About Me
I am currently a Ph.D. candidate in Electrical Engineering at Iowa State University’s (ISU) Department of Electrical and Computer Engineering (ECpE) located in Ames, IA, USA. My research interests center around data driven tools for actionable intelligence and the application of statistical and machine learning methods to improve the resilience and reliability of power systems. I am under the mentorship of Prof. Ian Dobson as I pursue my Ph.D. degree.
In 2021, I joined Iowa State University as a Fulbright Scholar and obtained my M.S. degree in 2023. Prior to that, I worked for an electrical power utility in Pakistan as a power distribution system operations engineer from 2016 to 2021. In 2015, I studied at the University of Twente in the Netherlands for an M.S. in Electrical Engineering, and in 2014, I completed my B.S. in Electrical Engineering from the University of Punjab in Pakistan.
Technical Expertise
Power Systems Analysis
- PSS/E, PSLF, PSCAD
- PowerWorld Simulator, OpenDSS
- ETAP, MatPower, Simscape, Simulink
Statistics & Data Analysis
- Python (Pandas, NumPy, Scikit-learn)
- R, MATLAB, Mathematica
- Microsoft Excel, Power BI, WEKA
Programming Languages
- Python, C#, C++, C
- Java, PHP, HTML
- Ruby
Recent Experience
Engineering Intern
- Evaluated the resilience benefits of transmission system investments using TADS data.
- Integrated weather, TADS, and GADS data for BPS resilience analysis and performed GIS mappings of BPS assets.
Student Employee
- Developed methods to conduct Risk Analysis, Cost/Benefit Analysis, and Sensitivity Analysis for the integration of Grid Enhancing Technologies (DLR and AAR) in transmission planning and operations.
- Performed case studies using PSS/E and Python to test and validate the developed methods.
Graduate Research Assistant
- Developed statistical models using power distribution system data to enhance reliability and resilience.
- Applied supervised and unsupervised machine learning techniques to 10+ projects as a Business Analytics Student.
Research Highlights
Towards using utility data to quantify how investments would have increased the wind resilience of distribution systems
The improvement in transmission resilience metrics from reduced outages or faster restoration can be calculated by rerunning historical outage data
Quantifying distribution system resilience from utility data: large event risk and benefits of investments
Contact
- 1201 Coover Hall
- 2520 Osborn Drive, Ames, IA, 50011, USA
- +1 515 715 8471
- arslan@iastate.edu
- engr.arslan_ahmad@yahoo.com
- ww.iarslan.com/