PVplr: R Package Implementation of Multiple Filters and Algorithms for Time-series Performance Loss Rate Analysis

Curran, Alan J.; Burleyson, Tyler L.; Rath, Kunal; Xin, Arthur S.; Lindig, Sascha; Moser, David; Stein, Joshua S.; Bruckman, Laura S.; French, Roger H.

doi:10.1109/pvsc45281.2020.9300807

Cited by 5 publications

(6 citation statements)

References 6 publications

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“…PVplr (Curran et al, 2020a;Curran et al, 2020b) is an R package (R Core Team, 2020) that allowed for the calculation of PLR with several different methods and parameters, as opposed to one universal method. This package makes it easy to test different filtering criteria and models to view the impact on a final PLR calculation.…”

Section: Pvplr: Performance Loss Rate Analysismentioning

confidence: 99%

Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries

Curran

Liu

et al. 2023

Front. Energy Res.

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We have studied the degradation of both full-sized modules and minimodules with PERC and Al-BSF cell variations in fields while considering packaging strategies. We demonstrate the implementations of data-driven tools to analyze large numbers of modules and volumes of timeseries data to obtain the performance loss and degradation pathways. This data analysis pipeline enables quantitative comparison and ranking of module variations, as well as mapping and deeper understanding of degradation mechanisms. The best performing module is a half-cell PERC, which shows a performance loss rate (PLR) of −0.27 ± 0.12% per annum (%/a) after initial losses have stabilized. Minimodule studies showed inconsistent performance rankings due to significant power loss contributions via series resistance, however, recombination losses remained stable. Overall, PERC cell variations outperform or are not distinguishable from Al-BSF cell variations.

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Section: Pvplr: Performance Loss Rate Analysismentioning

confidence: 99%

Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries

Curran

Liu

et al. 2023

Front. Energy Res.

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“…Thereby, a number of methods (as presented here) is used to calculate individual PLR values, divide the results into statistically similar approaches and outliers, and then calculate the mean over the inlier values to receive a ensemble PLR. The R package PVplr [27] has been developed to follow this calculation approach.…”

Section: Commonly Used Pipelinesmentioning

confidence: 99%

“…During such events, the power conversion is typically lower compared to 'standard' operating conditions. Therefore, the most reliable strategy for removing such instances is by using statistical performance filters or clustering approaches of power and irradiance trends [4,27]. Although it is common to remove such instances as they are usually time limited and do not represent actual PV system performance, caution is recommended when large periods of such effects exist; excessive filtering may introduce significant bias in the estimated PLR [36].…”

Section: Shading Soiling and Snowmentioning

confidence: 99%

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Best practices for photovoltaic performance loss rate calculations

2022

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The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simpliﬁes the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across diﬀerent types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using diﬀerent pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for diﬀerentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.

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“…The second library, named “ PVplr ”, was developed in R and it was built as part of the International Energy Agency (IEA) PV Power Systems Programme Task 13 study on the determination and uncertainty of PLR calculations. [ 21 ] However, due to the aforementioned challenges of different pipelines for data processing, normalization, corrections, aggregation, statistical methods, and undefined “adequate length of timeseries”, there is no proven methodology that would enable a universally standardized procedure for estimating the PLR. For example, the state‐of‐the‐art library RdTools , which serves as the “standardized” procedure for PLR estimation, has only been experimentally verified (i.e., compared to discrete indoor measurements) against a handful of systems, with measurements mainly in Colorado.…”

Section: Introductionmentioning

confidence: 99%

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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PVplr: R Package Implementation of Multiple Filters and Algorithms for Time-series Performance Loss Rate Analysis

Cited by 5 publications

References 6 publications

Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries

Field studies of PERC and Al-BSF PV module performance loss using power and I-V timeseries

Best practices for photovoltaic performance loss rate calculations

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

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