Wind Turbine Power Coefficient Identification Using the FAST Simulator Data and Design of Switching Multiple Model Predictive Control
Issue:
Volume 5, Issue 1, June 2021
Pages:
1-12
Received:
14 June 2020
Accepted:
1 June 2021
Published:
21 June 2021
DOI:
10.11648/j.cse.20210501.11
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Views:
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.
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...
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Critical Commentary on Deterministic Artificial Intelligence Applied to Oscillatory Circuits
Eric Miller,
Timothy Sands
Issue:
Volume 5, Issue 1, June 2021
Pages:
13-19
Received:
12 August 2021
Accepted:
21 August 2021
Published:
31 August 2021
DOI:
10.11648/j.cse.20210501.12
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Abstract: With heritage in nonlinear adaptive control (as proposed by Slotine) and physics-based control (as proposed by Lorenz), recently proposed methods referred to as deterministic artificial intelligence (D.A.I.) claim slight performance improvement over the parent methods. This brief communication firstly validates claims of slight improvement, but furthermore highlights a key feature: indications that improvements in observer implementations are the proper path for subsequent development in the field. The manuscript validates the recently published 97% performance improvement over classical methods using nonlinear adaptive methods, with an addition 0.23% performance improvement using D.A.I. compared to nonlinear adaptive control. Furthermore, the work also identifies strong correlation between system performance and observer performance, which is significant since D.A.I. eliminates controller tuning. Thus, observer improvement is recommended for future developments. The recently published 2-norm optimal learning scheme (of Smeresky) is recommended as the next step in the lineage of research in the discipline assuming augmentation with nonlinear state observers.
Abstract: With heritage in nonlinear adaptive control (as proposed by Slotine) and physics-based control (as proposed by Lorenz), recently proposed methods referred to as deterministic artificial intelligence (D.A.I.) claim slight performance improvement over the parent methods. This brief communication firstly validates claims of slight improvement, but fur...
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