Engineering
Work description
1) Model and validate Digital Twins (DT) of power generation plants for the creation of a fault database. The models will be based on the technical specifications of each plant and validated using SCADA system data. 2) Develop machine learning (ML) algorithms for fault diagnosis and malfunctions in power generation plants, using SCADA system data combined with synthetic data from Digital Twins (DT). 3) Develop a recommendation system that will support operation and maintenance (O&M) teams by providing strategic information on the current status of various equipment in the power generation plant. 4) Validate the developed methodologies with real data and different use cases focused on the energy transition. 5) Disseminate the work in international journals and/or conferences.
Academic Qualifications
PhD degree in: Electrical and Computer Engineering; Electrical Engineering; Energy, Applied Mathematics; Physics; Computer Science; Industrial Engineering or similar.
Minimum profile required
- Past experience with system modelling (e.g., Modelica, MATLAB Simulink)- Python programming skills- Academic knowledge of energy systems and/or renewable energy- A minimum of 2 publications in Q1 journals
Preference factors
- Experience in the development of Digital Twins (DT) - Experience in applying machine learning (ML) algorithms to engineering problems - Knowledge or experience of energy systems problems
Application Period
Since 27 Jun 2024 to 27 Jul 2024
Centre
Power and Energy Systems