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Analysing Waveform Distortion in Wind Power Plants by a Deep Learning-Based Graphical Tool
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0001-5845-5620
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0002-3625-8578
Luleå University of Technology, Department of Engineering Sciences and Mathematics, Energy Science.ORCID iD: 0000-0003-4074-9529
CGT Eletrosul Brazil.
2022 (English)In: 2022 20th International Conference on Harmonics & Quality of Power (ICHQP) Proceedings: “Power Quality in the Energy Transition”, IEEE, 2022Conference paper, Published paper (Refereed)
Abstract [en]

This work shows an application of a deep learning-based graphical tool for analyzing waveform distortion in wind power plants. The tool consists of a deep autoencoder followed by a clustering algorithm. Previous applications of such a tool have covered harmonic emission which follows daily patterns. The challenge of measurements in wind power plants is the intermittence of the power production, which can vary in a time frame of minutes and hours. To this point, this work proposes a modification of a DL method presented in the literature to address measurements from wind power plants. The method can automatically obtain the number of clusters. The method is applied to harmonic measurements from H2 to H50 and active power in a Brazilian wind power plant. The graphical results allowed obtaining the correlation between patterns of odd and even current harmonic with the active power generated by a wind power plant.

Place, publisher, year, edition, pages
IEEE, 2022.
Series
International Conference on Harmonics and Quality of Power, ISSN 1540-6008, E-ISSN 2164-0610
Keywords [en]
wind power generation, waveform distortion, deep learning, machine learning, harmonic analysis
National Category
Other Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electric Power Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-92107DOI: 10.1109/ICHQP53011.2022.9808731ISI: 000844604500089Scopus ID: 2-s2.0-85133765019OAI: oai:DiVA.org:ltu-92107DiVA, id: diva2:1681843
Conference
20th International Conference on Harmonics & Quality of Power (ICHQP 2022), Naples, Italy, May 29 - June 1, 2022
Funder
Swedish Energy AgencySwedish Transport Administration
Note

ISBN för värdpublikation: 978-1-6654-1639-9

Available from: 2022-07-07 Created: 2022-07-07 Last updated: 2022-09-19Bibliographically approved

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de Oliveira, Roger AlvesDe Souza Salles, RafaelBollen, Math H.J.

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