Dr Rafat Aljarrah is an assistant professor in electrical engineering at Princess Sumaya University for Technology (PSUT). He was awarded his PhD in Electrical and Electronics Engineering from The University of Manchester with Power System Engineering as a research subject. Also, he was awarded Postgraduate certificates in the field of power systems and renewable energy (Smart Grids & Sustainable Electricity Systems, Analysis of Electrical Power and Energy Conversion Systems, Power System Operation and Economics, and Solar Energy Technologies). He also holds an MSc in Electrical Power Engineering from Yarmouk University. Rafat worked at several institutions as a teaching and research assistant at the University of Manchester, a lecturer at the American University of Middle East AUM, and a teaching and research assistant at German Jordanian University GJU. Rafat’s research interest includes Future Power Systems, Fault Level Monitoring, Renewable Energy, Artificial Intelligence, and Power System Protection.
كلية الملك عبدالله الثاني للهندسة
- Aljarrah, Rafat, Hesamoddin Marzooghi, James Yu, and Vladimir Terzija. "Monitoring of fault level in future grid scenarios with high penetration of power electronics‐based renewable generation." IET Generation, Transmission & Distribution (2020).
- Aljarrah, Rafat, Hesamoddin Marzooghi, James Yu, and Vladimir Terzija. "Sensitivity analysis of transient short circuit current response to the penetration level of non-synchronous generation." International Journal of Electrical Power & Energy Systems 125 (2021): 106556.
- R. Aljarrah, H. Marzooghi, J. Yu and V. Terzija, "Issues and Challenges of Steady-State Fault Calculation Methods in Power Systems with a High Penetration of Non-Synchronous Generation," 2019 IEEE Milan PowerTech, Milan, Italy, 2019, pp. 1-6.
- R. Aljarrah, H. Marzooghi, J. Yu and V. Terzija, "Modifying IEC60909 Standard to Consider Fault Contribution from Renewable Energy Resources Utilizing Fully-Rated Converters," 2019 9th International Conference on Power and Energy Systems, Perth,Australia, 2019, pp. 1-6.
- Feilat, E.A., Aljarrah, R.R. and Rifai, M.B., 2017. Detection and classification of voltage variations using combined envelope-neural network based approach. Jordan Journal of Electrical Engineering. All rights reserved-Volume, 3(2), p.113.