Ehsan Sheikhasadi
PhD thesis title: Optimization of Energy Networks Equipped with Prognostics and Health Management Capabilities
Academic Tutor: Francesco Di Maio
Academic Supervisor: Enrico Zio
Industrial Supervisor: Michele Compare (Greydient)
PhD cycle: 37° (see all student profiles of the same cycle > LINK)
BSc: Chemical Engineering, Sharif University of Technology
MSc: Industrial Engineering, K.N. Toosi University of Technology
Leibniz Universität Hannover, Germany (3 months), efficient simulation of interdependent complex networks with vine copula with M. Broggi / ETH Zurich, Switzerland (3 months), study of deep learning approaches with a focus on reinforcement learning with S. Marelli and B. Sudret
Marie Skłodowska-Curie Scholarship for PhD
Thesis abstract
I’ll develop a methodology to estimate the reliability and availability of energy-grid systems including components equipped with Prognostics and Health Management (PHM) capabilities.
In addition, I’ll apply the developed methods to obtain optimal solutions depending on the O&M costs as well.
In addition, I’ll apply the developed methods to obtain optimal solutions depending on the O&M costs as well.
Personal interest in my research theme
My profound interest in my research theme stems from its tangible impact and the intricate blend of mathematics and Artificial Intelligence. The research results, comprising innovative methodologies and algorithms, hold the potential to revolutionize current reliability and availability of energy-grid strategies. My passion lies in constructing mathematical optimization problems and utilizing AI tools, including Machine Learning methods and reinforcement learning, to resolve them. The excitement of pioneering solutions in this pivotal domain fuels my relentless pursuit of excellence.
