AI-Based Optimization of D-STATCOM Placement for Power Quality Improvement in Isolated Power Networks

Abstract

This research proposes an intelligent optimization approach for determining the optimal placement and capacity of a Distribution Static Compensator (DSTATCOM) in a 13-bus distribution network representing the electrical systems of Buton and Muna islands. Three metaheuristic algorithms, AI Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and a Hybrid PSO-GA (HPSOGA), are implemented to minimize active power losses and improve voltage profiles. Simulations were performed using MATLAB, and results indicate that all three methods successfully identified bus 13 as the optimal placement location for a 100 kVAr D-STATCOM. Among them, the HPSOGA method outperformed the individual algorithms, yielding the lowest power losses (25.7097 kW) and the fastest computation time (1.0733s), while maintaining system voltage within acceptable limits (0.95}-1.05 p.u.). The findings demonstrate that the hybrid approach offers superior performance in terms of solution quality, convergence speed, and stability, making it a promising strategy for real-time optimization in isolated and renewable-integrated power systems.

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