Pareto Multi-Objective Reconfiguration of IEEE 123-Bus Unbalanced Power Distribution Networks Using Metaheuristic Algorithms: A Comprehensive Analysis of Power Quality Improvement

This study addresses the critical challenge of reconfiguration in unbalanced power distribution networks (UPDNs), focusing on the complex 123-Bus test system. Three scenarios are investigated: (1) simultaneous power loss reduction and voltage profile improvement, (2) minimization of voltage and current unbalance indices under various operational cases, and (3) multi-objective optimization using Pareto front analysis to concurrently optimize voltage unbalance index, active power loss, and current unbalance index. Unlike previous research that oftensimplified system components, this work maintains all equipment, including capacitor banks, transformers, and voltage regulators, to ensure realistic results. The study evaluates twelve metaheuristic algorithms to solve the reconfiguration problem (RecPrb) in UPDNs. A comprehensive statistical analysis is conducted to identify the most efficient algorithm for solving the RecPrb in the 123-Bus UPDN, employing multiple performance metrics and comparative techniques. The Artificial Hummingbird Algorithm emerges as the top-performing algorithm and is subsequently applied to address a multi-objective optimization challenge in the 123-Bus UPDN. This research contributes valuable insights for network operators and researchers in selecting suitable algorithms for specific reconfiguration scenarios, advancing the field of UPDN optimization and management.