An Adaptive Secondary Control Based on Recursive Neural Networks for Isolated Microgrids
L. Rodríguez(1), A. Pantoja(1), J. Revelo(1), and J. Barco-Jiménez(2,3,4) 1. Department of Electronics, University of Nariño 2. Program of Electronic Engineering, University CESMAG 3. Postgraduate Programs in Electrical and Electronic Engineering - PPIEE University of Valle 4. Electrical Engineering Department University of Malaga 2026-01-20

Abstract

This work proposes a model reference adaptivecontrol based on recursive neural networks. This secondary-level controller corrects the deviations on the voltage and frequency setpoints of a simple primary control in an isolated microgrid (MG) with multiple distributed generators (DGs). The controller has two nonlinear autoregressive with external input neural networks to emulate the microgrid and guide the whole system according to an appropriate reference model. The networks are trained with synthetic data from a simulation MG and the order of the networks are obtained with a deterministic method. The method is tested with a three-units MG in a Matlab simulation under different working conditions. Results show the proper performance of the proposed controllers in comparison to a PI strategy.

Key words: Model reference adaptive control, microgrids control, recursive neural networks, secondary control.

References

[1] D. S. Burbano-Benavides, O. D. Ortiz-Sotelo, J. Revelo-Fuelagán, and J. E. Candelo-Becerra, “Design of an on-grid microinverter control technique for managing active and reactive power in a microgrid,” Applied Sciences, vol. 11, no. 11, 2021.

[2] M. H. Cintuglu, T. Youssef, and O. A. Mohammed, “Development and application of a real-time testbed for multiagent system interoperability: A case study on hierarchical microgrid control,” IEEE Transactions on Smart Grid, vol. 9, no. 3, pp. 1759–1768, 2018.

[3] M. A. Ebrahim, B. A. Aziz, F. A. Osman, and M. N. F. Nashed, “Optimal PI based secondary control for autonomous micro-grid via particle swarm optimization technique,” in 2018 Twentieth International Middle East Power Systems Conference (MEPCON), pp. 1148–1155, 2018.

[4] M. Yazdanian and A. Mehrizi-Sani, “Distributed control techniques in microgrids,” IEEE Transactions on Smart Grid, vol. 5, no. 6, pp. 2901–2909, 2014.

[5] A. Parisio, E. Rikos, and L. Glielmo, “A model predictive control approach to microgrid operation optimization,” IEEE Transactions on Control Systems Technology, Vol. 22, no. 5, pp. 1813–1827, 2014.

[6] S. K. Sahoo, A. K. Sinha, and N. K. Kishore, “Control techniques in AC, DC, and hybrid AC/DC microgrid: A review,” IEEE Journal of Emerging and Selected Topics in Power Electronics, Vol. 6, no. 2, pp. 738–759, 2018.

[7] P. I. N. Barbalho, V. A. Lacerda, R. A. S. Fernandes, y D. V. Coury, “Deep reinforcement learning-based secondary control for microgrids in islanded mode”, Electr. Power Syst. Res., Vol. 212, no 445374, p. 108315, 2022.

[8] M. Adibi y J. Van Der Woude, “Secondary Frequency Control of Microgrids: An Online Reinforcement Learning Approach”, IEEE Trans. Automat. Contr., Vol. 67, no 9, pp. 4824-4831, 2022.

[9] G. Julio, O. Leony, A. Alexander, P. Wilson, and R. Milton, “An hybrid algorithm based NARX for non-linear identification and modelling of an AC/DC hybrid microgrid simulation,” in 2022 IEEE Sixth Ecuador Technical Chapters Meeting (ETCM), pp. 1–6, 2022.

[10] M. Norgaard, O. E. Ravn, N. K. Poulsen, and L. K. Hansen, “Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook,” Berlin, Heidelberg: Springer-Verlag, 1st ed., 2000.

[11] J. D. Bomberger and D. E. Seborg, “Determination of model order for NARXmodels directly from input-output data,” Journal of Process Control, Vol. 8, no. 5, pp. 459–468, 1998.