4271

4042

كارشناسي ارشد

قدرت

اصفهان : دانشگاه صنعتي اصفهان، دانشكده برق و كامپيوتر

1387

ده، 96، [II] ص. : جدول، نمودار

ص. ع. به: فارسي و انگليسي

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23/7/1387

مهندسي برق و كامپيوتر

ID4042

به فارسي و انگليسي: قابل رويت در نسخه ديجيتال

AbstractReconfiguration is one of the main operations in electric distribution systems forminimizing transmission losses load balancing increasing the reliability of distributionsystem etc These objectives would be considered individually or simultaneously Thereare different methods for system reconfiguration such as to close one of the normally openswitches and then open one of normally closed switches close all normally closed switchesand open some of them properly dividing system into several subsystems andreconfiguring them separately Some new methods based on artificial intelligency such asgenetic algorithm fuzzy logic neural networks simulated annealing and ant colonyalgorithm are also reported in reconfiguration of distribution systems In this report a newmethod based on ant colony algorithm using genetic operators is presented and itsperformance is investigated for reconfiguration of distribution systems The methods based on ant colony algorithm were inspired by the observation of real ant colonies Antshave social insects and one of the important and interesting behaviors of ant colonies istheir foraging behavior and in particular ability of finding shorter paths between foodsources and their nest naturally While moving from food source to the nest ants remain onthe ground a substance called pheromone that can be used by other ants to lead them toshorter paths between food source and nest Using foraging behavior of real ants we canuse artificial ants for searching optimum solution in optimization problems This idea isused in this thesis for reconfiguration of distribution systems in order to minimize loss andimprove load balancing In the proposed method by some modifications we may removesome of the weaknesses of classic ant colony algorithms such as premature convergenceand oscillation in the solution space This algorithm is specified by a complementarysearch stage using genetic algorithm operators that maintain a higher probability offinding the best solution on a collection of favorable solutions found by the ant colonyalgorithm Results obtained by applying the proposed method to typical power distributionsystems show its efficiency and capability compared to some other methods

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