Abstract

Traditional Current Reference Generators (CRGs) used in low-voltage ride-through (LVRT) control schemes often optimize a single objective, such as minimizing power oscillations, at the expense of other performance metrics such as total harmonic distortion (THD). This paper proposes a many-objective optimized current reference generation strategy (MaO-OCRGS) that yields a set of trade-off solutions, each defined by proposing two decision variables and their associated power quality performance metrics, i.e., power oscillations (?p, ?q), THD, and the unbalance index (UI). A hybrid physics-based and data-driven (HPD) approach is proposed to construct the performance objective functions of a many-objective optimization (MaOO) problem, where the power oscillations are theoretically derived, and an artificial neural network (ANN) is proposed to model the relationships between the decision variables and current-related metrics (THD and UI). A non-dominated sorting genetic algorithm III (NSGA-III) is employed to solve the MaOO problem, yielding a set of optimal decision variables. A subsequent post-optimization selection then identifies feasible solutions that prioritize one performance metric while keeping the others within acceptable limits, thereby avoiding undesirable trade-offs and resulting in three optimized CRGs. Experimental validation demonstrates that the optimized CRGs achieved through the proposed MaO-OCRGS effectively enhance one performance objective without unacceptable degradation in others, as the other metrics are maintained within their predefined limits, thus offering flexibility while enhancing the overall LVRT performance under unbalanced voltage conditions. © 2025 IEEE.