Artificial Neural Networks Applied to a Wind Energy System
Randriamanantenasoa Njeva1, Chrysostome Andrianantenaina2, Jean Claude Rakotoarisoa3, Jean Nirinarison Razafinjaka4

1Randriamanantenasoa Njeva*, Department of Electricity, University of Antsiranana, Antalaha, Madagascar.
2Chrysostome Andrianantenaina, Department, Department of Electricity, University of Antsiranana, Mahajanga, Madagascar.
3Jean Claude Rakotoarisoa, Department of Electricity, Higher Polytechnic School Antsiranana,, University of Antsiranana , Antsiranana Madagascar.
4Jean Nirinarison Razafinjaka, Department of Electricity, Higher Polytechnic School Antsiranana,, Univesity of Antsiranana , Antsiranana, Madagascar.

Manuscript received on October 06, 2020. | Revised Manuscript received on October 25, 2020. | Manuscript published on November 30, 2020. | PP:416-420 | Volume-9 Issue-4, November 2020. | Retrieval Number: 100.1/ijrte.C4670099320 | DOI: 10.35940/ijrte.C4670.119420
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Abstract: In this context, we are taking a close interest in the optimization of wind energy production. It consists in designing simple to implement control strategies of a wind energy conversion system, connected to the network based on the Double Fed Induction Generator (DFIG) driven by the Converter Machine Side (CSM) in order to improve the performance of Direct Torque Control (DTC) and Direct Power Control (DPC). For this purpose, the artificial neural networks (ANNs) is used. Hysteresis comparators and voltage vector switching tables have been replaced by a comparator based on artificial neural networks. The same structure is adopted to build the two neural controllers, for the DTC – ANN and for the DPC – ANN. The simulation results show that the combination of classical and artificial neural network methods permit a double advantage: remarkable performances compared to the DTC-C and DPC-C and a significant reduction of the fluctuations of the output quantities of the DFIG and especially the improvement of the harmonics rate currents generated by the machine. 
Keywords: MPPT, wind energy, DFIG, Artificial Neural Network, optimization.