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https://doi.org/10.21256/zhaw-26001| Publication type: | Conference paper |
| Type of review: | Peer review (publication) |
| Title: | Physics informed deep learning for tracker fault detection in photovoltaic power plants |
| Authors: | Zgraggen, Jannik Guo, Yuyan Notaristefano, Antonio Goren Huber, Lilach |
| et. al: | No |
| DOI: | 10.36001/phmconf.2022.v14i1.3235 10.21256/zhaw-26001 |
| Proceedings: | Proceedings of the Annual Conference of the PHM Society 2022 |
| Editors of the parent work: | Kulkarni, Chetan Saxena, Abhinav |
| Volume(Issue): | 14 |
| Issue: | 1 |
| Conference details: | 14th Annual Conference of the Prognostics and Health Management Society, Nashville, USA, 1-4 November 2022 |
| Issue Date: | 28-Oct-2022 |
| Publisher / Ed. Institution: | PHM Society |
| Language: | English |
| Subjects: | Predictive maintenance; Deep learning; Anomalieerkennung; Solarkraftanlage; Physics informed DL; Condition based maintenance; Machine learning |
| Subject (DDC): | 006: Special computer methods 620: Engineering |
| Abstract: | One of the main challenges for fault detection in commercial fleets of machines is the lack of annotated data from the faulty condition. The use of supervised algorithms for anomaly detection or fault diagnosis is often unrealistic in this case. One approach to overcome this challenge is to augment the available normal data by generating synthetic anomalous data that represents faulty conditions. In this paper we apply this approach to the detection of faults in the tracking system of solar panels in utility-scale photovoltaic (PV) power plants. We develop a physical model in order to augment the training data for a deep convolutional neural network. We show that the physics informed learning algorithm is capable of detecting faults in an accurate and robust manner under diverse weather conditions, outperforming a purely data-driven approach. Developing and testing the algorithm with real operational data ensures its efficient deployment for PV power plants that are monitored at string level. This in turn enables the early detection of root causes for power losses, thereby contributing to the accelerated adoption of solar energy at utility scale. |
| URI: | https://digitalcollection.zhaw.ch/handle/11475/26001 |
| Fulltext version: | Published version |
| License (according to publishing contract): | CC BY 3.0: Attribution 3.0 Unported |
| Departement: | School of Engineering |
| Organisational Unit: | Institute of Data Analysis and Process Design (IDP) |
| Published as part of the ZHAW project: | Intelligente Diagnostik von Leistungseinbussen in Solarkraftwerken |
| Appears in collections: | Publikationen School of Engineering |
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| File | Description | Size | Format | |
|---|---|---|---|---|
| 2022_Zgraggen-etal_Deep-learning-tracker-fault.detection.pdf | 173.42 kB | Adobe PDF |  View/Open |
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Zgraggen, J., Guo, Y., Notaristefano, A., & Goren Huber, L. (2022). Physics informed deep learning for tracker fault detection in photovoltaic power plants [Conference paper]. In C. Kulkarni & A. Saxena (Eds.), Proceedings of the Annual Conference of the PHM Society 2022 (Vol. 14, Issue 1). PHM Society. https://doi.org/10.36001/phmconf.2022.v14i1.3235
Zgraggen, J. et al. (2022) ‘Physics informed deep learning for tracker fault detection in photovoltaic power plants’, in C. Kulkarni and A. Saxena (eds) Proceedings of the Annual Conference of the PHM Society 2022. PHM Society. Available at: https://doi.org/10.36001/phmconf.2022.v14i1.3235.
J. Zgraggen, Y. Guo, A. Notaristefano, and L. Goren Huber, “Physics informed deep learning for tracker fault detection in photovoltaic power plants,” in Proceedings of the Annual Conference of the PHM Society 2022, Oct. 2022, vol. 14, no. 1. doi: 10.36001/phmconf.2022.v14i1.3235.
ZGRAGGEN, Jannik, Yuyan GUO, Antonio NOTARISTEFANO und Lilach GOREN HUBER, 2022. Physics informed deep learning for tracker fault detection in photovoltaic power plants. In: Chetan KULKARNI und Abhinav SAXENA (Hrsg.), Proceedings of the Annual Conference of the PHM Society 2022. Conference paper. PHM Society. 28 Oktober 2022
Zgraggen, Jannik, Yuyan Guo, Antonio Notaristefano, and Lilach Goren Huber. 2022. “Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants.” Conference paper. In Proceedings of the Annual Conference of the PHM Society 2022, edited by Chetan Kulkarni and Abhinav Saxena. Vol. 14. PHM Society. https://doi.org/10.36001/phmconf.2022.v14i1.3235.
Zgraggen, Jannik, et al. “Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants.” Proceedings of the Annual Conference of the PHM Society 2022, edited by Chetan Kulkarni and Abhinav Saxena, vol. 14, no. 1, PHM Society, 2022, https://doi.org/10.36001/phmconf.2022.v14i1.3235.
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