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.

Highlights

  • R ECENT studies show that worldwide installed photovoltaic (PV) capacity reached 580.16 GW by the end of 2019 [1]

  • The most widely used approaches include the year-on-year model, developed by Sunpower [12] and further adapted by NREL [8], and PV time series decomposition followed by a linear regression

  • The results of an international benchmarking exercise of linear performance loss rate calculation methodologies suggest that averaging across multiple calculation methods provides the most consistent results across different PV systems [15]

Read more

Summary

INTRODUCTION

R ECENT studies show that worldwide installed photovoltaic (PV) capacity reached 580.16 GW by the end of 2019 [1]. When assessing the PLR of PV systems, a linear performance behavior is assumed, and many current studies primarily apply this approach [6]–[9]. Possible common performance evolution patterns depending on certain parameters such as the chosen PV technology, the prevailing climate, the mounting type or the year of installation are more isolated and, detectable. This knowledge might be very beneficial for optimizing PV systems under varying conditions. The determination of breakpoints in the data offers insight into the piecewise evolution of PV system performance, with breakpoints possibly indicating changes in the dominant failure mode This information can aid investigations that aim to identify specific failure modes.

BACKGROUND
TEST SITE
METHODOLOGY
Input Data Assessment and Data Treatment
Model Application
Result Evaluation
MS-PL Algorithm Applied to Validation Systems
Findings
SUMMARY AND OUTLOOK
Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call