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Failure Diagnosis and Trend-Based Performance Losses Routines for the Detection and Classification of Incidents in Large-Scale Photovoltaic Systems
A. Livera, M. Theristis, J.S. Stein, G.E. Georghiou
Photovoltaic (PV), Failure Diagnosis, Performance Loss, Data Quality, Change-Point Techniques
PV Systems – Modelling, Design, Operation and Performance
Subtopic: Operation, Performance and Maintenance of PV Systems
Event: 38th European Photovoltaic Solar Energy Conference and Exhibition
Session: 5CO.9.3
973 - 978
ISBN: 3-936338-78-7
Paper DOI: 10.4229/EUPVSEC20212021-5CO.9.3
0,00 EUR
Document(s): paper, presentation


Failure diagnosis (detection and classification) in photovoltaic (PV) systems through data diagnostic algorithms is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating PV systems. The scope of this work is to present the development of Failure Diagnosis Routines (FDRs) and Trend-based performance Losses Routines (TLRs) for diagnosing PV underperformance issues due to failure occurrences and performance loss events. The proposed routines complement the developed Data Quality Routines (DQRs) and operate exclusively on recorded electrical and meteorological measurements. The proposed routines were experimentally validated on a large-scale PV system installed in Larissa, Greece. The results demonstrated the effectiveness of the routines for detecting system underperformance issues and accurately classifying the detected issues into zero power production incidents, degradation, soiling and snow losses. The failure detection stage of the FDRs achieved a detection accuracy of 97.3% for zero power production incidents during daylight hours. A precision accuracy of 96.32% was obtained by the FDRs when classifying zero power production due to fault incidents, while the TLRs achieved 91.66% classification accuracy.