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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Abdelouahed Abounada
Sultan Moulay Slimane University
Control & Systems Engineering Electrical & Electronics Engineering

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Robust non-linear control of a hybrid water pumping system based on induction motor Zakaria Massaq; Abdelouahed Abounada; Mohamed Ramzi
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 11, No 4: December 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v11.i4.pp1995-2006

Abstract

This contribution presents a non-linear control of a hybrid pumping system supplied with a photovoltaic generator and a battery. This system is employed for delivering a continuous volume of water whatever the climatic conditions. In the DC side, a boost converter is controlled with the indirect double integral sliding mode controller (DISMC) for maximum power point tracking (MPPT). The DISMC is suitable for MPPT because it gives a fast response and reduces the amplitude of power oscillations. Then, a bidirectional buck-boost converter is adopted to ensure the energy management between the battery and the DC-bus, and this converter is controlled with integral sliding mode control (ISMC) theory.  The non-linear predictive control (NPC) is chosen to drive an induction motor (IM), the NPC is known by its fast dynamic and high capacity to reject disturbances. The hybrid system is modelled in MATLAB/Simulink software. During simulations, the DISMC-MPPT is compared with other techniques such as sliding mode controller (SMC) MPPT and integral SMC MPPT, the DISMC provides the best tracking performances under different irradiances. Moreover, the designed controller for the bidirectional converter regulates the DC-link voltage with better performances than the classical PI controller. Lastly, the NPC regulates the speed of the IM with high robustness.
Adaptive control of the virtual synchronous generator by deep neural networks for a wind high power conversion chain Wijdane El Maataoui; Abdelouahed Abounada
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 17, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v17.i2.pp1440-1450

Abstract

The virtual synchronous generator (VSG) is commonly used to reproduce the inertial response of conventional synchronous machines. However, the VSG control architecture relies on controller chains, benchmark transformations, and parameter settings, including virtual inertia and damping, which limit its flexibility in highly dynamic environments. This paper proposes an innovative end-to-end control approach based on a neural network to fully replace the classical VSG control structure. The neural network developed is trained to directly generate inverter control signals from real-time electrical measurements, including voltages and currents, as well as active and reactive power. A dataset is generated from a detailed VSG model under different operating conditions, and then a multilayer neural network is trained using supervised learning with MATLAB. The resulting model is then integrated into a complete wind energy conversion chain simulated in Simulink. The simulation results demonstrate that control based on artificial neural networks ensures better frequency and voltage stability, more accurate tracking of the active power injected, and a significant improvement in power quality, with total harmonic distortion (THD) reduced to 0.04%, compared to 0.51% for conventional VSG control. These results confirm the potential of artificial intelligence-based approaches for the intelligent control of renewable energy systems.
Co-Authors Mohamed Ramzi Wijdane El Maataoui Zakaria Massaq