Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants



Published Oct 28, 2022
Jannik Zgraggen Yuyan Guo Antonio Notaristefano Lilach Goren Huber


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.

How to Cite

Zgraggen, J., Guo, Y., Notaristefano, A., & Goren Huber, L. . (2022). Physics Informed Deep Learning for Tracker Fault Detection in Photovoltaic Power Plants. Annual Conference of the PHM Society, 14(1).
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physics informed neural network, photovoltaic, pv, Fault detection, anomaly detection, deep learning, siamese network, distance learning, predictive maintenance

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