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Kriging assisted surrogate evolutionary computation to solve optimal power flow problems

Kriging assisted surrogate evolutionary computation to solve optimal power flow problems
Kriging assisted surrogate evolutionary computation to solve optimal power flow problems
Kriging, Optimal power flow, evolutionary computation, genetic algorithm, particle swarm optimization, surrogate modelling
0885-8950
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
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), 831-839, [8809848]. (doi:10.1109/TPWRS.2019.2936999).

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More information

e-pub ahead of print date: 22 August 2019
Published date: March 2020
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
ORCID for Zhida Deng: ORCID iD orcid.org/0000-0002-8448-1934
ORCID for Jan K. Sykulski: ORCID iD orcid.org/0000-0001-6392-126X

Catalogue record

Date deposited: 09 Sep 2020 16:30
Last modified: 09 Oct 2020 16:30

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