Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control

Saman Saki; Ali Azizi

doi:doi:10.11648/j.cse.20210501.11

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Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control

Saman Saki, Ali Azizi

Published in Control Science and Engineering (Volume 5, Issue 1)

Received: 14 June 2020 Accepted: 1 June 2021 Published: 21 June 2021

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Abstract

Due to the economic aspects and the global warming aims, the wind turbines have attracted a notable percent of the research subjects in the recent decades. The motivation of this paper is identification of the Wind Turbine (WT) power coefficient curve and improvement of the power tracking performance. To accomplish the first, using the steady state mode of the Fatigue Aerodynamics Structures and Turbulence (FAST) simulator, we collect the necessary data pack and, then, identify the power coefficient curve. For the second aim, a Multiple Model Predictive Control (MMPC) with a new adaptive structure is designed. The model selection, through the constructed model bank, is handled based on the estimated wind speed using the Newton-Rapshon (NR) and the kalman filter algorithm. The new adaptation law based on the Lyapunove theory damps the hazardous chattering in the control signal coming from the sudden switching between controllers and models. This will improve the wind turbine longevity. Afterwards, to investigate the effectiveness of the given method, the suggested algorithm is implemented on the NREL 1.5 MW baseline WT using the FAST simulator. Finally, the simulation results validate the efficiency of the suggested control system in the tracking error improvement, oscillation reduction in the generator torque and consequently mechanical power, simultaneously.

Published in	Control Science and Engineering (Volume 5, Issue 1)
DOI	10.11648/j.cse.20210501.11
Page(s)	1-12
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Multiple Model Predictive Control, Renewable Energy Systems, Oscillation Reduction, Power Coefficient Identification

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APA Style

Saman Saki, Ali Azizi. (2021). Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control. Control Science and Engineering, 5(1), 1-12. https://doi.org/10.11648/j.cse.20210501.11

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ACS Style

Saman Saki; Ali Azizi. Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control. Control Sci. Eng. 2021, 5(1), 1-12. doi: 10.11648/j.cse.20210501.11

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AMA Style

Saman Saki, Ali Azizi. Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control. Control Sci Eng. 2021;5(1):1-12. doi: 10.11648/j.cse.20210501.11

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@article{10.11648/j.cse.20210501.11,
  author = {Saman Saki and Ali Azizi},
  title = {Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control},
  journal = {Control Science and Engineering},
  volume = {5},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.cse.20210501.11},
  url = {https://doi.org/10.11648/j.cse.20210501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20210501.11},
  abstract = {Due to the economic aspects and the global warming aims, the wind turbines have attracted a notable percent of the research subjects in the recent decades. The motivation of this paper is identification of the Wind Turbine (WT) power coefficient curve and improvement of the power tracking performance. To accomplish the first, using the steady state mode of the Fatigue Aerodynamics Structures and Turbulence (FAST) simulator, we collect the necessary data pack and, then, identify the power coefficient curve. For the second aim, a Multiple Model Predictive Control (MMPC) with a new adaptive structure is designed. The model selection, through the constructed model bank, is handled based on the estimated wind speed using the Newton-Rapshon (NR) and the kalman filter algorithm. The new adaptation law based on the Lyapunove theory damps the hazardous chattering in the control signal coming from the sudden switching between controllers and models. This will improve the wind turbine longevity. Afterwards, to investigate the effectiveness of the given method, the suggested algorithm is implemented on the NREL 1.5 MW baseline WT using the FAST simulator. Finally, the simulation results validate the efficiency of the suggested control system in the tracking error improvement, oscillation reduction in the generator torque and consequently mechanical power, simultaneously.},
 year = {2021}
}

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TY  - JOUR
T1  - Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control
AU  - Saman Saki
AU  - Ali Azizi
Y1  - 2021/06/21
PY  - 2021
N1  - https://doi.org/10.11648/j.cse.20210501.11
DO  - 10.11648/j.cse.20210501.11
T2  - Control Science and Engineering
JF  - Control Science and Engineering
JO  - Control Science and Engineering
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2994-7421
UR  - https://doi.org/10.11648/j.cse.20210501.11
AB  - Due to the economic aspects and the global warming aims, the wind turbines have attracted a notable percent of the research subjects in the recent decades. The motivation of this paper is identification of the Wind Turbine (WT) power coefficient curve and improvement of the power tracking performance. To accomplish the first, using the steady state mode of the Fatigue Aerodynamics Structures and Turbulence (FAST) simulator, we collect the necessary data pack and, then, identify the power coefficient curve. For the second aim, a Multiple Model Predictive Control (MMPC) with a new adaptive structure is designed. The model selection, through the constructed model bank, is handled based on the estimated wind speed using the Newton-Rapshon (NR) and the kalman filter algorithm. The new adaptation law based on the Lyapunove theory damps the hazardous chattering in the control signal coming from the sudden switching between controllers and models. This will improve the wind turbine longevity. Afterwards, to investigate the effectiveness of the given method, the suggested algorithm is implemented on the NREL 1.5 MW baseline WT using the FAST simulator. Finally, the simulation results validate the efficiency of the suggested control system in the tracking error improvement, oscillation reduction in the generator torque and consequently mechanical power, simultaneously.
VL  - 5
IS  - 1
ER  -

