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

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## Abstract

**:**

## 1. Introduction

_{2}emissions annually [13]. The worldwide potential for wave and tidal of renewable energy is approximately 337 GW [14].

## 2. Fluctuation Aspects of Tidal Power

## 3. Control Objectives and Modeling of the Tidal Stream Generator

#### 3.1. Control Problem Statement

#### 3.2. Tidal Turbine Model

#### 3.3. Shaft Model

#### 3.4. Generator Model

#### 3.5. Power Converters Model

## 4. Artificial Neural Networks Based-Planning Control Trajectories

#### 4.1. Multi-Layer ANN Control Design

#### 4.2. Training Performance of ANN-Based Controller

## 5. Rotational Speed Control of TSG System

#### 5.1. Rotor Side Converter Control

#### 5.2. Grid Side Converter Control

## 6. Validation Tests and Discussion

#### 6.1. Comparative Study between the Switching and ANN-Based Controls

#### 6.2. Robustness of the ANN-Based Control against Swell Effects

## 7. Conclusions

## Author Contributions

## Acknowledgments

## Conflicts of Interest

## Appendix A

Neurons Number in Hidden Layer | Epochs | MSE |
---|---|---|

13 | $0.032676$ | |

18 | $0.049599$ | |

2 | 21 | $0.036628$ |

35 | $0.03845$ | |

41 | $0.029477$ | |

58 | $0.034446$ | |

70 | $0.0004752$ | |

153 | $0.00038727$ | |

5 | 187 | $0.00053197$ |

225 | $0.00049101$ | |

326 | $0.000532$ | |

714 | $0.0001963$ | |

69 | $0.00016313$ | |

138 | $0.00019381$ | |

7 | 222 | $0.0002327$ |

653 | $0.00017829$ | |

751 | $0.00014904$ | |

1000 | $0.00017071$ | |

118 | $0.00012838$ | |

361 | $9.1106\times {10}^{-5}$ | |

10 | 649 | $8.7021\times {10}^{-5}$ |

720 | $6.2352\times {10}^{-5}$ | |

955 | $4.3441\times {10}^{-5}$ | |

1000 | $3.8614\times {10}^{-5}$ | |

115 | $0.00019116$ | |

157 | $0.00014699$ | |

11 | 241 | $0.00011463$ |

623 | $9.5236\times {10}^{-5}$ | |

904 | $8.9541\times {10}^{-5}$ | |

1000 | $5.3738\times {10}^{-5}$ |

Turbine | Drive-Train | DFIG | Converter |
---|---|---|---|

$\rho $ = 1027kg/m${}^{3}$ | ${H}_{t}$ = 3 s | ${P}_{n}$ = 1.5MW | ${V}_{dc}$ = 1150V |

R = 8m | ${H}_{g}$ = 0.5 s | ${U}_{rms}$ = 690V | C = 0.01F |

${C}_{pmax}=0.44$ | ${K}_{sh}=2\times {10}^{6}$ Nm/rad | ${f}_{req}$ = 50Hz | |

${\lambda}_{opt}=6.96$ | ${D}_{sh}=3.5\phantom{\rule{0.166667em}{0ex}}\times {10}^{5}$ Nms/rad | ${R}_{s}=2.63\phantom{\rule{0.277778em}{0ex}}\mathrm{m}\phantom{\rule{1.0pt}{0ex}}\Omega $ | |

${V}_{n}$ = 3.2m/s | ${R}_{r}=2.63\phantom{\rule{0.277778em}{0ex}}\mathrm{m}\phantom{\rule{1.0pt}{0ex}}\Omega $ | Choke | |

${L}_{s}=0.168\phantom{\rule{0.277778em}{0ex}}\mathrm{mH}$ | ${R}_{g}=0.595\phantom{\rule{0.277778em}{0ex}}\mathrm{m}\Omega $ | ||

${L}_{r}=0.133\phantom{\rule{0.277778em}{0ex}}\mathrm{mH}$ | ${L}_{g}=0.157\phantom{\rule{0.277778em}{0ex}}\mathrm{mH}$ | ||

${L}_{m}=5.474\phantom{\rule{0.277778em}{0ex}}\mathrm{mH}$ | |||

$p=2$ |

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**Figure 14.**Control performances in scenario 1: (

**a**) power coefficient response; (

**b**) pitch angle response.

**Figure 18.**Control performances in scenario 2: (

**a**) power coefficient response; (

**b**) pitch angle response.

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**MDPI and ACS Style**

Ghefiri, K.; Bouallègue, S.; Garrido, I.; Garrido, A.J.; Haggège, J.
Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator. *Sensors* **2018**, *18*, 1317.
https://doi.org/10.3390/s18051317

**AMA Style**

Ghefiri K, Bouallègue S, Garrido I, Garrido AJ, Haggège J.
Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator. *Sensors*. 2018; 18(5):1317.
https://doi.org/10.3390/s18051317

**Chicago/Turabian Style**

Ghefiri, Khaoula, Soufiene Bouallègue, Izaskun Garrido, Aitor J. Garrido, and Joseph Haggège.
2018. "Multi-Layer Artificial Neural Networks Based MPPT-Pitch Angle Control of a Tidal Stream Generator" *Sensors* 18, no. 5: 1317.
https://doi.org/10.3390/s18051317