Self-healing strategy to enhance microgrid resilience during faults occurrence

Abstract

The interruption in the power system has a substantial social and economic effect, especially during natural disasters and faults causing a massive outage. Therefore, the appropriate and fast diagnosis of the fault locations and service restoration should be conducted quickly to restore power to as many undamaged sections as possible. In this context, self-healing service restoration can restore power systems and enhance distribution networks’ resilience. This paper proposes a novel self-healing topology to maintain the power system's balance while prioritizing the critical loads in a micro-grid system. The micro-grid system is operated in two different modes, which are the normal mode and self-healing mode. In the normal mode, the energy management system (EMS) solves the economic power dispatch function to minimize the cost while satisfying the load demand. The self-healing mode is triggered once a fault occurred in any of the distributed energy resources (DERs). This mode aims to maximize the undamaged DERs power generation to meet the total prioritized critical loads. The metaheuristic binary teaching-learning-based optimization (BTLBO) technique is utilized to obtain the service restoration problem's optimal switching action sequence and avoid tuning the parameters to handle the problem of premature convergence. The micro-grid model is based on the IEEE 37 bus, where several disturbance scenarios have been simulated to evaluate the proposed self-healing topology's performance. The results show the proposed technique outperforms traditional self-healing methods.