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

Automated summary

This article proposes a distributed intelligent secondary control approach for power electronic-based ac microgrid using brain emotional learning-based intelligent controller (BELBIC). The BELBIC controller is capable of quickly learning and handling the complexity, nonlinearity, and uncertainty of the microgrid. It is fully model-free, regulating voltage amplitude and frequency deviations without prior knowledge of the system model and parameters. The proposed approach ensures low steady-state variations with higher bandwidth and accurate power-sharing through the droop mechanism. Additionally, primary control is achieved using robust finite control set-model predictive control and droop control to optimize the system's frequency bandwidth and power-sharing among distributed generations. Experimental tests conducted on a hardware-in-the-loop testbed validate the effectiveness of the proposed control strategy for various scenarios.