Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150722
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dc.contributor.authorPareek, Parikshiten_US
dc.contributor.authorYu, Wengen_US
dc.contributor.authorNguyen, Hung Dinhen_US
dc.date.accessioned2021-08-12T01:23:08Z-
dc.date.available2021-08-12T01:23:08Z-
dc.date.issued2020-
dc.identifier.citationPareek, P., Yu, W. & Nguyen, H. D. (2020). Optimal steady-state voltage control using Gaussian process learning. IEEE Transactions On Industrial Informatics, 17(10), 7017-7027. https://dx.doi.org/10.1109/TII.2020.3047844en_US
dc.identifier.issn1551-3203en_US
dc.identifier.urihttps://hdl.handle.net/10356/150722-
dc.description.abstractIn this paper, an optimal steady-state voltage control framework is developed based on a novel linear Voltage-Power dependence deducted from Gaussian Process (GP) learning. Different from other point-based linearization techniques, this GP-based linear relationship is valid over a subspace of operating points and thus suitable for a system with uncertainties such as those in power injections due to renewables. The proposed optimal voltage control algorithms, therefore, perform well over a wide range of operating conditions. Both centralized and distributed optimal control schemes are introduced in this framework. The least-squares estimation is employed to provide analytical forms of the optimal control which offer great computational benefits. Moreover, unlike many existing voltage control approaches deploying fixed voltage references, the proposed control schemes not only minimize the control efforts but also optimize the voltage reference setpoints that lead to the least voltage deviation errors with respect to such setpoints. The control algorithms are also extended to handle uncertain power injections with robust optimal solutions which guarantee compliance with the voltage regulation standards. As for the distributed control scheme, a new network partition problem is cast, based on the concept of Effective Voltage Control Source (EVCS), as an optimization problem which is further solved using convex relaxation. Various simulations on the IEEE 33-bus and 69-bus test feeders are presented to illustrate the performance of the proposed voltage control algorithms and EVCS-based network partition.en_US
dc.description.sponsorshipEnergy Market Authority (EMA)en_US
dc.description.sponsorshipNanyang Technological Universityen_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relation2019-T1-001-119 (RG 79/19)en_US
dc.relationEMA-EP004-EKJGC-0003en_US
dc.relation.ispartofIEEE Transactions on Industrial Informaticsen_US
dc.rights© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/TII.2020.3047844.en_US
dc.subjectEngineering::Electrical and electronic engineeringen_US
dc.titleOptimal steady-state voltage control using Gaussian process learningen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Electrical and Electronic Engineeringen_US
dc.identifier.doi10.1109/TII.2020.3047844-
dc.description.versionAccepted versionen_US
dc.identifier.scopus2-s2.0-85099107240-
dc.identifier.issue10en_US
dc.identifier.volume17en_US
dc.identifier.spage7017en_US
dc.identifier.epage7027en_US
dc.subject.keywordsEffective Voltage Control Sourceen_US
dc.subject.keywordsGaussian Process Learningen_US
dc.subject.keywordsSteady-state Voltage Controlen_US
dc.description.acknowledgementThis work was supported in part by the Nanyang Technological University SUG, in part by the Academic Research Fund TIER 1 2019-T1-001-119 (RG 79/19), and in part by the Energy Market Authority (EMA) and National Research Foundation (NRF) Singapore under Grant EMA-EP004-EKJGC-0003.en_US
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