New PV Performance Loss Methodology Applying a Self-Regulated Multistep Algorithm

Abstract

Analyses of performance loss rates in photovoltaic (PV) systems are not yet standardized, and are typically carried out by a linear regression of the evolution of a certain performance metric (e.g., performance ratio, yield, etc.) over time. In this article, we propose a novel methodology of advanced PV system performance loss rate modeling applying a self-regulated multistep algorithm. The developed algorithm automatically detects the number and positions of breakpoints in nonlinear performance time series and divides the performance trend into an adequate number of linear segments. Instead of calculating one linear performance loss rate, given in percentage per year, as is common practice, multiple linear performance loss values are determined, depending on the trend of the time series and subsequently the number of breakpoints. The algorithm is fully automated. We have applied our methodology on data of an experimental PV installation in Bolzano/Italy, which consists of 26 different PV systems. The overall linear performance loss rate of the facility's crystalline silicon systems is between −0.5 and −1.3%/year, whereas the thin-film PV systems experience values between −0.6 and −2.4%/year. Based on our results, the algorithm appears to be stable and accurate. The methodology is to be used as a fast and automated check of PV systems in operation to detect anomalies affecting performance (in an early stage). By building up a large database of detected issues in the field this algorithm will enable us to better understand the performance evolution of different PV system types in varying climates.

Onymous early‐life performance degradation analysis of recent photovoltaic module technologies

“…Rd values tend to converge within ±1%/year after $3-4 years of outdoor exposure. Such nonlinearities raise a concern on the statistical approaches usually used on outdoor field data assuming linearity.Applying methodologies that consider nonlinear Rd behavior may improve accuracies: e.g.,13,[28][29][30] . From a warranty perspective, if degradation modes such as LeTID cause significant early performance loss, there may be a risk of triggering warranty claimsbefore regeneration occurs.…”

Comparative analysis of photovoltaic performance metrics for reliable performance loss rate

“…The proposed approach just requires a measured power as an input. In [16], the authors propose a novel method for modeling PV system performance loss rate (PLR) through a self‐regulated multistep algorithm. The proposed method automatically detects the number and positions of breakpoints in nonlinear performance time series and divides the performance trend into an adequate number of linear segments.…”

“…Data openly available in a public repository that does not issue DOIs. The data that support the findings of this study are openly available in National Solar Radiation DataBase (NSRDB) Data Viewer at https://maps.nrel.gov/ nsrdb-viewer/ , reference number [12], NREL Solar Power Data for Integration Studies at https://www.nrel.gov/grid/ solar-power-data.html , reference number [13], and US DOE-RTC-Baseline dataset at https://osf.io/wn35j/ , reference number [16].…”

“…Regarding the statistical methods, some studies try to understand the sources of uncertainties [9] and the impact of missing data [11]. Although some studies propose new performance metrics and statical methods for better PLRs [12][13][14][15][16][17], a comparison with existing well-known methods is missing.…”

How Climate and Data Quality Impact Photovoltaic Performance Loss Rate Estimations

“…other effects such as nonlinear performance loss or degradation behavior may also be important [7][8][9][10][11][12] and can increase the statistical uncertainty if the time series are fitted with linear models. Blind round-robins have demonstrated wide differences between multiple analysts using the same raw dataset.…”