Dr. Muhammad Akram Bhayo
Assistant Professor
Electrical EngineeringBiography
Muhammad Akram Bhayo, was born in Khairpur Mir’s, Sindh, Pakistan, in 1980. He received the B.E (Electrical) degree from Q.U.E.S.T, Nawabshah, Pakistan in 2004 and the M.Sc. (Electrical & Electronic Engineering) degree from Universität Duisburg-Essen, Germany, in 2013 and PhD (Electrical Engineering) from Universiti Teknologi Malaysia (UTM), Malaysia in 2021. Since 2006, he has been with the Department of Electrical Engineering, Q.U.E.S.T, Nawabshah, Pakistan, where he is currently working as an Assistant Professor. He has published a number of research articles in various national, international journals and conferences. His current research interests include modeling and simulation of wind energy conversion system, with focus on implementation of Adaptive Neuro –Fuzzy Inference System (ANFIS) based controllers in wind turbine emulator, Microgrid and PID Optimization techniques.
Research Interests
Memberships
Courses Teaching
Contact Info
City: Khairpur, Mir's
Phone: +923081220991
Email: bhayoakram@quest.edu.pk
CV: View CV
Publication Stats (1996 – 2025)
Qualifications
- PhD (University of Technology Malaysia, Malaysia, )
- M.Sc. (University of Duisburg-Essen, Germany, )
- B.E (Q.U.E.S.T. Nawabshah, )
Experiences
- Assistant Professor at Q.U.E.S.T. Nawabshah (2019 - Present)
- Lecturer at Q.U.E.S.T. Nawabshah (2006 - 2019)
- Electrical Engineer at Global Technologies, Islamabad (2006 - 2006)
Publications
Evaluating Students‘ Perceptions of Microsoft Teams for Online Academics Improvement
()
Design and Development of aWind Turbine Emulator for Analyzing the Performance of Stand-alone Wind Energy Conversion System
()
Islanding protection and islanding detection in low voltage cigre distribution network with distributed generations
()
Automated recognition of single & hybrid power quality disturbances using wavelet transform based support vector machine
()
Discrete Wavelet Transform based Probabilistic Neural Network Technique of Detection and Classification of Power Quality Disturbances
()
Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids
()
Distant Temperature and Humidity Monitoring: Prediction and Measurement
()
Machine learning-based electricity theft detection using support vector machines
()
Machine Learning-Based Multiclass Anomaly Detection and Classification in Hybrid Active Distribution Networks
()
Machine Learning-Based Islanding Detection Technique for Hybrid Active Distribution Networks
()
Tunicate Swarm Algorithm based Optimized PID controller for Automatic Generation Control of Two Area Hybrid Power system
()
Hybrid ANFIS – PI-Based Robust Control of Wind Turbine Power Generation System
()
Experimental Hybrid Control Approach for Wind Turbine Emulator
()
High precision experimentally validated adaptive neuro fuzzy inference system controller for DC motor drive system
(2025)