M. CHOUG Noreddine

MCB

Directory of teachers

Department

BASE COMMON ST Departement ST

Research Interests

Specialized in BASE COMMON ST Departement ST. Focused on academic and scientific development.

Contact Info

University of M'Sila, Algeria

Email : choug.noreddine@univ-msila.dz

On the Web:

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Recent Publications

2025-05-07

Adaptive and Robust Control of DFIG-Based Wind Energy Conversion System Using Fuzzy Logic

the objective of this work is to optimize the performance of
a wind energy conversion system (WECS) based on a DoublyFed Induction Generator (DFIG). A vector control strategy
based on stator flux orientation is implemented to
independently regulate the active and reactive power
exchanged between the stator and the grid. three innovative
controllers are proposed and compared: a fuzzy logic
controller (FLC) and a Proportional-Integral controller with
Adaptive Fuzzy Gain Scheduling (AFGPI). The latter adjusts
its gains in real time using a fuzzy algorithm based on the error
and its derivative, thereby improving the system's dynamic
response. Simulations performed in MATLAB/Simulink on a
1.5 MW DFIG model validate the effectiveness of both
approaches, with the AFGPI showing particular robustness
against wind speed variations and grid disturbances. The
results highlight a clear advantage in terms of dynamic
performance and stability compared to conventional control
methods.
Citation

M. CHOUG Noreddine, (2025-05-07), "Adaptive and Robust Control of DFIG-Based Wind Energy Conversion System Using Fuzzy Logic", [national] The First National Conference on Renewable Energies and Advanced Electrical Engineering NC-REAEE’25 , Mohammed BOUDIAF University of M'Sila

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2024-04-25

Advanced Direct Torque Control: Employing Fuzzy Logic for Dynamic and Adaptive Regulation

this article presents a novel approach to Direct Torque Control (DTC) for doubly fed induction machines using fuzzy logic. Traditional DTC suffers from limitations due to its inflexible switching strategy, treating large and small torque and flux errors identically. This can lead to suboptimal performance, particularly during start up or when reference values fluctuate. Our proposed solution leverages a streamlined fuzzy logic controller with a minimized rule set to enhance the switching strategy. This approach replaces the conventional hysteresis-based regulators and switching table, resulting in reduced computational burden and a faster sampling period. This improved sampling rate translates to significantly smoother torque and flux control with reduced ripple. Furthermore, we've integrated fuzzy logic as a supervisory element to dynamically adjust the PI controller gains for speed regulation, effectively creating a nonlinear PI controller with adaptive parameters. Simulations conducted in MATLAB/SIMULINK showcase the effectiveness and superior performance of this advanced DTC strategy.
Citation

M. CHOUG Noreddine, (2024-04-25), "Advanced Direct Torque Control: Employing Fuzzy Logic for Dynamic and Adaptive Regulation", [national] Journal of Electrical Systems (JES) , 10.52783/jes.8394

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