الفهرس 
IntrductionOnly 14 pages are availabe for public view
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Abstract
Recently the use of renewable energy resources has been
increased widely due to the advantages of having clean energy
resources and the reduction in oil, coal and natural gas availability.
Wind power, photovoltaic and fuel cell energy resources have been
recently the scope for power generation . Renewable energy
resources are often combined together to form a hybrid power
generation system. This hybrid system increases the stability and
reliability of the generated power. However, changes and
nonlinearity of environmental conditions reduce the efficiency of the
extracted power. So the development of a high accuracy controllers for
harvesting maximum power from generation systems becomes
necessary. Different traditional techniques and convention methods
have been developed to achieve this goal.
Early controllers that depend on artificial intelligent, evolutionary
and bio -inspired algorithms have been improved to track maximum
power. The main objective of this research is to enhance the power
transfer capability of grid interfaced hybrid generation system. This
hybrid system is a combination of photovoltaic, wind turbine, fuel cell
and battery energy systems. The wind, photovoltaic and fuel cell
energy systems acts as a primary power system. The battery is added
as a backup system to ensure continuous power supply and to take care
of the intermittent nature of wind and photovoltaic.
In order to get maximum and continuous output power from these
renewable energy systems at any instant of time, this thesis proposes