Wind Maximum Power Point Prediction and Tracking Using Artificial Neural Network and Maximum Rotation Speed Method

The power characteristic of a wind turbine is naturally nonlinear, because the position of the maximum power varies with the wind speed, for each wind speed, it is necessary that the system finds the maximum power. To approach this goal, a specific command must be used. In this paper, a variable speed wind generator maximum power point tracking (MPPT) based on artificial neural network (ANN) is presented.

[1]  A. Moussi,et al.  Neural Network Use in the MPPT of Photovoltaic Pumping System , 2003 .

[2]  Okyay Kaynak,et al.  A novel analysis and design of a neural network assisted nonlinear controller for a bioreactor , 1999 .

[3]  Mohammed Ouassaid,et al.  Modelling and optimal power control for permanent magnet synchronous generator wind turbine system connected to utility grid with fault conditions , 2015 .

[4]  Soteris A. Kalogirou,et al.  Artificial neural networks in renewable energy systems applications: a review , 2001 .

[5]  A. Boudghene Stambouli,et al.  A variable speed wind generator maximum power tracking based on adaptative neuro-fuzzy inference system , 2011, Expert Syst. Appl..

[6]  Mamadou Lamine Doumbia,et al.  Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator) , 2014 .

[7]  Godpromesse Kenné,et al.  A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction , 2016 .

[8]  Kai Shi,et al.  Wide-Speed-Range-Operation Dual Stator-Winding Induction Generator DC Generating System for Wind Power Applications , 2015, IEEE Transactions on Power Electronics.

[9]  Jamel Belhadj,et al.  Energy output estimation of hybrid Wind-Photovoltaic power system using statistical distributions JES , 2014 .

[10]  João M.A. Rebello,et al.  The use of artificial neural network in the classification of pulse-echo and TOFD ultra-sonic signals , 2005 .

[11]  Abdul Azim Sobaih,et al.  An intelligent maximum power extraction algorithm for hybrid wind–diesel-storage system , 2010 .

[12]  Zhu Xin-jian,et al.  Nonlinear modeling of PEMFC based on neural networks identification , 2005 .

[13]  Wei Li,et al.  Sliding mode voltage control strategy for capturing maximum wind energy based on fuzzy logic control , 2015 .

[14]  Rasit Ata,et al.  Artificial neural networks applications in wind energy systems: a review , 2015 .