Intelligent Diagnostics of Performance Degradation in Solar Power Plants
At a glance
- Project leader : Dr. Lilach Goren Huber
- Deputy of project leader : Dr. Gianmarco Pizza
- Project team : Jannik Zgraggen
- Project status : ongoing
- Funding partner : Innosuisse (Innovationsprojekt / Projekt Nr. 55018.1 IP-ICT)
- Project partner : Nispera AG
- Contact person : Lilach Goren Huber
Description
A new software module for intelligent performance analytics and fault diagnosis for photovoltaic power plants will be developed and integrated in the existing Nispera platform. The service includes diagnosing prominent under-performance factors, allowing for a cost-effective maintenance planning.
Publications
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Zgraggen, Jannik; Guo, Yuyan; Notaristefano, Antonio; Goren Huber, Lilach,
2022.
Physics informed deep learning for tracker fault detection in photovoltaic power plants [paper].
In:
Kulkarni, Chetan; Saxena, Abhinav, eds.,
Proceedings of the Annual Conference of the PHM Society 2022.
14th Annual Conference of the Prognostics and Health Management Society, Nashville, USA, 1-4 November 2022.
PHM Society.
Available from: https://doi.org/10.36001/phmconf.2022.v14i1.3235
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Goren Huber, Lilach; Notaristefano, Antonio,
2022.
Predictive Maintenance mit Physics-Informed-Deep-Learning : Anwendungsfall Photovoltaikanlagen.
fmpro service.
2022(3), pp. 24-25.
Available from: https://doi.org/10.21256/zhaw-25292