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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
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.
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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