Lorenzo Donadio Sepulveda
PhD
Design of optimal mitigation actions through digital twinning
Host organisation
University of Porto, Faculty of Engineering (FEUP)
Project Description
My project focuses on advancing the digital twinning of wind turbines to enhance performance, reliability, and predictive maintenance. By integrating machine learning, atmospheric modeling, and real-world sensor data, I aim to simulate turbine behavior under various environmental and operational conditions. These digital twins will support the optimization of control strategies, minimize downtime, and extend turbine lifespans, directly contributing to more efficient wind energy systems. The ultimate goal is to help make wind energy a more dependable component of the grid.
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Supervisors
Filipe Magalhaes
Background
I’m Italian and Colombian, with a background in environmental engineering from EPFL, where I completed my MSc with an energy minor. My academic journey has taken me across Europe, from Switzerland to the Netherlands and Portugal, combining technical innovation with sustainable development. I have conducted research on wind power forecasting, turbine collision risk, and digital solutions for energy systems. Outside of academia, I’ve co-founded a clean water startup and worked in the climate tech and building sustainability sectors. I enjoy cross-cultural teamwork and thrive in multilingual environments.
Motivation
With a strong belief in international collaboration and technological innovation for sustainability, I’m excited to contribute to the IntelliWind network. My passion lies in merging AI with environmental systems to make renewable energy more predictable and resilient. Having already worked on wind energy forecasting and digital modeling, I see IntelliWind as a unique chance to build technical expertise while actively shaping the future of Europe’s clean energy landscape
