الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
The power demand in the world is increasing rapidly and new forms of energy sources must be found in order to cover the future power demands, however, the conventional power generation sources are about to be emptied. Renewable energies can theoretically, cover the global power demand. One of the forms of renewable energies that have recently experienced rapid growth around the world is the solar Photovoltaic (PV) energy, which converts sunlight intensity to electrical Direct Current (DC), without using any mechanical part. The electrical infrastructure around the world is based on Alternating Current (AC). The solar PV modules cannot be directly connected to the utility grid. Therefore, the power processing stage is used. There are two types of power processing stage that can be used to connect the solar PV modules with the utility grid: single power processing stage and dual power processing stage. The dual power processing stage is the traditional type, which consists of a DC-DC converter direct coupled with the PV modules and the grid-connected inverter. The proposed PV system utilizes two power processing stages consist of a DC-DC Boost Converter, followed by three-phase, two-level, Voltage Source Inverter (VSI). The DC-DC Boost Converter is used to extract the maximum PV array power and increasing the PV array voltage. The Fuzzy Logic Control (FLC) based on the Maximum Power Point Tracking (MPPT) technique is applied to fine-tune the duty cycle for the DC-DC boost converter to realize the maximum PV array power at different climate dataset and different PV faults scenarios. The VSI is used to inject a sinusoidal current into the utility grid through the LCL filter. The control strategy of the VSI consists of two control loops: the DC-Link voltage control loop and the AC-current control loop. Abnormal conditions in the solar Photovoltaic (PV) array whether permanent or temporary faults leads to lessening the overall solar PV system efficiency and might lead to a fire hazard. As a result, it is compulsory to detect and diagnose faults in the PV array in order to ameliorate system reliability, safety, and efficiency. This research proposes an automatic Fault Detection and Diagnosis (FDD) for the PV array under a grid-connected PV system operation based on the Supervised Machine Learning (ML) algorithm, which is the Support Vector Machine (SVM) algorithm in order to detect and diagnosis the PV array faults under different climate dataset conditions and different solar PV array faults scenarios. Modeling, controller design, a simulation study of a grid-connected PV system, the overall configuration of the grid-connected PV system, and the proposed Fault Detection and Diagnosis algorithm for the PV array is performed using MATLAB/Simulink® to investigate system performance. Practical implementation is carried out to validate the simulation results for the proposed PV array Fault Detection and Diagnosis algorithm.