Comparative Analysis of Change-Point Techniques for Nonlinear Photovoltaic Performance Degradation Rate Estimations

Abstract

A linear performance drop is generally assumed during the photovoltaic (PV) lifetime. However, operational data demonstrate that the PV module degradation rate (Rd) is often nonlinear, which, if neglected, may increase the financial uncertainty. Although nonlinear behavior has been the subject of numerous publications, it was only recently that statistical models able to detect change-points and extract multiple Rd values from PV performance time-series were introduced. A comparative analysis of six open-source libraries, which can detect change-points and calculate nonlinear Rd, is presented in this article. Since the real Rd and change-point locations are unknown in field data, 960 synthetic datasets from six locations and two PV module technologies have been generated using different aggregation and normalization decisions and nonlinear degradation rate patterns. The results demonstrated that coarser temporal aggregation (i.e., monthly vs. weekly), temperature correction, and both PV module technologies and climates with lower seasonality can benefit the change-point detection and Rd extraction. This also raises a concern that statistical models typically deployed for Rd analysis may be highly climaticand technology-dependent. The comparative analysis of the six approaches demonstrated median mean absolute errors (MAE) ranging from 0.06 to 0.26%/year, given a maximum absolute Rd of 2.9%/year. The median MAE in change-point position detection varied from 3.5 months to 6 years.

Failure diagnosis and trend‐based performance losses routines for the detection and classification of incidents in large‐scale photovoltaic systems

“…The FBP model was selected due to its ability to decompose the signal, perform CP analysis, and adjust the trend flexibility and its additional functionalities (e.g., forecasting) 58 . This model was also applied to PV performance time series and exhibited low prediction error under different conditions (e.g., two‐ and three‐step degradation profiles, range of PV module technologies seasonality in different climate zones, and different aggregation) 64 …”

“…The changepoint_prior_scale argument can then be adjusted to control the fluctuations. For example, if the value is decreased (e.g., 0.04), 64 an almost linear trend will be forced, which avoids fluctuations due to outliers (e.g., faults), or other temporary effects such as snow and soiling. In this case, the FBP model will be extracting rates of irreversible effects caused by linear/nonlinear degradation.…”

“…58 This model was also applied to PV performance time series and exhibited low prediction error under different conditions (e.g., two-and three-step degradation profiles, range of PV module technologies seasonality in different climate zones, and different aggregation). 64 The FBP algorithm detects the number and location(s) of CPs by capturing statistical changes in the slopes of predefined segments. It initially distributes "potential" CPs uniformly along the selected range of the time series' trend, and it then compares the slopes in order to extract the most significant CPs by performing comparisons against a set TL.…”

Operation and Maintenance Decision Support System for Photovoltaic Systems

“…The S-H-ESD algorithm detects both global and local anomalies by applying Seasonal and Trend decomposition using Loess [43] and robust statistics (i.e., statistical test hypothesis, median based estimation, piecewise approximation) together with Extreme Studentized Deviates (ESD). In parallel, the Facebook prophet (FBP) algorithm was used to identify the number and location(s) of change-point(s) in time series data by capturing linear and complex trends as well as abrupt profile changes [44,45]. The FBP has the capability of differentiating reversible from irreversible mechanisms, extracting the soiling losses and estimating both the performance loss rate (PLR) and degradation rate (RD) of PV systems [41].…”

The Influence of Different Degradation Characteristics on the Greenhouse Gas Emissions of Silicon Photovoltaics: A Threefold Analysis

“…In general, a linear degradation of 0.7% points per year is to be assumed in life-cycle-based approaches to determine the environmental footprint of PV systems [5,6], without accounting for differences between technologies or conditions of installation. However, research has shown that the ageing pattern of PV modules typically follows a non-linear degradation curve [7,8]. Indeed, different factors such as the operating climatic condition (influences like temperature, wind and a corrosive atmosphere) and the type of module technology (glass-glass or glass-backsheet modules, thin-film, or silicon based modules) can lead to different degradation patterns, thereby influencing the economic and environmental performance of PV systems [9].…”