Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

Livera, Andreas; Paphitis, George; Theristis, Marios; Lopez-Lorente, Javier; Makrides, George; Georghiou, George

doi:10.3390/solar2010006

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

Journal Article · Tue Mar 15 00:00:00 EDT 2022 · Solar

DOI:https://doi.org/10.3390/solar2010006· OSTI ID:1855530

Livera, Andreas; ; ; ; ;

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

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Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE)

OSTI ID:: 1855530

Alternate ID(s):: OSTI ID: 1870471

Journal Information:: Solar, Journal Name: Solar Journal Issue: 1 Vol. 2; ISSN 2673-9941

Publisher:: MDPI AGCopyright Statement

Country of Publication:: Switzerland

Language:: English

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Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

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Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

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