الفهرس 
CHAPTER 1. IntroductionOnly 14 pages are availabe for public view
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Abstract
In order to maximize the benefit of solar panels, it is crucial to enhance the operating efficiency of PV systems. Usually, the PV arrays are exposed to harsh outdoor operating conditions that affect their operating efficiency. Extraction of global maximum power point (GMPP) among the other local MPPs (LMPPs) from the distorted characteristics of the PV array is considered the main challenge of PV system controllers under partial shading conditions (PSCs). Classical MPP tracking (MPPT) techniques have incompetent performance in tracing the GMPP under the PSCs of PV arrays. Therefore, many metaheuristics-based MPPT techniques are developed by researchers to defeat the problems related to the PSCs and cope with GMPP and other LMPPs.
Among all metaheuristic algorithms, Particle Swarm Optimization (PSO) and its modified versions are the most common optimization methods owing to their easy implementation, simplicity, and low computational burden. On the other hand, the standard PSO (SPSO) algorithm suffers from low convergence speed, and difficulty in tuning its parameters, and it may be trapped in LMPP under fast changes in solar radiation and/or PSCs. In order to tackle these issues, a novel metaheuristic MPPT technique based on an Enhanced Autonomous group PSO (EAGPSO) algorithm is proposed. A comparative study between EAGPSO and other versions of PSO techniques is performed to emphasize the efficacy of the EAGPSO technique. The simulation results demonstrate that the proposed EAGPSO algorithm is superior to the counterpart versions of the PSO algorithms in terms of fast-tracking time, and high energy efficiency. Experimental validation of the EAGPSO technique has been conducted using an array of 3*3 series-parallel PV panels under PSC. The EAGPSO algorithm results have been compared with SPSO the proposed EAGPSO technique has the highest MPPT efficiency of 98.6% and the lowest power oscillations of about 2.03% of the tracked power with a tracking time of 2.6 s.