Service Lifetime Prediction of PV Modules and Systems: Progress of the SOLAR-TRAIN Project

Kaaya, Ismail; Betts, Thomas R.; Lindig, Sascha; Hrelja, N.; Hernández, Gómez; Mariottini, F.; Moser, David; Weiß, Karl-Anders; Iseghem, M. Van; Chiantore, P.

doi:10.1109/pvsc40753.2019.8980791

2019 IEEE 46th Photovoltaic Specialists Conference (PVSC) 2019

DOI: 10.1109/pvsc40753.2019.8980791

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Service Lifetime Prediction of PV Modules and Systems: Progress of the SOLAR-TRAIN Project

Ismail Kaaya

Thomas R. Betts

Sascha Lindig

et al.

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2020

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“…upper limit for irradiance was set and fixed at 1200 W/m 2 , and the lower limit was chosen flexible between 600-900 W/m 2 , depending on the location under evaluation. This clear sky filter is used as a way to separate low performances due to low irradiance from degradation and also due to its consistence in degradation analysis based on our previous work,21 where different filters are compared. The filtered power is then corrected to STC of irradiance by applying a linear correction of power to 1000 W/m 2 irradiance.…”

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Photovoltaic lifetime forecast model based on degradation patterns

Kaaya

Lindig

Weiß

et al. 2020

Progress in Photovoltaics

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The ever‐growing secondary market of photovoltaic (PV) systems (i.e., the transaction of solar plants ownership) calls for reliable and high‐quality long‐term PV degradation forecasts to mitigate the financial risks. However, when long‐term PV performance degradation forecasts are required after a short time with limited degradation history, the existing physical and data‐driven methods often provide unrealistic degradation scenarios. Therefore, we present a new data‐driven method to forecast PV lifetime after a small performance degradation of only 3%. To achieve an accurate and reliable forecast, the developed method addresses the fundamental challenges that usually affect long‐term degradation evaluation such as data treatment, choosing a good degradation model, and understanding the different degradation patterns. In the paper, we propose and describe an algorithm for degradation trend evaluation, a new concept of multiple “time‐ and degradation pattern‐dependent” degradation factors. The proposed method has been calibrated and validated using different PV modules and systems data of 5 to 35 years of field exposure. The model has been benchmarked against existing statistical models evaluating 11 experimental PV systems with different technologies. The key advantage of our model over statistical ones is the ability to perform more reliable forecasts with limited degradation history. With an average relative uncertainty of 7.0%, our model is outstanding in consistency for different forecasting time horizons. Moreover, the model is applicable to all PV technologies. The proposed method will aid in making reliable financial decisions and also in adequately planning operation and maintenance activities.

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confidence: 99%

Photovoltaic lifetime forecast model based on degradation patterns

Kaaya

Lindig

Weiß

et al. 2020

Progress in Photovoltaics

View full text Add to dashboard Cite

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Service Lifetime Prediction of PV Modules and Systems: Progress of the SOLAR-TRAIN Project

Cited by 1 publication

References 9 publications

Photovoltaic lifetime forecast model based on degradation patterns

Photovoltaic lifetime forecast model based on degradation patterns

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