Network Reconfiguration for an Electric Distribution System with Distributed Generators based on Symbiotic Organisms Search

Abstract

This paper proposes a method of network reconfiguration based on symbiotic organisms search (SOS) algorithm for reducing power loss of the electric distribution system. The SOS is a recent developed meta-heuristic algorithm inspired from the symbiotic interaction strategies of organisms for surviving and propagating in the ecosystem. Compared to other algorithms, SOS does not need any control parameters during the searching process. The advantages of the proposed SOS method have been validated in two electric distribution systems. Three network cases have been considered for each system, consisting of performing network reconfiguration on the system without distributed generator (DG) placement, the system installed type-P DGs and the system installed type-PQ DGs. The comparison results with particle swarm optimization and other previous methods show that the proposed SOS can be a promising technique for the problem of electric network reconfiguration.

Finding Radial Network Configuration of Distribution System Based on Modified Symbiotic Organisms Search

“…In addition, the SOS and its improved versions have been effectively applied for many technical problems, such as engineering structure problems [24,25] and the resource leveling problem in the construction field [26], and vehicle routing and traveling salesman problems in the transport field [27,28]. In the field of power system, the problems consisting of distributed generation placement [29][30][31][32][33], capacitor placement [34], dispatch problem [35,36], and NR problem [37] have been successful solved by SOS. In addition, there are a lot of problems related to energy optimization and engineering design that have been successful carried out by the SOS [38].…”

Optimal electric distribution network configuration using adaptive sunflower optimization

“…The common feature of these methods is that they yield more positive results than heuristic methods. Typical of the above methods must be mentioned to genetic algorithm (GA) [5], [6], particle swarm optimization (PSO) [7]-[9], grey wolf optimization [10], [11], backtracking search algorithm [12], tabu search algorithm (TS) [13], runner root (RRA) [14], symbiotic organisms search (SOS) [15], adaptive shuffled frogs leaping algorithm (ASFLA) [16], improved shuffled frogs leaping algorithm (ISFLA) [17], improved elitist-jaya algorithm (IEJAYA) [18], improved cuckoo search algorithm (ICSA) [19], binary particle swarm gravity search algorithm (BPSO-GSA) [20], and biogeography based optimization (BBO) [21].…”

Optimal Location and Size of Distributed Generators in an Electric Distribution System based on a Novel Metaheuristic Algorithm

“…The disadvantages of these methods are the slow convergence to optimal results, or the fall into local extremes. Metaheuristic methods have been applied for solving this problem, such as Genetic Algorithm (GA) [10,11], Particle Swarm Optimization (PSO) [11][12][13], symbiotic organisms search [14], Artificial Bee Colony algorithm (ABC) [15], invasive weed optimization [16], Cuckoo Search (CSA) [17], Fireworks Algorithm (FWA) [18], Stochastic Fractal Search (SFS) [19], Harmony Search Algorithm (HSA) [20], and Salp Swarm Algorithm (SSA) [21]. These methods have the advantage of handling conveniently problem's constrains.…”