Maximum power tracking control for a wind energy conversion system based on a quasi-ARX neural network model

2015 ◽  
Vol 10 (4) ◽  
pp. 368-375 ◽  
Author(s):  
Mohammad Abu Jami'in ◽  
Imam Sutrisno ◽  
Jinglu Hu
Keyword(s):  
Neural Network ◽  
Wind Energy ◽  
Energy Conversion ◽  
Neural Network Model ◽  
Tracking Control ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
Conversion System ◽  
Maximum Power Tracking ◽  
Energy Conversion System

Intelligent maximum power tracking control of a PMSG wind energy conversion system

2019 ◽  
Vol 21 (4) ◽  
pp. 1980-1990 ◽  
Author(s):  
Aicha Asri ◽  
Youcef Mihoub ◽  
Said Hassaine ◽  
Pierre‐Olivier Logerais ◽  
Tayeb Allaoui
Keyword(s):  
Wind Energy ◽  
Energy Conversion ◽  
Tracking Control ◽  
Maximum Power ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
Conversion System ◽  
Maximum Power Tracking ◽  
Energy Conversion System

Artificial neural network–based sensorless control of wind energy conversion system driving a permanent magnet synchronous generator

2020 ◽  
pp. 0309524X2090325 ◽  
Author(s):  
Omessaad Elbeji ◽  
Marwa Hannachi ◽  
Mouna Benhamed ◽  
Lassaad Sbita
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wind Energy ◽  
Energy Conversion ◽  
Sensorless Control ◽  
Maximum Power ◽  
Wind Energy Conversion System ◽  
Wind Energy Conversion ◽  
Conversion System ◽  
Energy Conversion System

Due to the wind characteristic, wind speed measure requires more than one sensor. However, to track the maximum power point of the wind, knowing the wind speed or mechanical speed is necessary. So, the solution is to use a sensorless control. This article is mainly focused on a sensorless control of a wind energy conversion system that employs an artificial neural network observer. The detailed mathematical model of the studied system is presented. It includes a permanent magnet synchronous generator. The contribution of the studied wind energy conversion system is to integrate a three-cell DC–DC converter. For the generation of maximum power from the wind, an algorithm to track the maximum power is developed. Then, to avoid the disadvantages of using sensors, an artificial neural network observer is implemented. The capabilities and contributions of the proposed control scheme are demonstrated by simulation results using MATLAB/Simulink.

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