Robust and Fast Detection of Small Power Losses in Large-Scale PV Systems

Due to the fast growth in global installed photovoltaic (PV) capacity, performance monitoring for large-scale PV systems is an increasingly relevant and important topic. A large volume of research exists in this field, but there is a need for comparison of different methods and their performance tow...

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Bibliographic Details
Published in:IEEE journal of photovoltaics 2021-05, Vol.11 (3), p.819-826
Main Authors: Skomedal, Asmund F., Ogaard, Mari B., Haug, Halvard, Marstein, Erik Stensrud
Format: Article
Language:English
Subjects:
Circuit faults
Control charts
Current measurement
CUSUM charts
Data analysis
Data integrity
Electric power generation
Fault detection
Inverters
Mathematical analysis
Monitoring
Photovoltaic cells
photovoltaics (PV)
PV systems
Robustness
Seasonal variations
solar power generation
Strings
time series analysis
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Summary:Due to the fast growth in global installed photovoltaic (PV) capacity, performance monitoring for large-scale PV systems is an increasingly relevant and important topic. A large volume of research exists in this field, but there is a need for comparison of different methods and their performance toward relevant metrics, a broad discussion of the different choices involved, and subsequent consolidation. In this article, we focus on the detection of small power losses on string-level. We discuss the different choices involved in building a robust string performance monitoring scheme. We suggest the following approach: 1) identify bad data quality and do data-filtering; 2) calculate the daily specific yield on string level; 3) calculate the relative difference in specific yield between the strings (relative yield); 4) identify historical faults; 5) correct for seasonal variations; and 6) apply control charts to detect performance losses in new data and issue alarms/report to the system operators. Based on data from a utility scale PV power plant we compare different control charts in terms of detection time and sensitivity. We show that the cumulative sum (CUSUM) median and the Tukey-CUSUM charts are the most promising fault detection methods of the ones we have tested. We can robustly detect faults causing a performance loss of about 1% within 35 days of the drop in performance.
ISSN:2156-3381
2156-3403
DOI:10.1109/JPHOTOV.2021.3060732