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Conference Paper: An implementation of synthetic generation of wind data series

TitleAn implementation of synthetic generation of wind data series
Authors
KeywordsMarkov chain
Power fluctuations
Renewable energy integration
Synthetic generation
Wind power
Issue Date2013
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003148
Citation
The 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT 2013), Washington, DC., 24-27 February 2013. In Conference Proceedings, 2013, p. 1-6 How to Cite?
AbstractWind power fluctuation is a major concern of large scale wind power grid integration. To test methods proposed for wind power grid integration, a large amount of wind data with time series are necessary and will be helpful to improve the methods. Meanwhile, due to the short operation history of most wind farms as well as limitations of data collections, the data obtained from wind farms could not satisfy the needs of data analysis. Consequently, synthetic generation of wind data series could be one of the effective solutions for this issue. In this paper, a method is presented for generating wind data series using Markov chain. Due to the high order Markov chain, the possibility matrix designed for a wind farm could cost a lot of memory, which is a problem with current computer technologies. Dynamic list will be introduced in this paper to reduce the memory required. Communication errors are un-avoidable on long way signal transmission between the control centre and wind farms. Missing of data always happens in the historical wind data series. Using these data to generate wind data series may result in some mistakes when searching related elements in the probability matrix. An adaptive method will be applied in this paper to solve the problem. The proposed method will be verified using a set of one-year historical data. The results show that the method could generate wind data series in an effective way. © 2013 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/189867
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLiang, Len_US
dc.contributor.authorZhong, Jen_US
dc.contributor.authorLiu, JN-
dc.contributor.authorLi, PM-
dc.contributor.authorZhan, CL-
dc.contributor.authorMeng, ZJ-
dc.date.accessioned2013-09-17T15:01:00Z-
dc.date.available2013-09-17T15:01:00Z-
dc.date.issued2013en_US
dc.identifier.citationThe 2013 IEEE PES Innovative Smart Grid Technologies Conference (ISGT 2013), Washington, DC., 24-27 February 2013. In Conference Proceedings, 2013, p. 1-6en_US
dc.identifier.isbn978-1-4673-4896-6-
dc.identifier.urihttp://hdl.handle.net/10722/189867-
dc.description.abstractWind power fluctuation is a major concern of large scale wind power grid integration. To test methods proposed for wind power grid integration, a large amount of wind data with time series are necessary and will be helpful to improve the methods. Meanwhile, due to the short operation history of most wind farms as well as limitations of data collections, the data obtained from wind farms could not satisfy the needs of data analysis. Consequently, synthetic generation of wind data series could be one of the effective solutions for this issue. In this paper, a method is presented for generating wind data series using Markov chain. Due to the high order Markov chain, the possibility matrix designed for a wind farm could cost a lot of memory, which is a problem with current computer technologies. Dynamic list will be introduced in this paper to reduce the memory required. Communication errors are un-avoidable on long way signal transmission between the control centre and wind farms. Missing of data always happens in the historical wind data series. Using these data to generate wind data series may result in some mistakes when searching related elements in the probability matrix. An adaptive method will be applied in this paper to solve the problem. The proposed method will be verified using a set of one-year historical data. The results show that the method could generate wind data series in an effective way. © 2013 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1003148-
dc.relation.ispartofInnovative Smart Grid Technologies (ISGT) Proceedingsen_US
dc.rightsInnovative Smart Grid Technologies (ISGT) Proceedings. Copyright © IEEE.-
dc.rights©2013 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectMarkov chain-
dc.subjectPower fluctuations-
dc.subjectRenewable energy integration-
dc.subjectSynthetic generation-
dc.subjectWind power-
dc.titleAn implementation of synthetic generation of wind data seriesen_US
dc.typeConference_Paperen_US
dc.identifier.emailLiang, L: lliang@eee.hku.hken_US
dc.identifier.emailZhong, J: jinzhong@hkucc.hku.hk-
dc.identifier.authorityZhong, J=rp00212en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISGT.2013.6497844-
dc.identifier.scopuseid_2-s2.0-84876892734-
dc.identifier.hkuros223111en_US
dc.identifier.spage1-
dc.identifier.epage6-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 131024-

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