Research Article
Microgrid dynamic responses enhancement using artificial neural network-genetic algorithm for photovoltaic system and fuzzy controller for high wind speeds
Alireza Rezvani,
Maziar Izadbakhsh,
Majid Gandomkar,
Corresponding Author
Alireza Rezvani
Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran
Correspondence to: A. Rezvani, Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran.
E-mail: [email protected]
Search for more papers by this authorMaziar Izadbakhsh
Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran
Search for more papers by this authorMajid Gandomkar
Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
Search for more papers by this authorAlireza Rezvani,
Maziar Izadbakhsh,
Majid Gandomkar,
Corresponding Author
Alireza Rezvani
Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran
Correspondence to: A. Rezvani, Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran.
E-mail: [email protected]
Search for more papers by this authorMaziar Izadbakhsh
Young Researchers and Elite Club, Saveh Branch, Islamic Azad University, Saveh, Iran
Search for more papers by this authorMajid Gandomkar
Department of Electrical Engineering, Saveh Branch, Islamic Azad University, Saveh, Iran
Search for more papers by this authorAbstract
The microgrid (MG) is described as an electrical network of small modular distributed generation, energy storage devices and controllable loads. In order to maximize the output of solar arrays, maximum power point tracking (MPPT) technique is used by artificial neural network (ANN), and also, control of turbine output power in high wind speeds is proposed using pitch angle control technic by fuzzy logic. To track the maximum power point (MPP) in the photovoltaic (PV), the proposed ANN is trained by the genetic algorithm (GA). In other word, the data are optimized by GA, and then these optimum values are used in ANN. The simulation results show that the ANN-GA in comparison with the conventional algorithms with high accuracy can track the peak power point under different insolation conditions and meet the load demand with less fluctuation around the MPP; also it can increase convergence speed to achieve MPP. Moreover, pitch angle controller based on fuzzy logic with wind speed and active power as inputs that have faster responses which leads to have flatter power curves enhances the dynamic responses of wind turbine. The models are developed and applied in Matlab/Simulink. Copyright © 2015 John Wiley & Sons, Ltd.
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