Multi-objective optimization and multi-criteria decision analysis of passive power filters for power quality improvement in arc furnace applications

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.