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Optimal Capacitor Allocation and Sizing in Distribution Networks Using Particle Swarm Optimization Algorithm | IEEE Conference Publication | IEEE Xplore

Optimal Capacitor Allocation and Sizing in Distribution Networks Using Particle Swarm Optimization Algorithm


Abstract:

This paper presents an algorithm based on Particle Swarm Optimization (PSO) methodology to solve optimal capacitors allocation and sizing problem in distribution networks...Show More

Abstract:

This paper presents an algorithm based on Particle Swarm Optimization (PSO) methodology to solve optimal capacitors allocation and sizing problem in distribution networks. The objective function is to minimize real power losses, operating costs, fixed costs and, consequently, to improve voltage quality. In this paper, two PSO algorithms are used, the first to determine the optimal capacitors allocation and the second one for optimal capacitors sizing. This algorithm has been executed on IEEE 34-bus and IEEE 85-bus radial distribution systems. The loads are considered as constant power loads. Results demonstrate effectiveness of the proposed alternative procedure PSO based algorithm.
Date of Conference: 07-09 November 2018
Date Added to IEEE Xplore: 10 January 2019
ISBN Information:
Conference Location: Brasília, Brazil

I. Introduction

The aim of capacitors in radial distribution systems is to reduce power losses, voltage profile improvement, system capacity release, reactive power compensation, power factor correction and reliability enhancement. To achieve these advantages, it is necessary an optimal capacitors placement, that determines locations and sizes of capacitors to be installed in the system [1]. Optimal capacitors placement is a combinatorial problem where the search space grows exponentially with the size of the system [2]. Mathematically, the capacitor placement is a nonlinear problem, whose solution is non-convex and may present different optimal local solutions [3]. This problem has been investigated since the 60’s, but in the 90’s several approaches have been developed through heuristic and metaheuristic techniques: Simulated Annealing [4], Genetic Algorithms [5], Tabu Search [6], Ant Colony Algorithm [3] and Particle Swarm Optimization (PSO). PSO is a metaheuristic technique that has been highlighted due to its efficiency to solve combinatorial problems [3]. PSO has also been found to be robust in solving problems featuring non-linearity, non-differentiability and high dimensionality [7].

References

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