Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm
Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization...
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doaj-5aedbf80f0c543fc881781e760c117302021-03-29T22:50:51ZengIEEEIEEE Access2169-35362019-01-017308903090410.1109/ACCESS.2019.29022428656477Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering AlgorithmAngel Molina-Garcia0https://orcid.org/0000-0001-6824-8684Ana Fernandez-Guillamon1https://orcid.org/0000-0003-2605-3269Emilio Gomez-Lazaro2https://orcid.org/0000-0002-3620-3921Andres Honrubia-Escribano3https://orcid.org/0000-0002-9756-8641Maria C. Bueso4https://orcid.org/0000-0001-6897-7430Department of Electrical Engineering, Universidad Politécnica de Cartagena, Cartagena, SpainDepartment of Electrical Engineering, Universidad Politécnica de Cartagena, Cartagena, SpainRenewable Energy Research Institute, DIEEAC, EDII-AB, Universidad de Castilla-La Mancha, Albacete, SpainRenewable Energy Research Institute, DIEEAC, EDII-AB, Universidad de Castilla-La Mancha, Albacete, SpainDepartment of Applied Mathematics and Statistics, Universidad Politécnica de Cartagena, Cartagena, SpainWind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. This paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on $z$ -normalization, shape-based distance metric solution, and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using commercial Windcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100 000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes.https://ieeexplore.ieee.org/document/8656477/Clustering algorithmspatterns clusteringwind power generation |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Angel Molina-Garcia Ana Fernandez-Guillamon Emilio Gomez-Lazaro Andres Honrubia-Escribano Maria C. Bueso |
spellingShingle |
Angel Molina-Garcia Ana Fernandez-Guillamon Emilio Gomez-Lazaro Andres Honrubia-Escribano Maria C. Bueso Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm IEEE Access Clustering algorithms patterns clustering wind power generation |
author_facet |
Angel Molina-Garcia Ana Fernandez-Guillamon Emilio Gomez-Lazaro Andres Honrubia-Escribano Maria C. Bueso |
author_sort |
Angel Molina-Garcia |
title |
Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm |
title_short |
Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm |
title_full |
Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm |
title_fullStr |
Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm |
title_full_unstemmed |
Vertical Wind Profile Characterization and Identification of Patterns Based on a Shape Clustering Algorithm |
title_sort |
vertical wind profile characterization and identification of patterns based on a shape clustering algorithm |
publisher |
IEEE |
series |
IEEE Access |
issn |
2169-3536 |
publishDate |
2019-01-01 |
description |
Wind power plants are becoming a generally accepted resource in the generation mix of many utilities. At the same time, the size and the power rating of individual wind turbines have increased considerably. Under these circumstances, the sector is increasingly demanding an accurate characterization of vertical wind speed profiles to estimate properly the incoming wind speed at the rotor swept area and, consequently, assess the potential for a wind power plant site. This paper describes a shape-based clustering characterization and visualization of real vertical wind speed data. The proposed solution allows us to identify the most likely vertical wind speed patterns for a specific location based on real wind speed measurements. Moreover, this clustering approach also provides characterization and classification of such vertical wind profiles. This solution is highly suitable for a large amount of data collected by remote sensing equipment, where wind speed values at different heights within the rotor swept area are available for subsequent analysis. The methodology is based on $z$ -normalization, shape-based distance metric solution, and the Ward-hierarchical clustering method. Real vertical wind speed profile data corresponding to a Spanish wind power plant and collected by using commercial Windcube equipment during several months are used to assess the proposed characterization and clustering process, involving more than 100 000 wind speed data values. All analyses have been implemented using open-source R-software. From the results, at least four different vertical wind speed patterns are identified to characterize properly over 90% of the collected wind speed data along the day. Therefore, alternative analytical function criteria should be subsequently proposed for vertical wind speed characterization purposes. |
topic |
Clustering algorithms patterns clustering wind power generation |
url |
https://ieeexplore.ieee.org/document/8656477/ |
work_keys_str_mv |
AT angelmolinagarcia verticalwindprofilecharacterizationandidentificationofpatternsbasedonashapeclusteringalgorithm AT anafernandezguillamon verticalwindprofilecharacterizationandidentificationofpatternsbasedonashapeclusteringalgorithm AT emiliogomezlazaro verticalwindprofilecharacterizationandidentificationofpatternsbasedonashapeclusteringalgorithm AT andreshonrubiaescribano verticalwindprofilecharacterizationandidentificationofpatternsbasedonashapeclusteringalgorithm AT mariacbueso verticalwindprofilecharacterizationandidentificationofpatternsbasedonashapeclusteringalgorithm |
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1724190660467097600 |