File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: A Hierarchical Framework for Ambient Signals Based Load Modeling: Exploring the Hidden Quasi-Convexity

TitleA Hierarchical Framework for Ambient Signals Based Load Modeling: Exploring the Hidden Quasi-Convexity
Authors
KeywordsLoad modeling
parameter estimation
ambient signals
convex optimization
Issue Date2021
PublisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59
Citation
IEEE Transactions on Power Systems, 2021, v. 36 n. 6, p. 5780-5791 How to Cite?
AbstractThe approach of ambient signals-based load modeling (ASLM) was recently proposed to better track the time-varying changes of load models. To improve computation efficiency and model structure complexity, a hierarchical framework for ASLM is proposed in this paper. Through this framework, the hidden quasi-convexity of the load modeling problem is explored for the first time. This allows more complicated static load model structures and gradient or Hessian-based optimization algorithms to be used. In the upper level, the identification of dynamic load parameters is regarded as an optimization problem. In the lower level, the optimal static load parameters are obtained through linear regression for a given group of dynamic load parameters. Afterward, the regression residuals are regarded as the objective function (OF) of the upper level optimization problem. The proposed method is validated by the case study results on the Guangdong Power Grid. The results show that the OF is mostly quasi-convex after the transformation of the induction motor model, which provides the basis for the application of gradient or Hessian-based optimization algorithms. The case study results also validate that the proposed approach has better computation efficiency and model structure complexity compared with the previous ASLM approaches.
Persistent Identifierhttp://hdl.handle.net/10722/307669
ISSN
2023 Impact Factor: 6.5
2023 SCImago Journal Rankings: 3.827
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, X-
dc.contributor.authorHill, DJ-
dc.contributor.authorLu, C-
dc.contributor.authorSong, Y-
dc.date.accessioned2021-11-12T13:36:04Z-
dc.date.available2021-11-12T13:36:04Z-
dc.date.issued2021-
dc.identifier.citationIEEE Transactions on Power Systems, 2021, v. 36 n. 6, p. 5780-5791-
dc.identifier.issn0885-8950-
dc.identifier.urihttp://hdl.handle.net/10722/307669-
dc.description.abstractThe approach of ambient signals-based load modeling (ASLM) was recently proposed to better track the time-varying changes of load models. To improve computation efficiency and model structure complexity, a hierarchical framework for ASLM is proposed in this paper. Through this framework, the hidden quasi-convexity of the load modeling problem is explored for the first time. This allows more complicated static load model structures and gradient or Hessian-based optimization algorithms to be used. In the upper level, the identification of dynamic load parameters is regarded as an optimization problem. In the lower level, the optimal static load parameters are obtained through linear regression for a given group of dynamic load parameters. Afterward, the regression residuals are regarded as the objective function (OF) of the upper level optimization problem. The proposed method is validated by the case study results on the Guangdong Power Grid. The results show that the OF is mostly quasi-convex after the transformation of the induction motor model, which provides the basis for the application of gradient or Hessian-based optimization algorithms. The case study results also validate that the proposed approach has better computation efficiency and model structure complexity compared with the previous ASLM approaches.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=59-
dc.relation.ispartofIEEE Transactions on Power Systems-
dc.rightsIEEE Transactions on Power Systems. Copyright © Institute of Electrical and Electronics Engineers.-
dc.rights©20xx 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.-
dc.subjectLoad modeling-
dc.subjectparameter estimation-
dc.subjectambient signals-
dc.subjectconvex optimization-
dc.titleA Hierarchical Framework for Ambient Signals Based Load Modeling: Exploring the Hidden Quasi-Convexity-
dc.typeArticle-
dc.identifier.emailHill, DJ: dhill@HKUCC-COM.hku.hk-
dc.identifier.emailSong, Y: songyue@hku.hk-
dc.identifier.authorityHill, DJ=rp01669-
dc.identifier.authoritySong, Y=rp02676-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1109/TPWRS.2021.3078528-
dc.identifier.scopuseid_2-s2.0-85105857289-
dc.identifier.hkuros329584-
dc.identifier.volume36-
dc.identifier.issue6-
dc.identifier.spage5780-
dc.identifier.epage5791-
dc.identifier.isiWOS:000709092000078-
dc.publisher.placeUnited States-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats