Empirical mode decomposition of wind speed signals

Empirical Mode Decomposition (EMD) is a powerful signal processing technique with diverse applications, particularly in the analysis of non-stationary data. In this study, we assess the capabilities of EMD for wind data analysis, aiming to uncover its effectiveness in capturing intricate temporal pa...

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Bibliographic Details
Author: Pinto Molina, Ines
Format: master thesis
Publication Date:2023
Country:España
Institution:Universitat Politècnica de Catalunya (UPC)
Repository:UPCommons. Portal del coneixement obert de la UPC
Language:English
OAI Identifier:oai:upcommons.upc.edu:2117/395883
Online Access:https://hdl.handle.net/2117/395883
Access Level:Open access
Keyword:Atmospheric circulation -- Measurement -- Data processing -- Mathematical models
Signal processing -- Digital techniques -- Mathematics
Empirical Mode Decomposition (EMD)
Ensemble Empirical Mode Decomposition (EEMD)
Intrinsic Mode Functions (IMFs)
Fourier
Average Diurnal Variation (ADV)
Average Seasonal Variation (ADV)
non-stationarity
Circulació atmosfèrica -- Mesurament -- Informàtica -- Models matemàtics
Tractament del senyal -- Tècniques digitals -- Matemàtica
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
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oai_identifier_str oai:upcommons.upc.edu:2117/395883
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spelling Empirical mode decomposition of wind speed signalsPinto Molina, InesAtmospheric circulation -- Measurement -- Data processing -- Mathematical modelsSignal processing -- Digital techniques -- MathematicsEmpirical Mode Decomposition (EMD)Ensemble Empirical Mode Decomposition (EEMD)Intrinsic Mode Functions (IMFs)FourierAverage Diurnal Variation (ADV)Average Seasonal Variation (ADV)non-stationarityCirculació atmosfèrica -- Mesurament -- Informàtica -- Models matemàticsTractament del senyal -- Tècniques digitals -- MatemàticaÀrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyalEmpirical Mode Decomposition (EMD) is a powerful signal processing technique with diverse applications, particularly in the analysis of non-stationary data. In this study, we assess the capabilities of EMD for wind data analysis, aiming to uncover its effectiveness in capturing intricate temporal patterns and decomposing data into Intrinsic Mode Functions (IMFs) to identify crucial frequency components. Various methods of sifting have been studied as the IMFs and therefore results may vary according to the type. It has been concluded that the Ensemble Empirical Mode Decomposition (EEMD) is the most suitable method for these data. A comparison with Fourier analysis is also conducted to elucidate the strengths and limitations of each method. Furthermore, this investigation examines the Average Diurnal Variation (ADV) and Average Seasonal Variation (ASV) patterns within the wind data. It is found that these patters have a physical significance and interpretation of the IMFs and that it is easier to use EMD than Fourier for wind signals.Universitat Politècnica de CatalunyaPérez González, Juan JesúsRunacres, Mark20232023-10-0320232023-11-06master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/395883reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2http://creativecommons.org/licenses/by-nc-sa/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3958832026-05-27T15:37:01Z
dc.title.none.fl_str_mv Empirical mode decomposition of wind speed signals
title Empirical mode decomposition of wind speed signals
spellingShingle Empirical mode decomposition of wind speed signals
Pinto Molina, Ines
Atmospheric circulation -- Measurement -- Data processing -- Mathematical models
Signal processing -- Digital techniques -- Mathematics
Empirical Mode Decomposition (EMD)
Ensemble Empirical Mode Decomposition (EEMD)
Intrinsic Mode Functions (IMFs)
Fourier
Average Diurnal Variation (ADV)
Average Seasonal Variation (ADV)
non-stationarity
Circulació atmosfèrica -- Mesurament -- Informàtica -- Models matemàtics
Tractament del senyal -- Tècniques digitals -- Matemàtica
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
title_short Empirical mode decomposition of wind speed signals
title_full Empirical mode decomposition of wind speed signals
title_fullStr Empirical mode decomposition of wind speed signals
title_full_unstemmed Empirical mode decomposition of wind speed signals
title_sort Empirical mode decomposition of wind speed signals
dc.creator.none.fl_str_mv Pinto Molina, Ines
author Pinto Molina, Ines
author_facet Pinto Molina, Ines
author_role author
dc.contributor.none.fl_str_mv Pérez González, Juan Jesús
Runacres, Mark
dc.subject.none.fl_str_mv Atmospheric circulation -- Measurement -- Data processing -- Mathematical models
Signal processing -- Digital techniques -- Mathematics
Empirical Mode Decomposition (EMD)
Ensemble Empirical Mode Decomposition (EEMD)
Intrinsic Mode Functions (IMFs)
Fourier
Average Diurnal Variation (ADV)
Average Seasonal Variation (ADV)
non-stationarity
Circulació atmosfèrica -- Mesurament -- Informàtica -- Models matemàtics
Tractament del senyal -- Tècniques digitals -- Matemàtica
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
topic Atmospheric circulation -- Measurement -- Data processing -- Mathematical models
Signal processing -- Digital techniques -- Mathematics
Empirical Mode Decomposition (EMD)
Ensemble Empirical Mode Decomposition (EEMD)
Intrinsic Mode Functions (IMFs)
Fourier
Average Diurnal Variation (ADV)
Average Seasonal Variation (ADV)
non-stationarity
Circulació atmosfèrica -- Mesurament -- Informàtica -- Models matemàtics
Tractament del senyal -- Tècniques digitals -- Matemàtica
Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal
description Empirical Mode Decomposition (EMD) is a powerful signal processing technique with diverse applications, particularly in the analysis of non-stationary data. In this study, we assess the capabilities of EMD for wind data analysis, aiming to uncover its effectiveness in capturing intricate temporal patterns and decomposing data into Intrinsic Mode Functions (IMFs) to identify crucial frequency components. Various methods of sifting have been studied as the IMFs and therefore results may vary according to the type. It has been concluded that the Ensemble Empirical Mode Decomposition (EEMD) is the most suitable method for these data. A comparison with Fourier analysis is also conducted to elucidate the strengths and limitations of each method. Furthermore, this investigation examines the Average Diurnal Variation (ADV) and Average Seasonal Variation (ASV) patterns within the wind data. It is found that these patters have a physical significance and interpretation of the IMFs and that it is easier to use EMD than Fourier for wind signals.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-10-03
2023
2023-11-06
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/395883
url https://hdl.handle.net/2117/395883
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2

http://creativecommons.org/licenses/by-nc-sa/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300724

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