Turkish Journal of Electrical Engineering and Computer Sciences




In recent years, the problem of optimum reconfiguration in distribution systems (DSs) has been a task that must be solved in an optimal manner. This paper presents a new approach for the optimal reconfiguration of DSs based on a hierarchical 2-stage optimization problem to improve the power system voltage stability margin and reduce losses incorporating the constraints. The mentioned problem has been modeled as a nonlinear and multiobjective optimization problem. It uses the ability of the developed harmony search algorithm (HSA) as the first stage of the proposed optimization problem to reach the best network configuration. This reconfiguration algorithm starts with a radial topology by a theoretical approach that is based on the graph concept and matroid theory. These concepts are used in order to propose new intelligent HSAs to form a new harmony vector that is well dedicated to the DS reconfiguration problem. Thus, all of the resulting individuals after forming a new harmony vector are claimed to be feasible configurations. Moreover, the presented approach is valid and avoids tedious mesh checks for the topology constraint validation. In the second stage of the proposed approach, the voltage stability index is calculated to evaluate the static voltage stability security margin for each reconfiguration pattern. Hence, a toolbox has been developed to recognize the loadability limit of DSs based on the Lagrangian optimization method. Finally, the proposed method establishes a tradeoff between the security index and power losses to reach a coordinated reconfiguration pattern. To demonstrate the validity of the proposed method, the simulations are carried out on 33- and 69-bus IEEE DSs. The proposed method is finally compared to some previous techniques used by other authors. The results show a good enhancement in the security margin and smaller power losses with considerably less computation effort. To validate the proposed method, the results that were obtained from the HSA are compared with the particle swarm optimization algorithm to ascertain its effectiveness.

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