Intelligent Secondary Control of Islanded AC Microgrids: A Brain Emotional Learning-Based Approach

This article proposes a distributed intelligent secondary control approach based on brain emotional learning-based controller (BELBIC) for power electronic-based ac microgrid (MG). The BELBIC is able to learn quick-auto and handle model complexity, nonlinearity, uncertainty of the MG. proposed fully model-free, indicating that voltage amplitude frequency deviations are regulated without previous knowledge system parameters. ensures low steady-state variations with higher bandwidth maintains accurate power-sharing droop mechanism. Furthermore, primary realized robust finite set-model predictive in inner level increase outer regulate among generations. Finally, experimental tests obtained from hardware-in-the-loop testbed validate strategy different cases.