Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

All freely available plane-of-array (POA) transposition models and photovoltaic (PV) temperature and performance models in pvlib-python and pvpltools-python were examined against multiyear field data from Albuquerque, New Mexico. The data include different PV systems composed of crystalline silicon modules that vary in cell type, module construction, and materials. These systems have been characterized via IEC 61853-1 and 61853-2 testing, and the input data for each model were sourced from these system-specific test results, rather than considering any generic input data (e.g., manufacturer's specification [spec] sheets or generic Panneau Solaire [PAN] files). Six POA transposition models, 7 temperature models, and 12 performance models are included in this comparative analysis. These freely available models were proven effective across many different types of technologies. The POA transposition models exhibited average normalized mean bias errors (NMBEs) within ±3%. Most PV temperature models underestimated temperature exhibiting mean and median residuals ranging from −6.5°C to 2.7°C; all temperature models saw a reduction in root mean square error when using transient assumptions over steady state. The performance models demonstrated similar behavior with a first and third interquartile NMBEs within ±4.2% and an overall average NMBE within ±2.3%. Although differences among models were observed at different times of the day/year, this study shows that the availability of system-specific input data is more important than model selection. For example, using spec sheet or generic PAN file data with a complex PV performance model does not guarantee a better accuracy than a simpler PV performance model that uses system-specific data.

It is commonly assumed that cleaning photovoltaic (PV) modules is unnecessary when the inverter is undersized because clipping will sufficiently mask the soiling losses. Clipping occurs when the inverter's AC size is smaller than the overall modules' DC capacity and leads to the conversion of only part of the PV-generated DC energy into AC. This study evaluates the validity of this assumption, theoretically investigating the current magnitude of clipping and its effect on soiling over the contiguous United States. This is done by modelling energy yield, clipping and soiling across a grid of locations. The results show that in reality, under the current deployment trends, inverter undersizing minimally affects soiling, as it reduces these losses by no more than 1%absolute. Indeed, clipping masks soiling in areas where losses are already low, whereas it has a negligible effect where soiling is most significant. However, the mitigation effects might increase under conditions of lower performance losses or more pronounced inverter undersizing. In any case, one should take into account that degradation makes clipping less frequent as systems age, also decreasing its masking effect on soiling. Therefore, even if soiling was initially mitigated by the inverter undersizing, its effect would become more visible with time.

In the rapidly evolving field of solar energy, Photovoltaic (PV) manufacturers are constantly challenged by the degradation of PV modules due to localized overheating, commonly known as hotspots. This issue not only reduce the efficiency of solar panels but, in severe cases, can lead to irreversible damage, malfunctioning, and even fire hazards. Addressing this critical challenge, our research introduces an innovative electronic device designed to effectively mitigate PV hotspots. This pioneering solution consists of a novel combination of a current comparator and a current mirror circuit. These components are uniquely integrated with an automatic switching mechanism, notably eliminating the need for traditional bypass diodes. We rigorously tested and validated this device on PV modules exhibiting both adjacent and non-adjacent hotspots. Our findings are groundbreaking: the hotspot temperatures were significantly reduced from a dangerous 55 °C to a safer 35 °C. Moreover, this intervention remarkably enhanced the output power of the modules by up to 5.3%. This research not only contributes a practical solution to a longstanding problem in solar panel efficiency but also opens new pathways for enhancing the safety and longevity of solar PV systems.

Abstract not provided.

Abstract not provided.

More than 90% of utility-scale photovoltaic (PV) power plants in the US use single-axis trackers (SATs) due to their potential for substantially higher power production over fixed-array systems. However, they are subject to software misconfigurations and mechanical failures, leading to suboptimal tracking accuracy. If failures are left undetected, the overall power yield of the PV power plant is reduced significantly. Robust detection and diagnosis of SAT faults is needed to minimize downtime and ensure continuous and efficient operation. This work presents analytic tools based on machine learning to detect deviations in SAT tracking performance and classify SAT faults.

