Data Analytics for Predictive Maintenance in PV and Wind Power Systems

  • Typ:Masterarbeit
  • Betreuung:

    Omar Mostafa

    Prof. Sanja Lazarova-Molnar


Problem: With increasing reliance on renewable energy resources, ensuring high reliability of these critical assets becomes essential to avoid unexpected failures and downtime. However, more research is needed to understand and optimize maintenance decisions for these systems. By predicting and preventing equipment failures, we can improve maintenance schedules and reduce downtime, ultimately increasing the reliability of renewable energy systems.

Goal: Implement and evaluate data analytics methods for predictive maintenance of PV and wind power systems. The focus will be on reviewing predictive maintenance methods for PV and wind systems from the literature, such as Markov models and machine learning techniques, followed by implementing these methods using publicly available data. The final step will be to evaluate the performance of the implemented models.   

Required Skills and Knowledge:

  • Modelling and simulation.
  • Data analytics.
  • Basic knowledge in machine learning.
  • Basic understanding of PV and wind power systems.
  • Knowledge in python programming language.