously minimizing cost, power losses and voltage deviation under operational constraints. The Pareto optimal and fuzzy methods are employed to identify the best compromise for conflicting objectives; the solutions provided by GA and PSO are compared with each other, as well as against other optimization methods.
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Published date: 2 July 2016
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Publisher Copyright:
© 2016 IEEE.
Venue - Dates:
51st International Universities Power Engineering Conference, UPEC 2016, , Coimbra, Portugal, 2016-09-06 - 2016-09-09
Keywords:
Fuzzy Decision Making, Genetic Algorithm, MATPOWER, Multi Objective Optimal Power Flow, Pareto Optimal Front, Particle Swarm Optimization
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Local EPrints ID: 477696
URI: http://eprints.soton.ac.uk/id/eprint/477696
PURE UUID: 270fffdd-1c0f-4e8e-8795-5e3e9ce1efd5
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Date deposited: 13 Jun 2023 16:56
Last modified: 17 Mar 2024 02:33
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Contributors
Author:
Zhida Deng
Author:
Mihai D. Rotaru
Author:
Jan K. Sykulski
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