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.
Highlights
Power loss in electric distribution systems (EDSs) takes a high portion in the total power loss of the electricity system
More and more new metaheuristic algorithms have being applied to this problem such as genetic algorithm [8], particle swarm optimization (PSO) algorithm [9,10,11], gravitational search algorithm (GSA) [12], fireworks algorithm (FWA) [13], runner root algorithm (RRA) [14, 15], and cuckoo search algorithm (CSA) [16, 17]
The power loss of the optimal configuration obtained by symbiotic organisms search (SOS) was 6.3120 lower than that of the method based on grey wolf optimizer (GWO) [30]
Summary
Power loss in electric distribution systems (EDSs) takes a high portion in the total power loss of the electricity system. In the branch exchange method, initially all switches are closed and an open switch is replaced by a closed switch for reducing power loss [7] The advantages of this method group are that they are easy to implement and the knowledge related to these methods is very close to the area of operating EDS. The main advantage of this method group is that they are very efficient for handling constraints and different objective functions Compared to the former group, the later has the ability to provide a sufficiently good solution to the problem of network reconfiguration. It is worth considering the application of recent developed metaheuristic algorithms which have less control parameters, for solving the network reconfiguration problem
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