Name
Title | ||
|---|---|---|
Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator. |
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.3390/s18051317 | SENSORS |
Keywords | Field | DocType |
artificial intelligence,artificial neural networks control,back-to-back converter,data processing,Doubly Fed Induction Generator (DFIG),Maximum Power Point Tracking (MPPT),pitch regulation,power control,Tidal Stream Generator (TSG) | Electric power,Power control,Blade pitch,Maximum power point tracking,Electronic engineering,Engineering,Maximum power principle,Artificial neural network,Electricity generation,Wind power | Journal |
Volume | Issue | Citations |
18 | 5.0 | 1 |
PageRank | References | Authors |
0.37 | 11 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Khaoula Ghefiri | 1 | 1 | 0.37 |
| Soufiene Bouallègue | 2 | 7 | 3.81 |
| Izaskun Garrido | 3 | 21 | 8.99 |
| Aitor J. Garrido | 4 | 22 | 11.11 |
| joseph haggege | 5 | 36 | 4.83 |