Kriging assisted surrogate evolutionary computation to solve optimal power flow problems
Kriging assisted surrogate evolutionary computation to solve optimal power flow problems
This paper proposes a Kriging assisted strategy to expedite evolutionary computation for solving Optimal Power Flow (OPF) problems. First, two algorithms were developed-a Kriging Assisted Genetic Algorithm (KAGA) and a Kriging Assisted Particle Swarm Optimization (KAPSO) - and tested using unconstrained benchmark functions; it was found that both algorithms provided reliable and robust solutions. Accordingly, KAGA and KAPSO were selected and tested on the IEEE 30 and 118 bus systems for minimizing generation costs and active power losses. It is shown that the proposed KAPSO outperforms other algorithms, especially in terms of the computation time. In reference to the solution quality yielded by KAGA and KAPSO, the proposed Kriging assisted strategy offers a promising method to improve the performance of evolutionary based computation when solving OPF problems.
Kriging, Optimal power flow, evolutionary computation, genetic algorithm, particle swarm optimization, surrogate modelling
831-839
Deng, Zhida
e059baea-8a79-4e08-ad14-fa30d4feba68
Rotaru, Mihai D.
c53c5038-2fed-4ace-8fad-9f95d4c95b7e
Sykulski, Jan K.
d6885caf-aaed-4d12-9ef3-46c4c3bbd7fb
March 2020
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.
(2020)
Kriging assisted surrogate evolutionary computation to solve optimal power flow problems.
IEEE Transactions on Power Systems, 35 (2), , [8809848].
(doi:10.1109/TPWRS.2019.2936999).
Abstract
This paper proposes a Kriging assisted strategy to expedite evolutionary computation for solving Optimal Power Flow (OPF) problems. First, two algorithms were developed-a Kriging Assisted Genetic Algorithm (KAGA) and a Kriging Assisted Particle Swarm Optimization (KAPSO) - and tested using unconstrained benchmark functions; it was found that both algorithms provided reliable and robust solutions. Accordingly, KAGA and KAPSO were selected and tested on the IEEE 30 and 118 bus systems for minimizing generation costs and active power losses. It is shown that the proposed KAPSO outperforms other algorithms, especially in terms of the computation time. In reference to the solution quality yielded by KAGA and KAPSO, the proposed Kriging assisted strategy offers a promising method to improve the performance of evolutionary based computation when solving OPF problems.
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e-pub ahead of print date: 22 August 2019
Published date: March 2020
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© 1969-2012 IEEE.
Keywords:
Kriging, Optimal power flow, evolutionary computation, genetic algorithm, particle swarm optimization, surrogate modelling
Identifiers
Local EPrints ID: 443685
URI: http://eprints.soton.ac.uk/id/eprint/443685
ISSN: 0885-8950
PURE UUID: 6b3b5f6f-bb81-44a6-8b1f-2a808cd56e30
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Date deposited: 09 Sep 2020 16:30
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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