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Author Information

Saman Saki

Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran
Ali Azizi

Department of Electrical Engineering, University of Kurdistan, Sanandaj, Iran

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APA Style

Saman Saki, Ali Azizi. (2021). Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control. Control Science and Engineering, 5(1), 1-12. https://doi.org/10.11648/j.cse.20210501.11

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ACS Style

Saman Saki; Ali Azizi. Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control. Control Sci. Eng. 2021, 5(1), 1-12. doi: 10.11648/j.cse.20210501.11

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AMA Style

Saman Saki, Ali Azizi. Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control. Control Sci Eng. 2021;5(1):1-12. doi: 10.11648/j.cse.20210501.11

Copy | Download

@article{10.11648/j.cse.20210501.11,
  author = {Saman Saki and Ali Azizi},
  title = {Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control},
  journal = {Control Science and Engineering},
  volume = {5},
  number = {1},
  pages = {1-12},
  doi = {10.11648/j.cse.20210501.11},
  url = {https://doi.org/10.11648/j.cse.20210501.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cse.20210501.11},
  abstract = {Due to the economic aspects and the global warming aims, the wind turbines have attracted a notable percent of the research subjects in the recent decades. The motivation of this paper is identification of the Wind Turbine (WT) power coefficient curve and improvement of the power tracking performance. To accomplish the first, using the steady state mode of the Fatigue Aerodynamics Structures and Turbulence (FAST) simulator, we collect the necessary data pack and, then, identify the power coefficient curve. For the second aim, a Multiple Model Predictive Control (MMPC) with a new adaptive structure is designed. The model selection, through the constructed model bank, is handled based on the estimated wind speed using the Newton-Rapshon (NR) and the kalman filter algorithm. The new adaptation law based on the Lyapunove theory damps the hazardous chattering in the control signal coming from the sudden switching between controllers and models. This will improve the wind turbine longevity. Afterwards, to investigate the effectiveness of the given method, the suggested algorithm is implemented on the NREL 1.5 MW baseline WT using the FAST simulator. Finally, the simulation results validate the efficiency of the suggested control system in the tracking error improvement, oscillation reduction in the generator torque and consequently mechanical power, simultaneously.},
 year = {2021}
}

Copy | Download

TY  - JOUR
T1  - Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control
AU  - Saman Saki
AU  - Ali Azizi
Y1  - 2021/06/21
PY  - 2021
N1  - https://doi.org/10.11648/j.cse.20210501.11
DO  - 10.11648/j.cse.20210501.11
T2  - Control Science and Engineering
JF  - Control Science and Engineering
JO  - Control Science and Engineering
SP  - 1
EP  - 12
PB  - Science Publishing Group
SN  - 2994-7421
UR  - https://doi.org/10.11648/j.cse.20210501.11
AB  - Due to the economic aspects and the global warming aims, the wind turbines have attracted a notable percent of the research subjects in the recent decades. The motivation of this paper is identification of the Wind Turbine (WT) power coefficient curve and improvement of the power tracking performance. To accomplish the first, using the steady state mode of the Fatigue Aerodynamics Structures and Turbulence (FAST) simulator, we collect the necessary data pack and, then, identify the power coefficient curve. For the second aim, a Multiple Model Predictive Control (MMPC) with a new adaptive structure is designed. The model selection, through the constructed model bank, is handled based on the estimated wind speed using the Newton-Rapshon (NR) and the kalman filter algorithm. The new adaptation law based on the Lyapunove theory damps the hazardous chattering in the control signal coming from the sudden switching between controllers and models. This will improve the wind turbine longevity. Afterwards, to investigate the effectiveness of the given method, the suggested algorithm is implemented on the NREL 1.5 MW baseline WT using the FAST simulator. Finally, the simulation results validate the efficiency of the suggested control system in the tracking error improvement, oscillation reduction in the generator torque and consequently mechanical power, simultaneously.
VL  - 5
IS  - 1
ER  -

Copy | Download