This paper proposes a distributed intelligent secondary control (SC) 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, non-linearity, 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 (FCS-MPC) 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.
Intelligent Secondary Control of Islanded AC Microgrids: A Brain Emotional Learning-based Approach
2023-01-01
Yeganeh , M S O , Oshnoei , A , Mijatovic , N , Dragicevic , T & Blaabjerg , F 2023 , ' Intelligent Secondary Control of Islanded AC Microgrids: A Brain Emotional Learning-based Approach ' , IEEE Transactions on Industrial Electronics , vol. 70 , no. 7 , pp. 6711-6723 . https://doi.org/10.1109/TIE.2022.3203677
Article (Journal)
Electronic Resource
English
DDC: | 629 |
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