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

Different data pipelines and statistical methods are applied to photovoltaic (PV) performance datasets to quantify the performance loss rate (PLR). Since the real values of PLR are unknown, a variety of unvalidated values are reported. As such, the PV industry commonly assumes PLR based on statistically extracted ranges from the literature. However, the accuracy and uncertainty of PLR depend on several parameters including seasonality, local climatic conditions, and the response of a particular PV technology. In addition, the specific data pipeline and statistical method used affect the accuracy and uncertainty. To provide insights, a framework of (≈200 million) synthetic simulations of PV performance datasets using data from different climates is developed. Time series with known PLR and data quality are synthesized, and large parametric studies are conducted to examine the accuracy and uncertainty of different statistical approaches over the contiguous US, with an emphasis on the publicly available and “standardized” library, RdTools. In the results, it is confirmed that PLRs from RdTools are unbiased on average, but the accuracy and uncertainty of individual PLR estimates vary with climate zone, data quality, PV technology, and choice of analysis workflow. Best practices and improvement recommendations based on the findings of this study are provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and −3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.

Abstract not provided.

Abstract not provided.

Photovoltaic systems may underperform expectations for several reasons, including inaccurate initial estimates, suboptimal operations and maintenance, or component degradation. Accurate assessment of these loss factors aids in addressing root causes of underperformance and in realizing accurate expectations and models. The performance loss rate (PLR) is a commonly cited high-level metric for the change in system output over time, but there is no precise, standard definition. Herein, an annualized definition of PLR that is inclusive of all loss factors and that can capture nonlinear changes to performance over time is proposed. The importance of distinguishing between recoverable and nonrecoverable losses which underly PLR is highlighted.

Abstract not provided.

The accurate quantification of the performance loss rate of photovoltaic systems is critical for project economics. Following the current research activities in the photovoltaic performance and reliability field, this work presents a comparative assessment between common change point methods for performance loss rate estimation of fielded photovoltaic installations. An extensive testing campaign was thus performed to evaluate time series analysis approaches for performance loss rate evaluation of photovoltaic systems. Historical electrical data from eleven photovoltaic systems installed in Nicosia, Cyprus, and the locations’ meteorological measurements over a period of 8 years were used for this investigation. The application of change point detection algorithms on the constructed monthly photovoltaic performance ratio series revealed that the obtained trend might not always be linear. Specifically, thin film photovoltaic systems showed nonlinear behavior, while nonlinearities were also detected for some crystalline silicon photovoltaic systems. When applying several change point techniques, different numbers and locations of changes were detected, resulting in different performance loss rate values (varying by up to 0.85%/year even for the same number of change points). The results highlighted the importance of the application of nonlinear techniques and the need to extract a robust nonlinear model for detecting significant changes in time series data and estimating accurately the performance loss rate of photovoltaic installations.

This document provides the instructions for participating in the 2021 blind photovoltaic (PV) modeling intercomparison organized by the PV Performance Modeling Collaborative (PVPMC). It describes the system configurations, metadata, and other information necessary for the modeling exercise. The practical details of the validation datasets are also described. The datasets were published online in open access in April 2023, after completing the analysis of the results.

Abstract not provided.

Abstract not provided.

Abstract not provided.

This report describes the creation process and final content of a spectral irradiance dataset for Albuquerque, New Mexico accompanied by a set of spectral response measurements for modules deployed at the same location. The spectral irradiance measurements were made using horizontally mounted spectroradiometers; therefore, they represent global horizontal irradiance. The dataset combines non-continuous spectroradiometer and weather measurements from a two-year period into a single calendar year. The data files are accompanied by extensive metadata as well as example calculations and graphs to demonstrate the potential uses of this database. The spectral response measurements were carried out by the National Renewable Energy Laboratory using 12 commercial silicon modules types that are undergoing long-term evaluation at Sandia National Laboratories in Albuquerque.

This report describes the structure and content of an open dataset created for the purpose of testing and validating PV module temperature prediction models and their parameters. The dataset contains the main environmental parameters that affect temperature: irradiance, ambient temperature, wind speed and down-welling infrared radiation, as well as measured back-of-module temperature.

