hh.sePublications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Stream Data Cleaning for Dynamic Line Rating Application
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-5863-0748
Department of Electrical and Energy Engineering, University of Cantabria, Santander, Spain.ORCID iD: 0000-0003-3751-7305
Department of Electrical and Energy Engineering, University of Cantabria, Santander, Spain.ORCID iD: 0000-0001-6886-8170
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), CAISR - Center for Applied Intelligent Systems Research.ORCID iD: 0000-0002-3495-2961
Show others and affiliations
2018 (English)In: Energies, ISSN 1996-1073, E-ISSN 1996-1073, Vol. 11, no 8, article id 2007Article in journal (Refereed) Published
Abstract [en]

The maximum current that an overhead transmission line can continuously carry depends on external weather conditions, most commonly obtained from real-time streaming weather sensors. The accuracy of the sensor data is very important in order to avoid problems such as overheating. Furthermore, faulty sensor readings may cause operators to limit or even stop the energy production from renewable sources in radial networks. This paper presents a method for detecting and replacing sequences of consecutive faulty data originating from streaming weather sensors. The method is based on a combination of (a) a set of constraints obtained from derivatives in consecutive data, and (b) association rules that are automatically generated from historical data. In smart grids, a large amount of historical data from different weather stations are available but rarely used. In this work, we show that mining and analyzing this historical data provides valuable information that can be used for detecting and replacing faulty sensor readings. We compare the result of the proposed method against the exponentially weighted moving average and vector autoregression models. Experiments on data sets with real and synthetic errors demonstrate the good performance of the proposed method for monitoring weather sensors.

Place, publisher, year, edition, pages
Basel: MDPI, 2018. Vol. 11, no 8, article id 2007
Keywords [en]
smart grids, dynamic line rating, stream data cleaning, data mining
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Other Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:hh:diva-37676OAI: oai:DiVA.org:hh-37676DiVA, id: diva2:1236365
Note

Funding: This research was partially funded by Spanish Government under Spanish R+D initiative with reference ENE2013-42720-R and RETOS RTC-2015-3795-3.

Available from: 2018-08-02 Created: 2018-08-02 Last updated: 2018-08-02Bibliographically approved

Open Access in DiVA

fulltext(2392 kB)4 downloads
File information
File name FULLTEXT01.pdfFile size 2392 kBChecksum SHA-512
8c749aeaee57c8af7cff9fccd4036571c7568647c2be03602a5ac4234bf61d6bfabe04217cd3bc9c1b4aabe4ba0c2b7c1c7e3d27cef7ce0a6f485bff1c0fb22a
Type fulltextMimetype application/pdf

Other links

Full text

Authority records BETA

Mashad Nemati, HassanPinheiro Sant'Anna, AnitaNowaczyk, Sławomir

Search in DiVA

By author/editor
Mashad Nemati, HassanLaso, A.Manana, M.Pinheiro Sant'Anna, AnitaNowaczyk, Sławomir
By organisation
CAISR - Center for Applied Intelligent Systems Research
In the same journal
Energies
Electrical Engineering, Electronic Engineering, Information EngineeringOther Electrical Engineering, Electronic Engineering, Information Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 4 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 11782 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf