**Genetic Algorithm Based Feeder Reconfiguration and Control of Reactive Power for Loss Reduction**

1Faculty of Engineering-Sulaimani University, Bakrajo Street, sulaimani, Iraq

**Abstarct**

Power distribution network consists of a group of radial feeders which can be connected together by several tie-switches and tie-lines. The power loss reduction in the network is a major concern of electric distribution utilities. Among conventional methods, optimal reconfiguration and capacitor placement are two effective methods which can be applied on the network. In this paper a feeder reconfiguration algorithm is build and present for

the purpose of power loss reduction in distribution systems. The methodology developed combine optimization technique. The network reconfiguration problem is formulated as single objective optimization problem with equality and inequality constraints. The proposed solution to this problem is based on a general combinatorial optimization algorithm known as genetic algorithm, and the load flow equations in distribution

system. The GA optimizes the system-switching pattern for losses reduction. Decides the system topology, and considered taken the size and location of capacitor placement in the radial distribution networks. The two selection method used in genetic algorithm, first method roulette wheel

selection and second method tournament selection. Tests show the different in execution time of the program to arrive the best fitness function.

The proposed algorithm has been implemented in technical MATLAB package and tested for practical network (48-bus) which is taken from the distribution system of Palestine Street in Baghdad city. Tests show that GA is suitable algorithm as optimization technique, high accuracy, and avoid local minimum by searching in several regions to arrive to the global optimum solution. Thus, the outcome of the study shows an efficient technique to solve the problem of network reconfiguration for loss reduction in the distribution power system.

**Keywords**: Genetic Algorithm, Load flows, Capacitor placement, Optimal, reconfiguration, Power, loss reduction

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