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

Citation Format:
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

Fulltext View|Download
Keywords: wind speed; time series; autoregressive models; wind turbine

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Investigating the effect of DG infeed on the effective cover of distance protection scheme in mixed-MV distribution network Cost optimization for the 100% renewable electricity scenario for the Java-Bali grid Prosopis juliflora pods mash for biofuel energy production: Implication for managing invasive species through utilization More recent articles
Most cited articles
Two-Phase Expander Approach for Next Generation of Heat Recovery Systems Thermal Decomposition and Kinetic Studies of Pyrolysis of Spirulina Platensis Residue Simulation of biogas utilization effect on the economic efficiency and greenhouse gas emission: a case study in Isfahan, Iran Evaluation and Modification of Processes for Bioethanol Separation and Production Sizing and Optimization for Hybrid Central in South Algeria Based on Three Different Generators More cited articles
  1. Cadenas, E. & W. Rivera (2010) Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model. Renewable Energy, 35, 2732-2738
  2. Durán Mario, J., D. Cros & J. Riquelme (2007) Short-Term Wind Power Forecast Based on ARX Models. Journal of Energy Engineering, 133, 172-180
  3. González-González, A., I. Etxeberria-Agiriano, E. Zulueta, F. Oterino-Echavarri & M. J. Lopez-Guede (2014) Pitch Based Wind Turbine Intelligent Speed Setpoint Adjustment Algorithms. Energies, 7
  4. Gualtieri, G. & S. Secci (2014) Extrapolating wind speed time series vs. Weibull distribution to assess wind resource to the turbine hub height: A case study on coastal location in Southern Italy. Renewable Energy, 62, 164-176
  5. Guerrero, E. Z., A. G. Gonzáez, J. M. Lopez-Guede & I. C. Gordillo (2011) Simulación basada en SMA de sistemas originalmente representados con EDO. Revista Iberoamericana de Automática e Informática Industrial RIAI, 8, 323-333
  6. Guo, Y., H. Gao & Q. Wu (2017) A meteorological information mining-based wind speed model for adequacy assessment of power systems with wind power. International Journal of Electrical Power & Energy Systems, 93, 406-413
  7. Hamilton, J. D. & R. Susmel (1994) Autoregressive conditional heteroskedasticity and changes in regime. Journal of Econometrics, 64, 307-333
  8. Li, H., Z. Hu, J. Wang & X. Meng (2018) Short-term fatigue analysis for tower base of a spar-type wind turbine under stochastic wind-wave loads. International Journal of Naval Architecture and Ocean Engineering, 10, 9-20
  9. Liu, J., G. Ren, J. Wan, Y. Guo & D. Yu (2016) Variogram time-series analysis of wind speed. Renewable Energy, 99, 483-491
  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
  12. Rajagopalan, S. & S. Santoso (2009) Wind power forecasting and error analysis using the autoregressive moving average modeling. 2009 IEEE Power & Energy Society General Meeting (PES), 6 pp.-6 pp
  13. Sedaghat, A., A. Hassanzadeh, J. Jamali, A. Mostafaeipour & W.-H. Chen (2017) Determination of rated wind speed for maximum annual energy production of variable speed wind turbines. Applied Energy, 205, 781-789
  14. Solari, S. & M. Á. Losada (2016) Simulation of non-stationary wind speed and direction time series. Journal of Wind Engineering and Industrial Aerodynamics, 149, 48-58
  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

Last update:

No citation recorded.

Last update: 2024-11-04 01:41:52

No citation recorded.