I arrived from Iran with a strong passion for wind energy.
From the very first time I studied turbomachines during my bachelor’s degree, I was captivated by wind turbines: their elegance, their complexity, and the challenge of making them more reliable. They truly are a remarkable feat of engineering.
After completing my bachelor’s degree, my curiosity and motivation pushed me to deepen my knowledge in this field. I enrolled in several online courses and worked hard to be admitted to one of Iran’s most prestigious universities, Tarbiat Modares University, where I pursued a Master’s degree in Renewable Energy Engineering.
During my master’s studies, I fully dedicated myself to research. My work focused on optimising vertical-axis wind turbines by combining computational fluid dynamics with machine learning techniques. I also explored data-driven optimisation methods, such as Auto ML-GA, to improve turbine performance. This effort led to tangible results: I graduated as the top-ranked student of my cohort with a GPA of 18.86 out of 20 and published three research papers. Alongside my academic work, I continued strengthening my background through online courses offered by leading universities in the field.
Despite completing my master’s degree, my desire to learn only grew stronger. The field of wind energy felt like a vast ocean, and my knowledge just a single drop. I have always been particularly passionate about applying machine learning to engineering problems—an interest rooted in an early crossroads in my academic path, when I hesitated between mechanical engineering and computer engineering. Wind energy ultimately became the perfect intersection of both worlds.
This journey brought me to Zaragoza, a city surrounded by wind turbines, and to the TWEED project, where world-leading researchers and professionals share a common ambition: advancing the digitalisation of wind energy. As a Doctoral Candidate (DC8) in TWEED at the University of Zaragoza, my research focuses on the predictive maintenance of wind turbines, using explainable machine learning methods to anticipate failures and optimise wind farm operation and maintenance strategies.
Working under the supervision of Prof. Julio Melero, and in close collaboration with TU Delft and ANNEA, I am integrating my previous experience with cutting-edge research and long-term objectives to improve the reliability, efficiency, and sustainability of wind energy systems.
In the end, I am Hamidreza Mirzaeian—a researcher driven by curiosity, resilience, and the ambition to contribute to a cleaner, smarter, and more reliable wind energy future.
This post is part of the “TWEED Researcher Stories” series, where we introduce the doctoral researchers shaping the future of wind energy digitalisation within the Marie Skłodowska-Curie Doctoral Network TWEED.
