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A Comparative Study for the Optimization of Active Power of an Electric Power Network

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In our present article, we present a comparative study between the Hessians methods such as direct search methods (Nelder-Mead Simplex) and indirect research methods that are known as Quasi-Newtonian (BFGS, DFP, Steepest-Descent) and genetic algorithms for optimization of the active power by the minimization of cost and taking into account the constraints of equalities and inequalities. The methods were tested on an electrical network of 57 nodes and the result that we obtained shows the advantage of AG compared to Hessian methods.
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Optimization; Electrical Network; Genetic Algorithms; Hessians Methods; Cost Function

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