Reconfiguration in Distribution Systems Based on Refined Genetic Algorithm for Improving Voltage Quality

Optimal Reconfiguration of Distribution Networks Using Hybrid Heuristic-Genetic Algorithm

“…From every pair of parents two new offspring are created, which need to keep the part of the parent's genetic material, preferably the best one. In most of the approaches that employ evolution algorithms for the network reconfiguration problem, this is usually the point at which the problems appear [5,8,10,11,16,17,19,20,22]. These problems are related to the methods used in the crossover process, which result in offspring that violate radiality constraint or have isolated parts of the network.…”

“…The distribution network optimal reconfiguration represents an optimization problem that identifies the optimal radial topology of the distribution network from the set of feasible radial network topologies [5]. In normal operating conditions, distribution network reconfiguration is usually conducted with a goal to minimize the total network power/energy losses [1][2][3][4], harmonize network voltage profile [5][6][7][8] and unify network loading [3], or to achieve a combination of the mentioned criteria through a multi-objective framework [9][10][11]. In achieving these goals, it is necessary to assure the radial topology of distribution networks, as well as to satisfy different physical and operational constraints.…”

“…So far, different sets of approaches have been developed for solving the optimal distribution network problem under different sets of objective functions. These approaches can be clustered into four major categories depending on the solution methods used: heuristic approaches [1][2][3], metaheuristic approaches [4][5][6][7][8][9][10][11], mathematical programming [13,14], fuzzy logic. Heuristic approaches use a different set of heuristic rules [1,2] or approximate analytical expressions [3], which can be applied on segments of distribution network to partially improve network conditions and objective function.…”

“…Different sets of metaheuristics approaches have been applied so far to solve the optimal distribution network problem, ranging from genetic algorithms, particle swarm optimization, simulate annealing, ant-colony optimization, bacterial foraging, bee swarm optimization, etc. Most of the approaches, regardless of the method applied and objective function, demonstrate challenges in maintaining network radial topology in the process of the initialization or modification of candidate solutions [5,8,10,11]. Most of the approaches use simple generic methods for candidate solution modification, not respecting the topological nature of the problem.…”

“…Most of the approaches use simple generic methods for candidate solution modification, not respecting the topological nature of the problem. In the case of genetic algorithms, the candidate solution modification in the form of crossover process, uses, for example, a simple single [5,8,10] or multipoint [11] crossover method, which in a large number of cases results in an infeasible candidate solution. These infeasible solutions usually include isolated parts of a distribution network or parts of network connected to a main supply point containing cycles (meshed network).…”