Afia Azam

In wind energy facilities, changes in wind speed can negatively impact power quality and the stability of the grid. My project aims to create self-managing intelligent systems for autonomous operation of modular wind power plants so that they can offer primary frequency response ancillary services to the grid. This comprises applying advanced control techniques to mitigate the wind-driven disturbances and data science for accurate forecasting. The major difficulty is integrating these systems with HVDC grids, which are fundamentally different from AC grids and are less common in Europe. The purpose is to advance climate-neutral objectives by enhancing the reliability and economic efficiency of offshore wind power transmission systems. 

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Supervisors

Prof. Estefanía Artigao Andicoberry 

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Professionally, I have worked in R&D at Bridgestone, gaining industry experience in innovation and technology development. Academically, I have conducted research on sustainable marine fuels and offshore hydrogen production, with the marine fuels project carried out during an Erasmus exchange program at Universidad Politécnica de Madrid.

Motivation

I am highly motivated to pursue a PhD in Industrial Engineering, specifically in the field of Intelligent Systems for Autonomous Wind Power Plant Operations. This research area offers an exciting opportunity to contribute to the evolving renewable energy sector. The integration of autonomous systems into wind power plants presents a chance to enhance efficiency and sustainability. My previous work, including my research on sustainable marine fuels and offshore energy systems, has prepared me to approach this challenge with a multidisciplinary mindset. I am eager to explore the potential of intelligent systems in optimizing wind energy operations and look forward to contributing to this innovative field.