An Islanding Detection and Load Curtailment Strategy for Radial Distribution Networks Using Squid Game Optimizer Algorithm

The reliable operation of islanded radial distribution networks with Distributed Generation (DG) presents significant challenges, particularly in maintaining voltage and frequency stability. Traditional demand curtailment strategies often result in excessive or insufficient load reduction, leading to suboptimal system performance. This paper introduces an advanced demand curtailment technique based on the Squid Game Optimizer Algorithm (SGOA) to address these inefficiencies. The proposed method optimizes load curtailment by incorporating a constrained function that evaluates the voltage stability margin (VSM) index and the total remaining load after curtailment. The goal is to achieve a stable and balanced operation of the islanded system. To validate the effectiveness of this strategy, four islanding scenarios were modeled using the IEEE 33-bus radial distribution network in MATLAB. The performance of the SGOA was benchmarked against other optimization techniques such as Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). Simulation results demonstrated that the SGOA outperformed these methods, delivering higher remaining loads and improved VSM values across all test cases. This suggests that the SGOA provides a more efficient and reliable approach to demand curtailment, contributing to enhanced voltage and frequency stability in islanded distribution networks.