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Data-Driven Adaptive Operation of Soft Open Points in Active Distribution Networks
Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin, Peoples R China..
Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin, Peoples R China..
Tianjin Univ, Key Lab Smart Grid, Minist Educ, Tianjin, Peoples R China..
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0003-0300-0762
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2021 (English)In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 17, no 12, p. 8230-8242Article in journal (Refereed) Published
Abstract [en]

The integration of soft open point (SOP) effectively improves the flexibility of active distribution networks (ADNs). However, in practical operation, accurate network parameters are difficult to obtain and the operation state changes rapidly with distributed generators (DGs). With the development of information technologies, massive operation data can be acquired in ADNs. How to utilize multisource data has become the key to realize the intelligent operation of ADNs. This article proposes a data-driven operation strategy of SOP based on model-free adaptive control (MFAC). First, considering the inaccurate parameters and frequent change of operation states, a data-driven framework is formulated for the real-time operation of SOP. Then, the operation strategies of multiple SOPs are further improved with interarea coordination. The results of case studies show that driven by the measurement data, the potential benefits of SOPs are explored to adaptively respond to system state changes and improve the operational performance of ADNs.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2021. Vol. 17, no 12, p. 8230-8242
Keywords [en]
Adaptation models, Data models, Adaptive control, Voltage measurement, Real-time systems, Voltage control, Optimization, Active distribution network (ADN), coordinated operation, data-driven, soft open point (SOP)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-55964DOI: 10.1109/TII.2021.3064370ISI: 000690940600035Scopus ID: 2-s2.0-85102651460OAI: oai:DiVA.org:mdh-55964DiVA, id: diva2:1596751
Available from: 2021-09-23 Created: 2021-09-23 Last updated: 2021-11-05Bibliographically approved

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Yan, Jinyue

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