Hybrid Optimization of Unified Power Quality Conditioner Placement for Enhanced Grid Performance

International Journal of Electrical and Electronics Engineering

© 2025 by SSRG - IJEEE Journal

Volume 12 Issue 3

Year of Publication : 2025

Authors : G. Lakshminarayana, D. Rene Dev, K. Vijetha, Neha Verma Gour, R. Mageswaran, S. Sree Dharinya

10.14445/23488379/IJEEE-V12I3P108

How to Cite?

G. Lakshminarayana, D. Rene Dev, K. Vijetha, Neha Verma Gour, R. Mageswaran, S. Sree Dharinya, "Hybrid Optimization of Unified Power Quality Conditioner Placement for Enhanced Grid Performance," SSRG International Journal of Electrical and Electronics Engineering, vol. 12,  no. 3, pp. 74-83, 2025. Crossref, https://doi.org/10.14445/23488379/IJEEE-V12I3P108

Abstract:

In response, this research paper introduces an innovative approach for enhancing power quality and stability in electrical grids by optimally placing Unified Power Quality Conditioner (UPQC) devices. The proposed methodology leverages the synergies of a Hybrid Genetic Algorithm based Chaotic Dragonfly Optimization (HGACDO) to determine the most effective locations for UPQC installation. The Genetic Algorithm (GA) brings powerful exploration capabilities, simulating natural selection to refine potential solutions. This is combined with the Chaotic Dragonfly Optimization’s (CDO) efficient global search mechanism inspired by the brood parasitism behaviour of cuckoos. This synergy results in the HGACDO method, which is uniquely tailored for optimal UPQC allocation. The proposed approach not only considers the technical aspects of UPQC placement but also considers the cost-effectiveness, power losses and voltage stability index. By fusing GA and CDO, the HGACDO algorithm enables a comprehensive exploration of solution spaces, enhancing the precision of UPQC placement. To validate the effectiveness of the HGACDO model, extensive testing is conducted on benchmark IEEE 69 and IEEE 33 test bus systems. The results demonstrate significant improvements in power quality, stability and operational efficiency. Through its innovative hybridization, the HGACDO method emerges as a promising avenue for achieving optimal UPQC allocation, thereby advancing the reliability and performance of modern power systems.

Keywords:

Hybrid Genetic Algorithm based Chaotic Dragonfly Optimization (HGACDO), IEEE 69, 1EEE 33 test bus, Optimal placement, UPQC system.

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