Power quality enhancement in autonomous microgrid operation using Particle Swarm Optimization

Document Type

Journal Article


Faculty of Computing, Health and Science


School of Engineering / Centre for Communications Engineering Research




This article was originally published as: Al-Saedi, W. A., Lachowicz, S. W., Habibi, D. , & Bass, O. (2012). Power quality enhancement in autonomous microgrid operation using Particle Swarm Optimization. International Journal of Electrical Power & Energy Systems, 42(1), 139-149.


This paper presents an optimal power control strategy for an autonomous microgrid operation based on a real-time self-tuning method. The purpose of this work is to improve the quality of power supply of the microgrid where some Distributed Generation (DG) units are connected to the grid. Voltage and frequency regulation, and power sharing are the main performance parameters which are considered in this work, particularly during the transition from grid-connected to islanding operation mode and also during load change. In this work, two typical DG units are connected in parallel to configure the microgrid. The controller scheme is composed of an inner current control loop and an outer power control loop based on a synchronous reference frame and the conventional PI regulators. The power controller employs two typical strategies: active-reactive power (PQ) control strategy and voltage-frequency (Vf) control strategy. Particle Swarm Optimization (PSO) is an intelligent searching algorithm that is applied for real-time self-tuning of the power control parameters. The proposed strategy in this paper is that both DG units adopt the Vf control mode once the microgrid is islanded in order to regulate the microgrid voltage and frequency, whereas during the load change, only the second DG unit invokes the PQ control mode to ensure maximum power exportation. The results show that the proposed controller offers an excellent response to satisfy the power quality requirements and proves the validity of the proposed strategy.