Modified Swarm-Based Artificial Intelligence Optimization for Optimal Coordination of Directional Overcurrent Relays in Power System

Abstract

The coordination of directional overcurrent relays (DOCRs) plays a critical role in ensuring the reliability and robustness of modern electrical power protection systems. Achieving optimal relay coordination in multi-loop power networks is a complex optimization challenge requiring the minimization of relay operating times and achieve optimal tuning of time dial settings (TDS) and plug settings (PS) while considering the impact of DG integration. The proposed method employs a Quantum-Inspired Adaptive Walrus Optimization Algorithm (QIAWOA), a modified swarm-based artificial intelligence technique (AI) that incorporate rates quantum-inspired principles, such as adaptive quantum rotation gates, to enhance search dynamics and facilitate precise relay coordination. The performance of QIAWOA is validated using the IEEE 3, 8, and 15-bus systems, as well as the CEC 2020 benchmark suite, which includes multimodal and multi-objective optimization functions (MMOOF). QIAWOA demonstrates superior capabilities in identifying globally optimal solutions, significantly reducing relay operating times, and achieving robust coordination. Comprehensive statistical analyses, including empirical cumulative distribution functions (CDF), boxplots, histograms, probability plots, and quantile-quantile (QQ) plots, underscore the reliability and efficiency of the proposed method. Comparative evaluations with state-of-the-art nature-inspired techniques further highlight the enhanced performance of QIAWOA, establishing it as a powerful tool for improving the protection system performance in complex power networks.