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000148610 0247_ $$2doi$$a10.1109/TPWRS.2024.3394371
000148610 0248_ $$2sideral$$a141954
000148610 037__ $$aART-2024-141954
000148610 041__ $$aeng
000148610 100__ $$aLopez-Garcia, Tania B.
000148610 245__ $$aOptimal Power Flow With Physics-Informed Typed Graph Neural Networks
000148610 260__ $$c2024
000148610 5060_ $$aAccess copy available to the general public$$fUnrestricted
000148610 5203_ $$aThis work describes a new way to solve the optimal power flow problem applying typed graph neural networks. Typed graph neural networks allow the representation of different elements of transmission systems with different types of nodes, thus adding accuracy and interpretability to the solutions obtained, in comparison to results obtained with conventional feed-forward neural network models. The proposed graph neural network architecture is trained without the need of training data, through a physics informed loss function which incorporates not only the optimization objective, but also operational constraints of the physical system. Results are comparable with those obtained with the interior point method, and it is shown that the calculation time is greatly reduced.
000148610 536__ $$9info:eu-repo/grantAgreement/ES/MICINN/PID2019-104711RB-100
000148610 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttps://creativecommons.org/licenses/by/4.0/deed.es
000148610 590__ $$a7.2$$b2024
000148610 592__ $$a3.629$$b2024
000148610 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b39 / 368 = 0.106$$c2024$$dQ1$$eT1
000148610 593__ $$aEnergy Engineering and Power Technology$$c2024$$dQ1
000148610 593__ $$aElectrical and Electronic Engineering$$c2024$$dQ1
000148610 594__ $$a15.9$$b2024
000148610 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000148610 700__ $$0(orcid)0000-0002-4770-0069$$aDomínguez-Navarro, José Antonio$$uUniversidad de Zaragoza
000148610 7102_ $$15009$$2535$$aUniversidad de Zaragoza$$bDpto. Ingeniería Eléctrica$$cÁrea Ingeniería Eléctrica
000148610 773__ $$g40, 1 (2024), 381-393$$pIEEE trans. power syst.$$tIEEE TRANSACTIONS ON POWER SYSTEMS$$x0885-8950
000148610 8564_ $$s3075196$$uhttps://zaguan.unizar.es/record/148610/files/texto_completo.pdf$$yVersión publicada
000148610 8564_ $$s3509408$$uhttps://zaguan.unizar.es/record/148610/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000148610 909CO $$ooai:zaguan.unizar.es:148610$$particulos$$pdriver
000148610 951__ $$a2026-02-17-20:29:19
000148610 980__ $$aARTICLE