PV degradation methodology comparison — A basis for a standard

What is the best method to determine long-term PV system performance and degradation rates? Ideally, one universally applicable methodology would be desirable so that a single number could be derived. However, data sets vary in their attributes and evidence is presented that defining two methodologies may be preferable. Monte Carlo simulations of artificial performance data allowed investigation of different methodologies and their respective confidence intervals. Tradeoffs between different approaches were delineated, elucidating as to why two separate approaches may need to be included in a standard. Regression approaches tend to be preferable when data sets are less contaminated by seasonality, noise and occurrence of outliers although robust regression can significantly improve the accuracy when outliers are present. In the presence of outliers, marked seasonality, or strong soiling events, year-on-year approaches tend to outperform regression approaches.

[1]  S. Kurtz,et al.  Analytical improvements in PV degradation rate determination , 2010, 2010 35th IEEE Photovoltaic Specialists Conference.

[2]  G. Destouni,et al.  Renewable Energy , 2010, AMBIO.

[3]  Dirk C. Jordan,et al.  The Dark Horse of Evaluating Long-Term Field Performance—Data Filtering , 2014, IEEE Journal of Photovoltaics.

[4]  Dirk Jordan,et al.  Compendium of photovoltaic degradation rates , 2016 .

[5]  George Makrides,et al.  Definition and Computation of the Degradation Rates of Photovoltaic Systems of Different Technologies With Robust Principal Component Analysis , 2015, IEEE Journal of Photovoltaics.

[6]  Selection of best methods to calculate degradation rates of PV modules , 2015, 2015 IEEE 42nd Photovoltaic Specialist Conference (PVSC).

[7]  B. Ripley,et al.  Robust Statistics , 2018, Encyclopedia of Mathematical Geosciences.

[9]  Dirk C. Jordan,et al.  PV degradation curves: non‐linearities and failure modes , 2017 .

[10]  Theresa L. Utlaut,et al.  Introduction to Time Series Analysis and Forecasting , 2008 .

[11]  Mike Anderson,et al.  Validation of the PVLife model using 3 million module-years of live site data , 2013, 2013 IEEE 39th Photovoltaic Specialists Conference (PVSC).

[12]  G. Makrides,et al.  Analysis of photovoltaic system performance time series: Seasonality and performance loss , 2015 .

[13]  George Makrides,et al.  Review of photovoltaic degradation rate methodologies , 2014 .

[14]  Thomas Janoski,et al.  Introduction to Time-Series Analysis , 1994 .