Network Reconfiguration of Primary Distribution System Using GWO Algorithm

Abstract

This manuscript presents a feeder reconfiguration in primary distribution networks with an objective of minimizing the real power loss or maximization of power loss reduction. An optimal switching for the network reconfiguration problem is introduced in this article based on step by step switching and simultaneous switching. This paper proposes a Grey Wolf Optimization (GWO) algorithm to solve the feeder reconfiguration problem through fitness function corresponding to optimum combination of switches in power distribution systems. The objective function is formulated to solve the reconfiguration problem which includes minimization of real power loss. A nature inspired Grey Wolf Optimization Algorithm is utilized to restructure the power distribution system and identify the optimal switches corresponding minimum power loss in the distribution network. The GWO technique has tested on standard IEEE 33-bus and 69-bus systems and the results are presented.

Electric distribution network reconfiguration for power loss reduction based on runner root algorithm

“…at the node 61 after performing reconfiguration. This results is identical to the results obtained by the cuckoo search (CSA) [13], adaptive shuffled frogs leaping algorithm (ASFLA) [25] and BPSOGSA [23] and better than harmony search algorithm (HSA) [26] and GWO [15]. Similar to the 16-node system, the values of the Fitaverge and the STD of BPSOGSA are 171.5 and 168.1 which are much higher compared with those of RRA.…”

“…In [14] modified BBO is successful applied for finding the optimal configuration for power loss reduction. The GWO is also successful applied for the NR problem to reduce power loss [15,16]. In comparison with the heuristic methods which are based on the knowledge of electric power system such as [1,2], the modern methods have more advantages.…”

Loss allocation in distribution networks with distributed generators undergoing network reconfiguration

“…Savier and Das [25] studied the impact of NR on LA in a much better way, where a heuristic branch exchange based algorithm is employed for optimal reconfiguration, and then quadratic loss allocation scheme is used to allocate losses to various consumers in a radial distribution network (RDN) before and after the reconfiguration. Recently, a metaheuristic based firefly algorithm [26], a modified FBS based technique [27] and a Grey Wolf optimization method [28] are also introduced for obtaining an efficient reconfigured power distribution network with improved voltage profile.…”

Determining optimal location and size of capacitors in radial distribution networks using moth swarm algorithm

“…Dissimilar to the previous methods, teaching learning based optimization (TLBO) [13], hybrid method of chaotic search, opposition-based learning, DE and quantum mechanics (HCODEQ) [14], particle swarm optimization approaches (PSOs) [15] and flower pollination algorithm (FPA) [16] have solved such OCLSD problem by considering locations and size of capacitor as control variables of each solution. On the other hand, voltage enhancement can be reached by using wind turbines and photovoltaic systems [17,18], network reconfiguration [19][20][21], and distributed generators [22]. In this paper, MSA is applied to OCLSD problem.…”