Hybrid Transient Search Algorithm With Levy Flight for Optimal PI Controllers of Islanded Microgrids

Abstract

This study combines the metaheuristic algorithm Transient Search Optimization (TSO) with the Levy flight distribution to find the optimal proportional-integral (PI) controllers for robust operation of islanded microgrids. The first step is to use the response surface methodology (RSM) to empirically express the multi-objective function. This function includes the transient variations of the terminal voltages of the microgrids. To demonstrate the efficacy of the hybrid Levyflight and TSO (LTSO), a benchmark microgrid system undergoes rigorous testing under different operational scenarios: i) transitioning the system into autonomous mode by disconnecting from the main grid; ii) adapting to varying load conditions while isolated; and iii) responding to a 3-phase fault while operating in islanded mode. Numerous simulations are run to verify the suggested methodology, employing conventional data extracted from the PSCAD/EMTDC software. The study’s findings are further reinforced through a comparative analysis with established optimization techniques such as the least mean and the square root of exponential approaches, the enhanced block-sparse adaptive Bayesian algorithm, the adaptive-width generalized correntropy diffusion algorithm, the sunflower optimization algorithm, the Coot bird metaheuristic optimizer, and particle swarm optimization. The results collectively underscore the superiority of the LTSO algorithm in enhancing the transient response of the terminal voltages of islanded microgrids, thereby offering a promising avenue for optimizing the control and stability of such systems.

Keywords

• Coot bird metaheuristic optimizer
• distributed generators
• renewable energy
• microgrid
• sunflower optimization algorithm