الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Renewable energy is essential for supplying the power needs of today’s
generation. In reality, renewable source of energy is the only one that allows
for power decentralization. The photovoltaic (PV) is one of a renewable
energy. The output of a single PV Module is affected by environmental factors
such as levels of irradiation and temperature. The main challenge of the PV
system is tracking the maximum power point (MPP). Many techniques are
used for maximum power point tracking (MPPT). Some of them are
traditional and others are intelligent. The best results are found to be based on
artificial intelligent (AI) techniques.
In this thesis, a novel MPPT strategy that depends on two stages is
presented. The first stage is to estimate values of temperature (T) and
irradiance (G) using two different proposed artificial intelligent (AI)
techniques which are artificial neural network (ANN) and fuzzy logic control
(FLC). Voltage and current sensors are used instead of temperature sensor and
pyranometer. This estimation achieves low cost and high accuracy. The
second stage is to control the duty cycle (D) to track maximum power point
using proposed ANN under variation of T, G, and load. The first stage for
estimation T and G is investigated using seven case studies. Four of them are
MATLAB simulation cases of proposed ANN and FLC. The other three cases
are real case studied in Hurgada and Elsalam city. The second stage for
tacking MPP is investigated using four case studies. Three of them are
MATLAB simulation cases of proposed ANN. The fourth case is real studied
in Hurgada. The results of tracking MPP are compared with three modified
traditional methods. The output power of proposed method is higher, lower
oscillation, higher efficiency and faster response. The proposed method is validated by experimental works. The experimental works are achieved in two
different sites. The first experiment is in LAB, where the second is in external
site (EAEAT academy). The experimental results are compared with
simulation results. The results of experimental works verify the simulation
results of the proposed methods with less error.