Print ISSN: 1681-6900

Online ISSN: 2412-0758

Keywords : Network reconfiguration


Analysis of Distribution System Reconfiguration under Different Load Demand in AL-KUT City by using PSO Algorithm

Zahraa H. Dawood; Rashid AL-Rubayi

Engineering and Technology Journal, 2021, Volume 39, Issue 5A, Pages 738-753
DOI: 10.30684/etj.v39i5A.1842

Network reconfiguration is the best way to inquisitive a flexible, reliable and effective distribution network. An efficient optimization technique that uses Particle Swarm Optimization (PSO) is described and analyzed with the goal of reducing power losses and enhancing the voltage profile in the distribution network by reconfiguring the network, taking into account the branch current limit, branch capacity limit, bus voltage limits and radial structure constraint (no meshed loop). The approach is applied to the part of AL-KUT city distribution system (TAMOZE region system) to attain an optimum network configuration in connection with power loss. Two dissimilar load situations are regarded, and the performance of the suggested approach is also proved by increasing the decrease in power loss by using MATLAB under steady-state conditions.

Optimum Simultaneous Distributed Generation Units Insertion and Distribution Network Reconfiguration Using Salp Swarm Algorithm

Ahmed H .Mashal; Rashid AL-Rubayi; Mohammed Kdair Abd

Engineering and Technology Journal, 2020, Volume 38, Issue 11, Pages 1730-1743
DOI: 10.30684/etj.v38i11A.1792

Contemporary researches offer that most researchers have concentrated on either network reconfiguration or Distributed Generation (DG) units insertion for boosting the performance of the distribution system (DS). However, very few researchers have been studied optimum simultaneous distributed generation units insertion and distribution networks reconfiguration (OSDGIR). In this paper, the stochastic meta-heuristic technique belong to swarm intelligence algorithms is proposed. Salp Swarm Algorithm (SSA) is inspired by the behavior of salps when navigating and foraging in the depth of the ocean. It utilized in solving OSDGIR. The objective function is to reduce power loss and voltage deviation in the Distribution System. The SSA is carried out on two different systems: IEEE 33-bus and local Iraqi radial (AL-Fuhood distribution network). Three cases are implemented; only reconfiguration, only DG units insertion, and OSDGIR. Promising results were obtained, where that power loss reduced by 93.1% and recovery voltage index enhanced by 5.4% for the test system and by 78.77% reduction in power loss and 8.2% improvement in recovery voltage for AL-Fuhood distribution network after applying OSDGIR using SSA. Finally, SSA proved effectiveness after an increase in test system loads by different levels in terms of reduced power loss and voltage deviation comparison with other methods.

Al - Kalij Sub-Station: Feeder Reconfiguration by Particle Swarm Optimization

Qais M. Alias; Rana Ali Abttan

Engineering and Technology Journal, 2011, Volume 29, Issue 12, Pages 2375-2385

This paper presents the solution approach for the optimal reconfiguration problem
in distribution networks implementing Particle Swarm Optimization (PSO)
technique.
Network reconfiguration in distribution system is changing the status of
sectionalizing switches to reduce the power loss in the system. The main objective of
network reconfiguration is to find the network topology which is having the
minimum losses during any conditions exists in the network. A network
configuration is a valid solution to the problem if it satisfies reliability, security and
other operation constraints.
Particle Swarm Optimization is a robust stochastic evolutionary computation
technique, which is based on the movement and intelligence of swarms.
A standard particle swarm optimization algorithm is adapted and used in this work.
The primary case study is a part of the Baghdad area distribution network. It consists
of four feeders and 102 buses. The algorithm validity is verified first via application
to standard systems. Results show that the standard particle swarm optimization is
suitable for off-line reconfiguration studies.