Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Applied Power Engineering (IJAPE)
Gastón Orlando Suvire
Universidad Nacional de San Juan (UNSJ)
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Multi-objective optimization and multi-criteria decision analysis of passive power filters for power quality improvement in arc furnace applications Alvaro Yassif Marca Yucra; Gastón Orlando Suvire; John Armando Morales
International Journal of Applied Power Engineering (IJAPE) Vol 15, No 3: September 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v15.i3.pp1200-1211

Abstract

This article presents a multi-objective optimization methodology for the optimal tuning of passive power filters in steelmaking facilities that operate with electric arc furnaces (EAFs). These industrial loads are well-known for introducing severe harmonic distortion, voltage unbalance, and flicker into the electrical network, significantly degrading power quality and equipment performance. To address these challenges, a multi-objective optimization problem is solved using the non-dominated sorting genetic algorithm II (NSGA-II), which simultaneously minimizes three key power quality indices: total harmonic distortion (THD), total demand distortion (TDD), and voltage unbalance factor (VUF). In addition, a multi-criteria decision analysis (MCDA) technique is applied to rank and select the most balanced and robust solution in different EAF operating scenarios. Unlike conventional filter design methods that prioritize a single performance criterion or rely on static harmonic assumptions, the proposed approach accounts for the nonlinear and time-varying behavior of EAFs, ensuring robust performance under diverse operating conditions. A comprehensive case study based on a Bolivian steel plant illustrates the effectiveness of the optimization strategy. Results indicate reductions of 47.6% in THD, 33.5% in TDD, and 63.6% in VUF, clearly outperforming conventional design approaches and significantly improving overall power quality. This work highlights the potential of evolutionary multi-objective algorithms for enhancing passive filter performance in complex industrial environments with highly distorted and unbalanced power conditions.
Co-Authors Alvaro Yassif Marca Yucra John Armando Morales