Multi-Layer neural network for sensorless MPPT control for Wind Energy Conversion System using Doubly Fed Twin Stator Induction Gnenerator

In this paper we present a new sensorless control for Wind Energy Conversion System (WECS) based on Doubly Fed Twin Stator Induction Generator (DFTSIG). The aim of this control is to extract the maximum wind power and to control the power generation only by measuring phase voltages and currents. The proposed control scheme is ensured without the need of using any rotor speed or wind sensors from the aspect of reliability and increase in Cost. For that, a Multi-Layers neural network observer is introduced to estimate the rotor speed. To impose the maximum power point MPP, an extended order Luenberger observer was introduced in cascade with the neural observer to estimate the optimal aerodynamic torque. After, an approximation with another Artificial Neuronal Network observer makes it possible to estimate the wind velocity. This later will be injected in the algorithm control to impose the MPPT. Simulation results demonstrate the sensorless control device excellent performances.