The Optimization of Distribution Network ReconfigurationBased on Hybrid Genetic Algorithm
Hong Zhan
Abstract
Hong Zhan
Abstract
An optimization model of distribution network reconfiguration is established, in which the minimum network loss is taken as objective function, the restrictions to the voltage, current and the capacity of power source are taken as constraint conditions. Through the outer penalty function method, the problem is transformed to nonrestraint optimization problem. Aiming at the limitation of genetic algorithm, the fitness function is adjusted, the optimized reserved strategy is used, the cross and mutation method are improved, and simulated annealing algorithm is combined with, then the hybrid genetic algorithm is formed. It can improve the speed of convergence and avoid premature convergence. Based on the features of distribution network, the hybrid genetic algorithm is used to solve the problem, so the computational efficiency is improved. Reconfiguration results show that the algorithm is efficient and practical.
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An optimization model of distribution network reconfiguration is established, in which the minimum network loss is taken as objective function, the restrictions to the voltage, current and the capacity of power source are taken as constraint conditions. Through the outer penalty function method, the problem is transformed to nonrestraint optimization problem. Aiming at the limitation of genetic algorithm, the fitness function is adjusted, the optimized reserved strategy is used, the cross and mutation method are improved, and simulated annealing algorithm is combined with, then the hybrid genetic algorithm is formed. It can improve the speed of convergence and avoid premature convergence. Based on the features of distribution network, the hybrid genetic algorithm is used to solve the problem, so the computational efficiency is improved. Reconfiguration results show that the algorithm is efficient and practical.
Key concepts: Mathematical optimization, Genetic algorithm, Simulated annealing, Penalty method, Computer science, Meta-optimization, Control reconfiguration, Fitness function