A New Maximum Power Point Tracking Based on Neural Networks and Incremental Conductance for Wind Energy Conversion System

¹ ENSAO, Mohammed First University, Renewable Energy Embedded Systems and Artificial Intelligence Team (SEERIA), Oujda, Morocco
² Faculty of Science Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Computer Science, Signal, Automation and Cognitivism Laboratory (LISAC), Fez, Morocco

^* Corresponding author: hayatel89@gmail.com

Abstract

This work presents a new Maximum Power Point Tracking (MPPT) for the connection of the wind turbine system (WT) to the synchronous permanent magnet generator (PMSG). To search the maximum power of the wind turbine, we have proposed a new MPPT which combines two techniques: Artificial Neural Network (ANN) and incremental conductance (IncCond) method. The advantage of ANN-based WT model method is the fast MPP approximation base on the ability of ANN according the parameters of WT that used. The advantage of IncCond method is the ability to search the exactly MPP based on the feedback voltage. In our case the ANN is employed to predict the maximum voltage of the WT, under different values of wind speed, and the control of DC–DC boost converter operation is executed by applying incremental conductance (IncCond) technique. The proposed system includes a wind turbine associated to a permanent magnet synchronous generator (PMSG), a rectifier and a DC-DC converter with MPPT control. The proposed algorithm is tested under MATLAB SIMULINK.