Fuzzy Satisfied Multiobjective Distribution Network Reconfiguration: an Application of Adaptive Weighted Improved Discrete Particle Swarm Optimization

Subramaniyan, Manikandan; Sasidharan, S.; Viswanatharao, J.; Veerasamy, Moorthy

doi:10.15866/iremos.v10i4.12267

Cited by 2 publications

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“…The reductions in the loss of power values for the CSA [7], IAICA [14], MBFOA [15], SFSA [19], CTLHSO [20], PSO [21], HIS [22], EO [24], and GWO methods are 63.20 kW, 63. [33,34]. Furthermore, it can be seen that the GWO algorithm considerably reduces the power loss compared to the other optimization algorithms in both test systems.…”

Section: Comparison Of the Best Possible Solutionmentioning

confidence: 87%

Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization

et al. 2023

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This paper describes a computational procedure to establish the optimal distribution of network reconfiguration by means of a novel gray wolf optimization (GWO) algorithm. The procedure aimed to diminish the system’s power loss and produce a better voltage profile while fulfilling the operating constraints described by different operating conditions. Under practical restrictions, the distribution network reconfiguration (DNR) problem is classified as multimodal and highly nonlinear. Constraint breaches were appropriately handled to produce stable convergence characteristics, and high-quality solutions were obtained in a shorter execution time. The 33-bus and 69-bus systems were used to obtain the optimal reconfiguration by incorporating the method developed in this work. The simulation results obtained were collated and compared with the outcomes of other well-known optimization techniques, confirming the efficacy of the GWO algorithm in solving the DNR problem.

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Section: Comparison Of the Best Possible Solutionmentioning

confidence: 87%

Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization

et al. 2023

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Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

Subramaniyan

Subramaniyan²,

Veeraswamy³

et al. 2019

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Purpose This paper aims to address not only technical and economic challenges in electrical distribution system but also environmental impact and the depletion of conventional energy resources due to rapidly growing economic development, results rising energy consumption. Design/methodology/approach Generally, the network reconfiguration (NR) problem is designed for minimizing power loss. Particularly, it is devised for maximizing power loss reduction by simultaneous NR and distributed generation (DG) placement. A loss sensitivity factor procedure is incorporated in the problem formulation that has identified sensitivity nodes for DG optimally. An adaptive weighted improved discrete particle swarm optimization (AWIDPSO) is proposed for ascertaining a feasible solution. Findings In AWIDPSO, the adaptively varying inertia weight increases the possible solution in the global search space and it has obtained the optimum solution within lesser iteration. Moreover, it has provided a solution for integrating more amount of DG optimally in the existing distribution network (DN). Practical implications The AWIDPSO seems to be a promising optimization tool for optimal DG placement in the existing DN, DG placement after NR and simultaneous NR and DG sizing and placement. Thus, a strategic balance is derived among economic development, energy consumption, environmental impact and depletion of conventional energy resources. Originality/value In this study, a standard 33-bus distribution system has been analyzed for optimal NR in the presence of DG using the developed framework. The power loss in the DN has reduced considerably by indulging a new and innovative approaches and technologies.

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Fuzzy Satisfied Multiobjective Distribution Network Reconfiguration: an Application of Adaptive Weighted Improved Discrete Particle Swarm Optimization

Cited by 2 publications

References 0 publications

Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization

Investigation and Minimization of Power Loss in Radial Distribution Network Using Gray Wolf Optimization

Optimal reconfiguration/distributed generation integration in distribution system using adaptive weighted improved discrete particle swarm optimization

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