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Sensors (Basel). 2018 Apr 24;18(5). pii: E1317. doi: 10.3390/s18051317.

Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator.

Author information

1
Laboratory of Research in Automatic Control-LA.R.A, National Engineering School of Tunis (ENIT), University of Tunis El Manar, BP 37, Le Belvédère, Tunis 1002, Tunisia. kghefiri001@ikasle.ehu.eus.
2
Automatic Control Group-ACG, Department of Automatic Control and Systems Engineering, Engineering School of Bilbao, University of the Basque Country, 48012 Bilbao, Spain. kghefiri001@ikasle.ehu.eus.
3
Laboratory of Research in Automatic Control-LA.R.A, National Engineering School of Tunis (ENIT), University of Tunis El Manar, BP 37, Le Belvédère, Tunis 1002, Tunisia. soufiene.bouallegue@issig.rnu.tn.
4
Automatic Control Group-ACG, Department of Automatic Control and Systems Engineering, Engineering School of Bilbao, University of the Basque Country, 48012 Bilbao, Spain. izaskun.garrido@ehu.es.
5
Automatic Control Group-ACG, Department of Automatic Control and Systems Engineering, Engineering School of Bilbao, University of the Basque Country, 48012 Bilbao, Spain. aitor.garrido@ehu.es.
6
Laboratory of Research in Automatic Control-LA.R.A, National Engineering School of Tunis (ENIT), University of Tunis El Manar, BP 37, Le Belvédère, Tunis 1002, Tunisia. joseph.haggege@enit.rnu.tn.

Abstract

Artificial intelligence technologies are widely investigated as a promising technique for tackling complex and ill-defined problems. In this context, artificial neural networks methodology has been considered as an effective tool to handle renewable energy systems. Thereby, the use of Tidal Stream Generator (TSG) systems aim to provide clean and reliable electrical power. However, the power captured from tidal currents is highly disturbed due to the swell effect and the periodicity of the tidal current phenomenon. In order to improve the quality of the generated power, this paper focuses on the power smoothing control. For this purpose, a novel Artificial Neural Network (ANN) is investigated and implemented to provide the proper rotational speed reference and the blade pitch angle. The ANN supervisor adequately switches the system in variable speed and power limitation modes. In order to recover the maximum power from the tides, a rotational speed control is applied to the rotor side converter following the Maximum Power Point Tracking (MPPT) generated from the ANN block. In case of strong tidal currents, a pitch angle control is set based on the ANN approach to keep the system operating within safe limits. Two study cases were performed to test the performance of the output power. Simulation results demonstrate that the implemented control strategies achieve a smoothed generated power in the case of swell disturbances.

KEYWORDS:

Doubly Fed Induction Generator (DFIG); Maximum Power Point Tracking (MPPT); Tidal Stream Generator (TSG); artificial intelligence; artificial neural networks control; back-to-back converter; data processing; pitch regulation; power control

PMID:
29695127
PMCID:
PMC5982424
DOI:
10.3390/s18051317
[Indexed for MEDLINE]
Free PMC Article

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