Hybrid GA–PSO framework for optimal passive filter placement and sizing in distribution networks
This article presents a hybrid optimization framework that combines particle swarm optimization (PSO) and the genetic algorithm (GA) for determining the optimal placement and sizing of passive harmonic filters in distribution systems. The objective is to reduce voltage distortion and active power losses while satisfying the harmonic limits of IEEE 519 and the capacitor requirements of IEEE Std. 18. In the proposed approach, PSO is first used to rapidly identify candidate solutions that significantly reduce total harmonic distortion (THD), after which GA refines these solutions to further improve system performance without violating harmonic constraints. The method is evaluated on both the IEEE 14-bus test system and a real 674-bus distribution network operated by Tubas District Electricity Company (TDECO). Sensitivity analysis and randomization of the nonlinear load location are employed to assess the robustness of the optimization results. The obtained results demonstrate that the hybrid GA–PSO approach provides competitive and stable performance compared with the individual algorithms. In the IEEE 14-bus system, the maximum THD decreased from 5.61% at Bus 9 in the base case to 1.4% with one filter and to 0.594% with two filters using the hybrid approach. For the large-scale TDECO network, the maximum THD was reduced from 6.34% in the original system to 0.919% with one filter and to 0.323% with two filters, while simultaneously reducing active power losses. The results confirm that the hybrid optimization strategy effectively mitigates harmonics in benchmark and real distribution networks with relatively low computational burden, making it suitable for practical applications.