Real-Time Implementation of Self-Adaptive Salp Swarm Optimization-Based Microgrid Droop Control

Abstract

This article presents a new version of the salp swarm inspired algorithm (SSIA) for the optimal design of the microgrid droop controller. The new version of SSIA is originated from the hybridizing of SSIA with the updating features of the particle swarm optimization (PSO). The development of SSIA is achieved by applying referential integrity between leaders and followers' candidates via employing both position and velocity update property of PSO. The hybrid SSIA-PSO also has a self-adaptive mechanism to avoid the necessity of refining the algorithm parameters for each optimization problem. Twenty-three benchmark test systems are tested to validate the superiority of the improved SSIA over the original PSO and SSIA. The proposed SSIA-PSO based control strategy is experimentally tested in a real-time environment. The control platform's performance is experimentally tested by using the Texas Instruments Launchpad TMS320F28379D. The developed real-time hardware-in-the-loop setup is a real investigation for implementing the suggested SSIA-PSO based control strategy with a low-cost control platform. The attained results prove the efficacy of the hybrid SSIA-PSO algorithm over the presented techniques. The introduced hybrid SSIA-PSO is employed to tackle the real microgrid droop control uncertainties such as inaccuracy in controller gains, deterioration of system parameters, multi-sources energy sharing challenge and system dynamics.

Keywords

• Droop control
• microgrid
• particle swarm optimization
• salp swarm inspired algorithm
• TMS320F28379D