الفهرس 
Survey of Published LiteratureOnly 14 pages are availabe for public view
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Abstract
Solar power has experienced impressive growth in the past decade , and the Solar Energy Industries Association (SEIA) predicts that the global solar power capacity will reach 980 GW by 2020. The PV field of power generation provide one of the most efficient ways of producing energy, with real perspectives in the future considering the actual situation of the classical power resources around the world. A real problem that we have insufficient supplies of this kind of power resources for the world’s needs became urgent.
A PV solar power system converts the solar irradiance into electrical power directly. In order to optimize the cost of energy, it is ideal to maintain the PV operation at its maximum efficiency all time, and such achievement is complicated by uncertain nonlinear current-voltage (I-V) and power-voltage (P-V) characteristics because of the change in intrinsic and environmental conditions .
The so-called maximum power point tracking(MPPT) is a crucial aspect of control design for PV system operation. Several MPPT techniques have been proposed and most of them use the power measurement of the PV array feedback. An exemplary methods are basically static search, such as the perturbation and observation (P&O) method, and the incremental conductance (IncCond) method. Such methods may be limited when the system is exposed to quick change of the environmental conditions. More dynamic MPPT control methods have been investigated. Artificial intelligence (AI) is a very effective and flexible application as a dynamic control. There are multiple and miscellaneous types of AI techniques.
A simulation study has been carried out to investigate the output power of a stand-alone PV system which supplies both DC and AC power loads. The system is controlled within the Maximum Power Point Tracking (MPPT) method using two different modern techniques. The output power in all cases of study are presented. The study has shown that the artificial intelligent method using Fuzzy Logic Optimization (FLO) is more accurate.