|Title||Development of a generation model of a photovoltaic power plant|
Development of a generation model of a photovoltaic power plant
Bachelor Thesis in Computing Science
The expected service life of photovoltaic systems and their components significantly determines their economic efficiency and is thus a critical factor for their use. The sudden failure of a high-power inverter usually causes significant disruptions in the connected distribution grid, e.g., due to frequency disturbances.
These inverters are fed, for example, by large photovoltaic systems that generate electricity from solar energy. In order to simulate the behaviour of inverters, realistic input parameters are required. Thus, the current and voltage applied to the inverter are determined by the connected photovoltaic modules. Their performance, in turn, depends on the weather.
|Home institute||Department of Computing Science|
|Art der Arbeit||praktisch / anwendungsbezogen|
|Author||Jelke Wibbeke, M. Sc.|
In order to be able to simulate inverters with realistic performance profiles, a model is to be developed which uses weather data to generate generation time-series data of photovoltaic power plants. The goal is to calculate the resulting voltage, current, and power from solar radiation, temperature, humidity, and module parameters.
 N. Al-Messabi, Yun Li, I. El-Amin, and C. Goh, “Forecasting of photovoltaic power yield using dynamic neural networks,” in The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia, Jun. 2012, pp. 1–5, doi: 10.1109/IJCNN.2012.6252406.
 M. De Benedetti, F. Leonardi, F. Messina, C. Santoro, and A. Vasilakos, “Anomaly detection and predictive maintenance for photovoltaic systems,” Neurocomputing, vol. 310, pp. 59–68, Oct. 2018, doi: 10.1016/j.neucom.2018.05.017.
 A. Mellit, S. Sağlam, and S. A. Kalogirou, “Artificial neural network-based model for estimating the produced power of a photovoltaic module,” Renew. Energy, vol. 60, pp. 71–78, Dec. 2013, doi: 10.1016/j.renene.2013.04.011.
The thesis aims at students of computing science, physics, or comparable courses of study who are interested in energy informatics. Previous knowledge in the area of photovoltaics and Python programming is desirable, but not mandatory. The applicant should be willing to deal with the new topic in a motivated and independent way.