Resiliency-Oriented Optimization of Critical Parameters in Multi Inverter-Fed Distributed Generation Systems

Abstract

In the modern power grid, with the growing penetration of renewable and distributed energy systems, the use of parallel inverters has significantly increased. It is essential to achieve stable parallel operation and reasonable power sharing between these parallel inverters. Droop controllers are commonly used to control the power sharing between parallel inverters in an inverter-based microgrid. In this paper, a small signal model of droop controllers with secondary loop control and an internal model-based voltage and current controller is proposed to improve the stability, resiliency, and power sharing of inverter-based distributed generation systems. The distributed generation system’s nonlinear dynamic equations are derived by incorporating the appropriate and accurate models of the network, load, phase locked loop and filters. The obtained model is then trimmed and linearized around its operating point to find the distributed generation system’s state space representation. Moreover, we optimize the critical control parameters of the model, which are found using eigenvalue analysis, and Grey Wolf optimization technique. Through time-domain simulations, we show that the proposed method improves the system’s resiliency, stability, and power sharing characteristics.

Simulation-Optimization for the Planning of Off-Site Construction Projects: A Comparative Study of Recent Swarm Intelligence Metaheuristics

“…Because of the introduction of PSO and ACO, SI metaheuristics have witnessed a dramatic development, leading to tens of metaheuristics that have potential over PSO and ACO, while most of them use a lower number of tuning parameters. Examples of such recent SI metaheuristics are the firefly algorithm (FA) [37], grey wolf optimization (GWO) [38], the whale optimization algorithm (WOA) [39] and the salp swarm algorithm (SSA) [40].…”