Learning-based Grid Impedance Shaping Method Applied for High-Accuracy Power Hardware-in-the-Loop

Future power and energy systems integrate cutting-edge technologies such as power converter-interfaced distributed generation and energy storage systems and responsive loads organized as microgrid clusters. The performance evaluation of these complex systems requires flexible testbeds to provide relevant information under different operational scenarios. Thus, this paper proposes a Power Hardware-in-the-Loop (PHIL)'s power interface algorithm that uses a digital twin system (DTS) implemented through a learning-based virtual impedance control approach to provide high-accuracy experiments and enhance system stability. Experimental results obtained from a PHIL laboratory setup demonstrated the effectiveness of the proposed method.