الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
Meta-heuristic optimization techniques are now used to solve many electrical
engineering optimization problems. The importance of these techniques comes from their ability to solve several of optimization problems that can’t be solved using deterministic techniques due to the difficulty of defining the problem, constraints with all their continuity and differential properties.
In this thesis, a global maximum power point tracking (GMMPT) technique
based on invasive weed optimization algorithm for Photovoltaic (PV) array under partial shading conditions is proposed. The power-voltage curve of the PV array has several local maximum power points (LMPPs) and only one global maximum power point (GMPP).
An overall statistical appraisal of the proposed technique compared with
different meta-heuristic techniques such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Harmony Search Algorithm (HSA), Bat Algorithm (BA), Sine Cosine Algorithm (SCA), Wind Driven Optimization (WDO), Cuckoo Search (CS) and Genetic Algorithm (GA) is executed under different scenarios of
shading conditions to estimate the superiority of the proposed technique over all other techniques.
The statistical metrics that used for the comparison are such as geometric
mean, the root mean square error, mean absolute error, standard deviation, arithmetic mean, significance, and efficiency. The proposed algorithm Invasive Weed Optimization (IWO) is considered to be the most efficient and outstanding optimization technique compared to corresponding ones.
Due to the superiority of IWO with respect to the other techniques, another
study is proposed to improve the convergence and efficiency of IWO. The proposed Improved IWO (IIWO) algorithm modifies termination condition of the weed population to be faster. The effect of changing input parameters is determined to be more efficient. An overall statistical evaluation of IIWO, with standard IWO and Particle Swarm Optimization (PSO) is executed under different shading conditions. The simulation results show that IIWO has faster and better convergence as it can reach the GMPP in less iterations compared with other techniques.