Anouar Belahcen

Professor

T410 Dept. Electrical Engineering and Automation

Vice Dean

School services, ELEC

I do research in the field of magnetic materials modelling and numerical analysis of energy conversion devices. My interests are the losses in electrical machines, model order reduction methods, Multi-scale modelling of electrical steel, diagnosis and condition monitoring of electrical machines, magnetomechanics, and noise and vibrations, among others

Full researcher profile

https://research.aalto.fi/...

anouar.belahcen@aalto.fi

Phone number

+358504602366

Areas of expertise

Electrical Machines, Electromagnetic field computation, Magnetic material modelling, Magnetostriction, Magneto-elasticity, Energy conversion, 213 Electronic, automation and communications engineering, electronics, Electrical engineering, Electric power engineering, Metals, Computational science

Research groups

Computational Electromechanics

Publications

Single-Phase Transformerless Buck-Boost PV-Inverter For Leakage Current Minimization

Ahmed Ali, Ahmed Hemeida, Alaa Hassan, Mahmoud Saeed, Anouar Belahcen 2025 2024 25th International Middle East Power System Conference (MEPCON)

Challenges of Iron losses Characterization, Modelling, and Computation

Anouar Belahcen 2025 2025 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD)

Ensemble Learning-Based Data Augmentation for Condition Monitoring of Induction Machines

Md Masum Billah, Ahmed Hemeida, Karolina Kudelina, Bilal Asad, Muhammad Usman Naseer, Anouar Belahcen 2025 IET Electric Power Applications

Magnetic Losses and Domain Wall Activities: A Study of Barkhausen Noise under Rotational Magnetization

Benjamin Ducharne, Floran Martin, Anouar Belahcen 2025 IEEE Transactions on Instrumentation and Measurement

Co-simulations of induction machines coupled with a radial ball bearing for mechanical defects analysis

Nada El Bouharrouti, Maksim Sitnikov, Ahmed Hemeida, Floran Martin, Karolina Kudelina, Muhammad U. Naseer, Anouar Belahcen 2025 IET Electric Power Applications

Deep Transfer Learning Approach Using Filtered Time-Frequency Representations of Current Signals for Bearing Fault Detection in Induction Machines

Nada El Bouharrouti, Alireza Nemat Saberi, Muhammad Khan, Karolina Kudelina, Muhammad Usman Naseer, Anouar Belahcen 2025 IET Electric Power Applications