FPGA-Based Model Predictive Control for Three-Phase Smart Grid Automation

¹ IEEE SKillUp Hub, Region 8, Center for Future Technologies, University of Chichester, Bognor Regis, PO21 1HR, U.K.
² Department of Electrical and Electronic Engineering Science, University of Johannesburg, Johannesburg 2006, South Africa.
³ Imperial College London, 2 South Kensington Campus, Exhibition Road, London, SW7 2AZ, UK
⁴ Department of Engineering, Manchester Metropolitan University, M1 5GD Manchester, U.K.
⁵ Department of Prosthetics and Orthotics, Federal University of Allied Health Science Enugu, Nigeria.
⁶ Department of Computer Science, University of Nigeria, Nsukka
⁷ Department of Computer Engineering, University of Uyo
⁸ Department of Industrial & Production Engineering, Nnamdi Azikiwe University, Awka, Nigeria
⁹ Department of Industrial Technology Education, Nnamdi Azikiwe University, Awka,
¹⁰ Software Engineering Department, University of Calabar,
¹¹ Computer Engineering Department, University of Calabar

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

This study presents a smart real-time control framework for power management in virtualized grid environments for emerging African smart energy systems. The system coordinates power flow among distributed energy resources, including conventional generators and virtual microgrids, to ensure stable and reliable grid operation under unstable supply and fluctuating demand. An integer-order compartmental model is used to describe system dynamics under disturbances and varying operating conditions. A Model Predictive Control (MPC) scheme is implemented on a Field-Programmable Gate Array (FPGA) to achieve high-speed computation and low-latency control. A fuzzy logic supervisory layer improves adaptability under uncertainty and nonlinear conditions. Numerical simulations based on the Runge-Kutta method show stable, bounded system behaviour with low error propagation under optimal parameter settings. (β_i = 0.0003, σ = 5 × 10⁻⁵, ν = 0.03). Sensitivity analysis identifies the fuzzy adaptive gain ν as a key stability parameter. Results demonstrate improved transient response, enhanced dynamic stability, and robust performance. The framework demonstrates strong theoretical potential for resilient, intelligent smart grid automation across Africa.