Clear-sky detection for PV degradation analysis using multiple regression

Jordan, Dirk; Hansen, Clifford W.

doi:10.1016/j.renene.2023.04.035

Cited by 4 publications

(2 citation statements)

References 14 publications

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“…Periodic calibrations and maintenance are recommended for ground-based sensors. When ground-based measurements are unreliable, satellite data in combination with clear-sky filtering could be leveraged to avoid any bias due to irradiance sensor drifting [32] (although the uncertainty of the satellite data must be considered as well); this is being incorporated in RdTools. Nighttime "irradiance" measurements can provide useful information on the calibration of a particular sensor.…”

Section: Irradiance Sensor Driftmentioning

confidence: 99%

“…The relatively simple clear-sky method could be improved by either replacing it with a different method (e.g., using satellite data with clear-sky filtering [32] ), or by applying some siteadaptation techniques to remove bias. The existing clear-sky method appears to be dependent on climate and fails completely at locations characterized by more dynamic conditions.…”

Section: Rdtools Potential Improvement Recommendationsmentioning

confidence: 99%

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How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Theristis,

Anderson,

Ascencio-Vasquez

et al. 2023

Solar RRL

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Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

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Section: Irradiance Sensor Driftmentioning

confidence: 99%

Section: Rdtools Potential Improvement Recommendationsmentioning

confidence: 99%

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

Theristis,

Anderson,

Ascencio-Vasquez

et al. 2023

Solar RRL

View full text Add to dashboard Cite

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Extreme Weather and PV Performance

Jordan,

Perry,

White

et al. 2023

IEEE J. Photovoltaics

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The impact of extreme weather events on photovoltaic (PV) performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the National Renewable Energy Laboratory's PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1% of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60% loss, showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events above 90 km/h, by comparing PLR before and after these events. Similarly, hail events caused higher PLR for hail sizes of and above 25 mm indicating that more stringent hail testing for PV modules is required. In addition, very high snow loads may also cause increased PLRs, but more data are required to better quantify the impact. These findings illustrate the substantial risk extreme weather events pose short-and long-term to fielded PV installations.

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