User:Hafiz

Md Hafizur Rahman received his Bachelor of Science (B.Sc.) degree in Electrical and Electronics Engineering from Rajshahi University of Engineering and Technology, Bangladesh. Since February 2020, he has served as a lecturer at Eastern University. Currently, he is working as an assistant engineer at Dhaka Power Distribution Company Ltd. (DPDC). His MSc in Electrical & Electronic Engineering is also being pursued at Bangladesh University of Engineering & Technology.

Md Hafizur Rahman’s research expertise encompasses power electronics, power systems, renewable energy systems, and machine learning applications with a specialized focus on the design and application of power electronics in electric vehicles and renewable energy systems. In his MSc thesis work, he is doing his research on machine learning-based fault diagnosis of three-phase inverters, back-to-back converters, and three-phase induction motors. He also has extensive experience in power system modeling, fault diagnosis, and protection system design with a strong background in SIMULINK-based simulations. His interdisciplinary expertise bridges power electronics, renewable energy integration, power systems, and advanced control strategies, making significant contributions to the field of sustainable energy systems

• Power electronic system design and controlling.
• Fault diagnosis.
• Wide Band Gap Semiconductor (SiC, GaN).
• Machine learning and deep learning application.
• Renewable Integrated Power System.
• Electric Vehicle.

Assistant Engineer (February 2023 - present)

Dhaka Power Distribution Company Ltd. (DPDC), Dhaka, Bangladesh.

• Oversee operation of substations, feeders, transformers, and distribution lines.
• Ensure uninterrupted power supply by conducting routine inspections and preventive maintenance.
• Supervise fault diagnosis and quick restoration during outages.
• Assist in the planning and execution of new distribution projects (e.g., underground cabling, distribution lines).
• Monitor ongoing infrastructure development or renovation work.
• Monitor and analyze load data to optimize network performance.
• Coordinate with the load dispatch center to manage load shedding or demand fluctuations.
• Supervise installation of electrical equipment such as RMUs, transformers, breakers, and meters.
• Test and commission new systems before integration into the grid.

Lecturer (February 2020 - February 2023)

Eastern University, Dhaka, Bangladesh

Department of Electrical & Electronic Engineering

• Courses offered: Industrial Power Electronics, Power Systems, Electrical Transmission & Distribution Systems, Power System Protection, Control Systems, Numerical Analysis, Digital Signal Processing, Electromagnetic Fields, Microprocessors, and Interfacing.
• Lab instructor: Industrial Power Electronics, Power Systems, Power System Protection, Control Systems, Electronics I, and Electronics II laboratory.
• Student Coordinator of EEE Batches 18 and 19 (80 students)
• Member of the accreditation and self-assessment report (SAR) committee.

• This study proposes a deep learning-based method to detect such attacks effectively.
• The approach leverages advanced neural network architectures to analyze charging station data and identify anomalies caused by malicious data manipulation.
• The model is trained on real and simulated datasets to improve detection accuracy and reduce false positives.
• Experimental results demonstrate the method’s high precision and reliability in identifying false data injection attacks.
• The proposed solution enhances the security and resilience of EV charging infrastructure against cyber threats.
• This work contributes to safeguarding EV grid components, ensuring trustworthy and efficient power delivery.

• Open switch faults in three-phase inverters.
• The Tab-Net algorithm is used to detect and classify multiple open switch faults in insulated gate bipolar transistor (IGBT) modules.
• The proposed method leverages normalized average current values as input features, enabling a compact yet highly discriminative representation of fault conditions.
• Unlike conventional approaches, the model exhibits load-independent behavior, making it suitable for a wide range of real-world EV operating conditions.
• The architecture is optimized for robustness and generalizability, achieving 98.33% classification accuracy across various fault scenarios involving multiple IGBT switches.
• Extensive simulations and experimental validations confirm the effectiveness of the proposed approach, demonstrating superior performance in comparison to traditional fault diagnosis techniques.

• Proposed approach combines Multi-Layer Perceptron Artificial Neural Network (MLP-ANN) with Discrete Wavelet Transformation (DWT) for accurate fault identification.
• DWT improves measurement quality by reducing noise and extracting key features.
• Extracted features are used to train the MLP-ANN to classify different fault types effectively.
• The method is implemented and tested in MATLAB/Simulink to model the power system realistically.
• Results demonstrate high accuracy (99.4%) and exceptional performance in identifying HVDC line faults.
• The approach supports enhanced grid management and reliable power supply amid growing global energy demands.

• Multiple open switch fault diagnosis of three-phase inverter.
• Different machine learning algorithms (Linear Regression, Logistic Regression, Decision Tree, RF, SVM, KNN, Naïve Bayes, and Ensemble Decision Tree) are used to compare the performance.
• Simulation-based observation.
• Under load variation condition.

• Indium gallium phosphide (InGaP) makes up the first cell of this tandem cell, which has an energy gap of 1.9 eV.
• Gallium arsenide (GaAs) makes up the middle cell, which has an energy gap of 1.42 eV, Indium Gallium Arsenide Phosphide (InGaAsP) makes up the bottom cell, which has an energy gap of 1.2 eV and (GaAs) is a tunnel junction.
• An optimal design for an all-lattice-matched multijunction solar cell with a 5.65A° lattice constant is presented, along with a thorough performance investigation using full device modeling.
• According to the models, a 3-junction InGaP/GaAs/InGaAsP solar cell with a Voc of 2.05 V can attain efficiencies of greater than 41% when exposed to one sun.
• Tests were also conducted on the separately linked single junction solar cells, and the current density was compared to another. After then, tunnel junctions were stacked at every intersection. Top performance is attained by adjusting the thickness and doping concentration.
• All the simulations were done in Silvaco Atlas software.

• A new DC–DC step-up converter is proposed with high voltage gain and high efficiency.
• The converter is analyzed in two configurations: With magnetic coupling network & without magnetic coupling network
• For both configurations, output voltage and switching stress are calculated.
• Results show the proposed converter achieves higher voltage gain and lower switching stress compared to the conventional converter.
• The system is simulated in MATLAB/Simulink, and simulation results agree with analytical predictions.
• Achieved efficiencies: 96% without magnetic coupling & 94.5% with magnetic coupling
• Overall, the converter demonstrates strong performance in terms of efficiency, voltage gain, and reduced component stress.

• This work has proposed a high static gain DC-DC converter with improved efficiency.
• It provides high voltage gain with reduced voltage stress across the semiconductor devices.
• The operating principle, steady-state analysis, and design guidelines of the proposed converter are discussed in detail.
• An ANN-based MPPT technique has been proposed with temperature and solar irradiance variation.
• The proposed model is verified in the MATLAB simulation.
• Simulation results obtain high efficiency, a high step-up ratio, and various levels of output.