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All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
Boum, Alexandre Teplaira
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Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Robust adaptive integral sliding mode control of a half-bridge bidirectional DC-DC converter Cham, Julius Derghe; Koffi, Francis Lénine Djanna; Boum, Alexandre Teplaira; Harrison, Ambe
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i1.pp114-128

Abstract

A novel approach to improving the dynamic response of a half-bridge bidirectional DC-DC converter is presented in this paper, particularly in the face of disturbances from internal or external sources. These converters, which are integral to the operation of DC microgrids, are responsible for stepping up or stepping down voltage as required. To optimize the converter's performance under varying conditions, we propose an adaptive integral sliding mode controller (AISMC) enhanced by particle swarm optimization (PSO). The proposed controller leverages the strengths of both super-twisting sliding mode control (STSMC) and adaptive control, providing a robust and responsive solution to the challenges posed by the converter's nonlinear dynamics. The system's stability is rigorously ensured through the application of Lyapunov stability criteria, which underpin the enhanced performance of the controller. Simulations conducted in the MATLAB/Simulink environment demonstrate that the AISMC-PSO outperforms conventional control strategies, offering superior stability, robustness, and precision. The results clearly indicate that the proposed approach minimizes errors and enhances the overall efficiency and reliability of the bidirectional half-bridge DC-DC converter, making it a highly effective solution for DC microgrid applications.
Robust adaptive sliding mode control of a bidirectional DC-DC converter feeding a resistive and CPL based on PSO Cham, Julius Derghe; Koffi, Francis Lénine Djanna; Boum, Alexandre Teplaira; Harrison, Ambe
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 15, No 4: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v15.i4.pp2397-2408

Abstract

A DC-DC converter functioning in bidirectional (two-way) mode is a crucial component of direct current (DC) microgrids since it allows electricity to flow in both directions. However, because of load changes and other factors, the DC-bus voltage might become unstable. This research proposes a robust adaptive controller for a half-bridge two-way DC-DC converter founded on particle swarm optimization (PSO). Using a DC-DC half-bridge bidirectional converter, the effectiveness of various conventional and proposed control techniques is investigated. In comparison to a conventional sliding mode controller (CSMC), it is found that a PSO-based sliding mode control with an adaptive law is the optimal control approach for a bidirectional half-bridge DC-DC converter. This is because minimal steady-state error and the shortest rising and settling times are guaranteed. The benefits of robustness, chattering reduction, and simple design are combined in the suggested controller, which is especially beneficial when dealing with load and input voltage changes. The controller ensures robustness and stability in the face of parameter changes. Numerical simulations conducted in a MATLAB-Simulink environment on a DC-DC half-bridge converter operating in bidirectional mode show the controller's improved performance over its existing counterpart.
PSO-based adaptive sliding mode control of a bidirectional DC-DC converter with an improved reaching law Cham, Julius Derghe; Koffi, Francis Lénine Djanna; Gabriel, Ekemb; Boum, Alexandre Teplaira
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 16, No 2: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v16.i2.pp998-1011

Abstract

This paper explores the development of an adaptive sliding mode control (ASMC) that incorporates an improved optimal reaching law. We intend to use the proposed ASMC in DC microgrids or electric vehicle applications to regulate a bidirectional (two-way) buck-mode DC-DC converter. To initiate the design process, we develop a mathematical model of the converter operating in the charging mode. A particle swarm optimization is employed to help get the controller’s gains to better performance. By capitalizing on the benefits of an ASMC algorithm, the developed controller achieves improved reaching conditions, increased robustness, and strengthened stability. The efficacy of the suggested controller in comparison to conventional sliding mode control (CSMC) and ASMC is demonstrated through MATLAB/Simulink simulations conducted on the converter. The comparison demonstrates that the proposed controller achieves the intended transient response in steady-state conditions with minimal error and better reference tracking. The performance of the suggested controller is robust with regard to the rejection of variations in source voltage and load resistance. For applications involving DC microgrids or electric vehicles, the suggested controller will guarantee a consistent DC transit voltage.
A smart grid fault detection using neuro-fuzzy deep learning algorithm Mouckomey, Etienne Francois; Bikai, Jacques; Mbey, Camille Franklin; Boum, Alexandre Teplaira; Souhe, Felix Ghislain Yem; Kakeu, Vinny Junior Foba
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp5096-5105

Abstract

This paper proposes a novel data analysis framework that integrates deep learning with a binary neuro-fuzzy algorithm to address the problem of fault localization in smart power grids. In the first stage, a long short-term memory (LSTM) network is employed to train data samples collected from smart meters. The resulting learned features are subsequently utilized by an adaptive neuro-fuzzy inference system (ANFIS) for accurate fault detection and classification. Through this intelligent hybrid approach, multi-phase faults can be efficiently identified using a limited amount of data. The proposed method distinguishes itself by its capacity to rapidly train and test large datasets while maintaining high computational efficiency. To evaluate the performance of the model, an advanced simulation of the IEEE 123-node test feeder is conducted. The robustness and effectiveness of the proposed framework are validated using multiple performance metrics, including precision, recall, accuracy, F1-score, computational complexity, and the ROC curve. The results demonstrate that the proposed deep learning–based model significantly outperforms existing approaches in the literature, achieving a fault detection and classification precision of 99.99%.
Co-Authors Bikai, Jacques Cham, Julius Derghe Gabriel, Ekemb Harrison, Ambe Kakeu, Vinny Junior Foba Koffi, Francis Lénine Djanna Mbey, Camille Franklin Mouckomey, Etienne Francois Souhe, Felix Ghislain Yem