DOI: https://doi.org/10.14710/ijred.7.3.199-204

Longitudinal wind speed time series generation to wind turbine controllers tuning

1Tecnalia Research & Innovation, Industry and Transport Division, Parque Tecnológico de Álava Leonardo Da Vinci, 11, E-01510 Miñano (Spain), Spain

2Department of Systems Engineering and Automatic Department, Faculty of Engineering of Vitoria-Gasteiz, University of the Basque Country (UPV/EHU), Vitoria-Gasteiz, Spain

Published: 15 Dec 2018.
Editor(s): H Hadiyanto

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Abstract

Although there are a wide variety of applications that require wind speed time series (WSTS), this paper emphases on WSTS to be used into wind turbine controllers tuning. These simulations involve several WSTS to perform a proper assessment. These WSTS must assure realistic wind speed variations such as wind gusts and include some rare events such as extreme wind situations. The architecture proposed to generate this WSTS is based on autoregressive models with certain post-processing. The methodology used is entirely described by precise notation as well as it is parametrized by means of data gathered from a weather station. Two main different simulations are performed and assessment; the first simulation is fed by weather data with high wind speed and great variability. The second simulation, on the opposite, use calm wind speed as a data source.

Article History: Received 1st  June 2018; Received in revised form Sept 6th 2018; Accepted October 10th 2018; Available online

How to Cite This Article: González, A.G. and Guede, J.M.L. (2018) Longitudinal Wind Speed Time Series Generated With Autoregressive Methods For Wind Turbine Control.  International Journal of Renewable Energy Development, 7(3), 199-204.

https://doi.org/10.14710/ijred.7.3.199-204

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Keywords: wind speed; time series; autoregressive models; wind turbine

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Section: Original Research Article
Language : EN
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  10. Lojowska, A., D. Kurowicka, G. Papaefthymiou & L. van der Sluis (2010) Advantages of ARMA-GARCH Wind Speed Time Series Modeling. 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), 83-8
  11. Peng, X., W. Zheng, D. Zhang, Y. Liu, D. Lu & L. Lin (2017) A novel probabilistic wind speed forecasting based on combination of the adaptive ensemble of on-line sequential ORELM (Outlier Robust Extreme Learning Machine) and TVMCF (time-varying mixture copula function). Energy Conversion and Management, 138, 587-602
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  15. Sun, G., C. Jiang, P. Cheng, Y. Liu, X. Wang, Y. Fu & Y. He (2018) Short-term wind power forecasts by a synthetical similar time series data mining method. Renewable Energy, 115, 575-584

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