Maximum Wind Energy Extraction by Using Neural Network Estimation and Predictive Control of Boost Converter

Document Type: Original Article

Author

Department of Electrical Engineering, Faculty of Engineering, University of Zabol, Zabol, Iran

Abstract

The power generation from wind turbine are variable because of dependence on environmental conditions and it is important to extract maximum energy from wind. This paper proposes a new method to extract maximum energy from wind turbine systems. The artificial neural network (ANN) is used to estimate the wind speed based on the rotor speed and the output power. In addition to ANN, a predictive controller is used to maximize the efficiency of the boost converter. In predictive controller, duty cycle of boost converter is controlled to obtain the maximum power point based on the slope method. One of the most interesting advantages of this controller is simplicity of control and implementation that is leads to fast response and exact tracking. The method has been developed and analyzed by utilizing a turbine directly driven permanent-magnet synchronous generator (PMSG). The simulation results verify the performance of the proposed method. Results show that this method maximizes wind energy extraction with more accuracy and fastness.

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