Irfan Ali Channa | Power System Control | Excellence in Research

Dr. Irfan Ali Channa | Power System Control | Excellence in Research

Ph.D Scholor, Institute of Automation, Beijing University of Chemical Technology, Beijing China

Irfan Ali Channa is a highly motivated PhD scholar at the Institute of Automation, Beijing University of Chemical Technology in China. He is passionate about innovation in the field of AI, particularly in power systems. Irfan has a strong background in experimental design, literature review, and scientific writing. His career includes significant experience as a lecturer at Bahria Engineering University in Karachi, Pakistan, where he contributed to both academic and engineering solutions.

Education 🎓

Irfan Ali Channa is currently pursuing his PhD at the Institute of Automation, Beijing University of Chemical Technology in Beijing, China, a program he began in 2019 and is expected to complete in 2024. His advanced studies focus on leveraging artificial intelligence to enhance power systems. Prior to this, he developed a solid foundation in engineering education and research during his tenure at Bahria Engineering University in Karachi, Pakistan.

Experience 🏫

From 2014 to 2017, Irfan Ali Channa served as a lecturer at Bahria Engineering University in Karachi, Pakistan. During this period, he was responsible for providing academic services to students, developing engineering solutions, and facilitating research innovations. His role involved extensive interaction with both students and faculty, promoting a collaborative and progressive educational environment.

Research Interests 🔬

Irfan’s research interests lie primarily in the application of artificial intelligence in power systems. He is particularly focused on developing innovative methodologies for the classification and detection of power quality disturbances. His work frequently involves the use of advanced techniques such as deep learning, evolutionary algorithms, and swarm-based optimization. Irfan is also interested in renewable energy sources, as evidenced by his studies on wind potential and photovoltaic modules.

Awards 🏆

Throughout his academic and professional career, Irfan Ali Channa has received recognition for his contributions to the field of electrical engineering. His innovative research and dedication to advancing power system technology have earned him accolades within the academic community, particularly for his work on power quality disturbances and renewable energy analysis.

Publications Top Notes 📚

  1. A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid
    • Published in: Computers and Electrical Engineering, Elsevier (2024)
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    • Cited by: Articles focused on power quality and smart grid technology.
  2. Evolutionary and Swarm Based Optimization of Fit k-Nearest Neighbor Classifier for Classification of Power Quality Disturbances
    • Published in: Electric Power Components and Systems (2023)
    • Cited by: Articles related to optimization algorithms in electrical systems.
  3. Detection and classification of power quality disturbances using STFT and deep neural Network
    • Published in: Proceedings of the 2023 7th International Conference on Computer Science and Artificial Intelligence (2023)
    • Cited by: Research on deep learning applications in power quality analysis.
  4. A comparative study to analyze wind potential of different wind corridors
    • Published in: Energy Reports, Volume 9 (2023)
    • Cited by: Studies on renewable energy and wind power assessment.
  5. Temperature and irradiance based analysis of specific variation of PV module
    • Published in: Jurnal Teknologi, Volume 83(6) (2021)
    • Cited by: Research on photovoltaic module performance under varying conditions.

Ali Razban | Energy Management Award | Research and Development Excellence Award

Ali Razban | Energy Management Award | Research and Development Excellence Award

Ali Razban, INDIANA UNIVERSITY IUPUI, United States

Dr. Ali Razban is a distinguished academic and seasoned engineer specializing in controls, robotics, and energy management. Holding a Ph.D. from Imperial College, London, and an MBA from Purdue University, he serves as the Director of the Bachelor of Science in Energy Engineering program at IUPUI. With a Professional Engineering License and certifications in Energy Management, Dr. Razban is a recognized leader in his field. He has received numerous accolades, including the Frank E. Burley Distinguished Professor Award. Driven by a passion for innovation and education, he mentors aspiring scholars while contributing significantly to industrial energy efficiency and academic excellence.

publication profile

orcid

Education

Dr. Ali Razban earned his Ph.D. from Imperial College, University of London, specializing in Control, Robotics, and Automation. He holds dual M.S.E. degrees from the University of Michigan in Controls and Signal Processing, and Dynamic Systems and Controls. Further enhancing his expertise, he completed an M.B.A. from Krannert School of Management, Purdue University, focusing on Executive Management.

Professional Awards

His contributions have been recognized with awards such as the IUPUI Favorite Professor Award and the Industrial Assessment Center of Excellence Award.

Research focus

Dr. Ali Razban’s research primarily focuses on energy optimization and control systems in various domains. He has contributed significantly to the implementation of cloud-based Model Predictive Control (MPC) for HVAC systems in educational buildings, aiming to enhance energy efficiency and comfort. Additionally, his work extends to privacy-preserving federated learning for solar photovoltaic generation forecasting and probabilistic regression models for estimating electrical peak demand in commercial buildings. With expertise in HVAC systems, renewable energy integration, and predictive modeling, Dr. Razban’s research endeavors strive to address challenges in sustainable energy utilization, making notable contributions to the field of energy management.

Publication top notes