Data Scarcity in Fault Detection for Solar Tracking Systems: the Power of Physics-Informed Artificial Intelligence

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Published Jun 27, 2024
Mila Francesca Lüscher Jannik Zgraggen Yuyan Guo Antonio Notaristefano Lilach Goren Huber

Abstract

Combining physical and domain knowledge in artificial intelligence (AI) models has been gaining attention in various fields and applications.
Applications in machine prognostics and health management (PHM) are natural candidates for such hybrid approaches. In particular, they can be efficiently exploited for high fidelity anomaly detection in technical and industrial systems. A natural way for hybridization is using physical models to generate representative data for the training of AI models. Depending on the level of domain knowledge availability, data augmentation can compensate for scarcity of real data from the field. This is particularly attractive for anomaly detection tasks, in which data from the abnormal regimes is limited by definition. On top of this inherent data limitation, many real-world systems suffer from data limitations even within the normal regimes.
In this paper we propose a physics-informed deep learning algorithm for fault detection in grid scale photovoltaic power plants. We focus on a common data scarce scenario that emerges from a low asset monitoring granularity: instead of monitoring the power production of each solar string, the power output is monitored only at combiner-box or even inverter level (monitoring a large number of strings with a single sensor). As a result, the signatures of single local faults can become very subtle and challenging to detect. We show that in this case a physics-informed AI approach significantly outperforms the alternative of a purely data-driven anomaly detection model. This enables high fidelity fault detection in larger solar power plants, that are often limited in the granularity of their condition monitoring data.

How to Cite

Lüscher, M. F., Zgraggen, J., Guo, Y., Notaristefano, A., & Goren Huber, L. (2024). Data Scarcity in Fault Detection for Solar Tracking Systems: the Power of Physics-Informed Artificial Intelligence. PHM Society European Conference, 8(1), 8. https://doi.org/10.36001/phme.2024.v8i1.4059
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Keywords

physics informed deep learning, anomaly detection, fault detection, artificial intelligence, solar power plant, data scarcity, data augmentation

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Technical Papers