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Title: A methodology to analyze photovoltaic tracker uptime

Abstract

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.

Authors:
 [1]; ORCiD logo [1]
  1. National Renewable Energy Laboratory, Golden CO 80401 USA
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1436571
Report Number(s):
NREL/JA-5J00-71486
Journal ID: ISSN 1062-7995
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Progress in Photovoltaics
Additional Journal Information:
Journal Volume: 26; Journal Issue: 7; Journal ID: ISSN 1062-7995
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; 47 OTHER INSTRUMENTATION; clear-sky modeling; photovoltaic performance; tracking

Citation Formats

Ruth, Dan, and Muller, Matthew. A methodology to analyze photovoltaic tracker uptime. United States: N. p., 2018. Web. doi:10.1002/pip.3002.
Ruth, Dan, & Muller, Matthew. A methodology to analyze photovoltaic tracker uptime. United States. doi:10.1002/pip.3002.
Ruth, Dan, and Muller, Matthew. Tue . "A methodology to analyze photovoltaic tracker uptime". United States. doi:10.1002/pip.3002.
@article{osti_1436571,
title = {A methodology to analyze photovoltaic tracker uptime},
author = {Ruth, Dan and Muller, Matthew},
abstractNote = {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.},
doi = {10.1002/pip.3002},
journal = {Progress in Photovoltaics},
issn = {1062-7995},
number = 7,
volume = 26,
place = {United States},
year = {2018},
month = {4}
}

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