A Methodology to Analyze Photovoltaic Tracker Uptime

Dan Ruth, Matthew Muller

Research output: Contribution to journalArticlepeer-review

1 Scopus Citations


A metric is developed to analyze the daily performance of single-axis photovoltaic (PV) trackers. The metric relies on comparing correlations between the daily time series of the PV power output and an array of simulated plane-of-array irradiances for the given day. Mathematical thresholds and a logic sequence are presented, so the daily tracking metric can be applied in an automated fashion on large-scale PV systems. The results of applying the metric are visually examined against the time series of the power output data for a large number of days and for various systems. The visual inspection results suggest that overall, the algorithm is accurate in identifying stuck or functioning trackers on clear-sky days. Visual inspection also shows that there are days that are not classified by the metric where the power output data may be sufficient to identify a stuck tracker. Based on the daily tracking metric, uptime results are calculated for 83 different inverters at 34 PV sites. The mean tracker uptime is calculated at 99% based on 2 different calculation methods. The daily tracking metric clearly has limitations, but as there is no existing metrics in the literature, it provides a valuable tool for flagging stuck trackers.

Original languageAmerican English
Pages (from-to)491-501
Number of pages11
JournalProgress in Photovoltaics: Research and Applications
Issue number7
StatePublished - 2018

Bibliographical note

Publisher Copyright:
Copyright © 2018 John Wiley & Sons, Ltd.

NREL Publication Number

  • NREL/JA-5J00-71486


  • clear-sky modeling
  • photovoltaic performance
  • tracking


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