Mr. Muhammad Riaz, KP TEVTA, Pakistan
Mr. Muhammad Riaz is a dedicated educator and researcher with expertise in electrical engineering, specializing in power systems and sustainable energy solutions. He holds a Master’s in Electrical Engineering from the University of Wah, where his thesis focused on optimizing power flow in renewable energy-integrated systems. With extensive experience in teaching, laboratory management, and fieldwork, Mr. Riaz has a proven track record of mentoring students and advancing practical engineering solutions. Passionate about smart grids, artificial intelligence, and control systems, he is committed to shaping the next generation of engineers while contributing to innovative energy solutions.
Professional Profile
Suitability for Best Researcher Award
Mr. Muhammad Riaz’s dedication to advancing sustainable energy solutions and his impactful contributions to electrical engineering make him a strong candidate for the Best Researcher Award. His research on optimal power flow and renewable energy integration addresses critical challenges in modern power systems. His academic achievements, teaching excellence, and commitment to mentoring future engineers highlight his multifaceted contributions to the field. Mr. Riaz’s innovative approach and focus on practical solutions align perfectly with the award’s vision of recognizing transformative research.
Education
Mr. Muhammad Riaz completed his Master’s in Electrical Engineering at Wah Engineering College, University of Wah, in 2021, specializing in Power Engineering. His thesis, “Optimal Power Flow Solution of Modified IEEE 30 Bus System Integrated with Renewable Energy Sources,” highlights his focus on integrating renewable energy into power systems. He earned his Bachelor’s in Electrical Engineering from Bahria University Islamabad in 2018, where he designed a battery-free MPPT solar inverter as his final year project. His academic journey reflects a strong foundation in electrical and electronics engineering, complemented by minors in computer and communication engineering.
Experience
As a Lecturer at the Govt. Technical & Vocational Training Authority KP since 2021, Mr. Riaz has designed comprehensive lesson plans and delivered engaging lectures on motors, generators, PLCs, and solar PV systems. He also mentors students, fostering innovation and critical thinking. Previously, as a Lab Engineer at Wah Engineering College, he developed and implemented laboratory experiments, collaborated with faculty to optimize resources, and maintained equipment for effective teaching. His hands-on experience and dedication to teaching underscore his commitment to advancing electrical engineering education.
Awards and Honors
- Fully Funded Scholarship: Secured a scholarship for BEE from the ICT R&D Fund, Government of Pakistan.
- Rector’s Honor List: Recognized with Cum Laude distinction at Bahria University Islamabad.
- Prime Minister Laptop Scheme: Awarded under the national initiative for academic excellence.
These accolades reflect Mr. Riaz’s academic excellence and commitment to professional growth.
Research Focus
Mr. Muhammad Riaz’s research focuses on smart grids, optimal power flow, and the application of artificial intelligence in power systems. His work emphasizes sustainable energy solutions, including the integration of renewable energy into existing grids. His interests extend to control systems and optimization algorithms, aiming to enhance energy efficiency and reliability. His research aligns with global efforts to address energy challenges through innovative and intelligent systems.
Publication Top Notes
Article: An Optimal Power Flow Solution of a System Integrated with Renewable Sources Using a Hybrid Optimizer
Article: An Optimization-Based Strategy for Solving Optimal Power Flow Problems in a Power System Integrated with Stochastic Solar and Wind Power Energy
Conference Paper: An Innovative Model Based on FCRBM for Load Forecasting in the Smart Grid
Conference Paper: Day Ahead Electric Load Forecasting by an Intelligent Hybrid Model Based on Deep Learning for Smart Grid