Hybrid Power Systems Energy Management Based on Artificial Intelligence

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Journal Title, Volume, Page: 
PhD Thesis
Year of Publication: 
Emad Maher Natsheh
Current Affiliation: 
Department of Computer Engineering, Faculty of Engineering and Information Technology, An-Najah National University, Nablus, Palestine
Preferred Abstract (Original): 

The thesis presents a novel adaptive scheme for energy management in stand-alone hybrid power  systems. The proposed management system is designed to manage the power flow between the hybrid  power system and energy storage elements in order to satisfy the load requirements based on artificial  neural network (ANN) and fuzzy logic controllers. The neural network controller is employed to achieve  the maximum power point (MPP) for different types of photovoltaic (PV) panels. The advance fuzzy logic  controller is developed to distribute the power among the hybrid system and to manage the charge and  discharge current flow for performance optimization. The developed management system performance  was assessed using a hybrid system comprised PV panels, wind turbine (WT), battery storage, and proton  exchange membrane fuel cell (PEMFC).