Optimal Reconfiguration of Power Distribution Network Using Hybrid Firefly and Particle Swarm Optimization Algorithm

  • Ayesha Azhar Electrical Engineering Department, University of Engineering and Technology, Taxila, Pakistan
  • Aftab Ahmad Electrical Engineering Department, University of Engineering and Technology, Taxila, Pakistan
Keywords: Hybrid firefly and particle swarm optimization, Network reconfiguration, Power distribution network, Voltage profile.

Abstract

Electrical energy has become the most essential requirement for working of today’s modern world. Power distribution networks (PDN) are required for providing power from substations to consumers but are subjected to power loss and voltage drop problems. These problems greatly affect the operational cost and voltage stability level of a PDN. Network reconfiguration (NR) is a cost effective approach to optimize PDN for reduction of power loss and improvement of voltage profile (VP). This paper presents an effective meta-heuristic, population-based algorithm for finding optimal configuration of a PDN. In particular Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm is used. The HFPSO algorithm has enhanced exploration and exploitation strategies, and fast convergence rate. MATLAB software is used to implement the algorithm and IEEE 33-bus radial distribution system (RDS) is considered for NR. The results obtained show that active power loss is reduced by 46.35% from original value and minimum voltage is improved to 0.9572p.u. The comparison of obtained results with literature show that HFPSO algorithm has efficiently reduced active power loss and improved VP of the network.

Author Biography

Aftab Ahmad, Electrical Engineering Department, University of Engineering and Technology, Taxila, Pakistan

Dean, Electrical Engineering Department, UET Taxila

Published
2021-12-20
How to Cite
[1]
A. Azhar and A. Ahmad, “Optimal Reconfiguration of Power Distribution Network Using Hybrid Firefly and Particle Swarm Optimization Algorithm”, PakJET, vol. 4, no. 4, pp. 23-28, Dec. 2021.