Samuel Loureiro
PhD
PhD scholarship in Development and Implementation of an Autonomous Decision Support System for Optimized Maintenance in Wind Turbine Infrastructure
Host organisation
DTU Wind and Energy Systems
Project Description
The project aims to develop and implement an autonomous Decision Support System (DSS) to optimize maintenance in wind turbine infrastructure.
The DSS will integrate advanced data analysis and intelligent algorithms to detect and assess faults that degrade turbine and plant performance. Key objectives include identifying health-related signals and metrics, selecting suitable sensors and data sources, and creating automated tools to evaluate performance indicators in relation to environmental and operational factors.
The resulting modular system will support trustworthy, data-driven decision-making for predictive maintenance and operational optimization, contributing to the IntelliWind initiative’s vision of intelligent, autonomous wind power plant operations.
​
Supervisors
Nikolay Dimitrov
Athanasios Kolios
Background
I was born in Fragoso, a small village in northern Portugal. For the past six years, I’ve lived in Porto where I completed both my Bachelor’s and Master’s degrees in Electrical and Computer Engineering, specializing in Automation.
During my Master’s, I joined the DIGI2 Lab as a Research Fellow, where I developed my dissertation in the field of Predictive Maintenance. After that, I continued pursuing my passion for innovation at INEGI, an R&D institute, developing Predictive Maintenance solutions for industrial partners.
Outside of engineering, I have a keen interest in geopolitics, history, and handcrafted beer - a fantastic trio for a pleasant afternoon.
I also enjoy playing volleyball with friends (even though I’m terrible at it!) and staying active.
Motivation
Witnessing Europe’s recent energy crisis reinforced my belief that advancing renewable energy technologies is not just a technical challenge, but a societal imperative.
I want to contribute to a future where energy independence and sustainability go hand in hand. This PhD project aligns perfectly with that vision by aiming at the development of autonomous decision support systems that can not only detect faults before they occur but also potentially extend the service life of wind turbines components, making wind energy even more reliable and sustainable.
With my background in automation and predictive maintenance, I am eager to apply my knowledge to strengthen Europe’s green economy and help accelerate the transition toward a resilient, carbon-neutral energy system.