The cost of photovoltaic (PV) modules has declined by 85% since 2010. To achieve this reduction, manufacturers altered module designs and bill of materials; changes that could affect module durability and reliability. To determine if these changes have affected module durability, we measured the performance degradation of 834 fielded PV modules representing 13 module types from 7 manufacturers in 3 climates over 5 years. Degradation rates (Rd) are highly nonlinear over time, and seasonal variations are present in some module types. Mean and median degradation rate values of −0.62%/year and −0.58%/year, respectively, are consistent with rates measured for older modules. Of the 23 systems studied, 6 have degradation rates that will exceed the warranty limits in the future, whereas 13 systems demonstrate the potential of achieving lifetimes beyond 30 years, assuming Rd trends have stabilized.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

We propose a set of benchmark tests for current-voltage (IV) curve fitting algorithms. Benchmark tests enable transparent and repeatable comparisons among algorithms, allowing for measuring algorithm improvement over time. An absence of such tests contributes to the proliferation of fitting methods and inhibits achieving consensus on best practices. Benchmarks include simulated curves with known parameter solutions, with and without simulated measurement error. We implement the reference tests on an automated scoring platform and invite algorithm submissions in an open competition for accurate and performant algorithms.

Photovoltaic (PV) performance is affected by reversible and irreversible losses. These can typically be mitigated through responsive and proactive operations and maintenance (O&M) activities. However, to generate profit, the cost of O&M must be lower than the value of the recovered electricity. This value depends both on the amount of recovered energy and on the electricity prices, which can vary significantly over time in spot markets. The present work investigates the impact of the electricity price variability on the PV profitability and on the related O&M activities in Italy, Portugal, and Spain. It is found that the PV revenues varied by 1.6 × to 1.8 × within the investigated countries in the last 5 years. Moreover, forecasts predict higher average prices in the current decade compared to the previous one. These will increase the future PV revenues by up to 60% by 2030 compared to their 2015–2020 mean values. These higher revenues will make more funds available for better maintenance and for higher quality components, potentially leading to even higher energy yield and profits. Linearly growing or constant price assumptions cannot fully reproduce these expected price trends. Furthermore, significant price fluctuations can lead to unexpected scenarios and alter the predictions.

Irradiance transposition models seem to perform well, except the Isotropic with -11.25 W/m² underestimation. Most temperature models could not capture behavior when ΔΤ between module and ambient is negative. Uncertainties due to derate factors: modelers overbudgeted resulting in significant power underestimation; maybe ~10% is appropriate for commercial systems but not lab-scale? Most software and models cluster together showing good reproducibility among participants. Modeler’s skills seem to be more important than the PV model itself (flat efficiency with irradiance, positive power temperature coefficients, etc.). Results and best practices will be communicated in a journal article.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

In photovoltaic power ratings, a single solar spectrum, AM1.5, is the de facto standard for record laboratory efficiencies, commercial module specifications, and performance ratios of solar power plants. More detailed energy analysis that accounts for local spectral irradiance, along with temperature and broadband irradiance, reduces forecast errors to expand the grid utility of solar energy. Here, ground-level measurements of spectral irradiance collected worldwide have been pooled to provide a sampling of geographic, seasonal, and diurnal variation. Applied to nine solar cell types, the resulting divergence in solar cell efficiencies illustrates that a single spectrum is insufficient for comparisons of cells with different spectral responses. Cells with two or more junctions tend to have efficiencies below that under the standard spectrum. Silicon exhibits the least spectral sensitivity: relative weekly site variation ranges from 1% in Lima, Peru to 14% in Edmonton, Canada.

Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

Fault detection and classification in photovoltaic (PV) systems through real-time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend-based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change-point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.

PV module operating temperature is the second-most important factor influencing PV system yield–after irradiance–and a substantial contributor to uncertainty in energy system yield predictions. Models commonly used to predict operating temperature in system simulations are based on a simplified energy balance that lumps together different heat loss mechanisms–including radiation–and assumes an overall linear behavior. Radiative heat loss to the sky is usually substantial, but modeling it accurately requires additional information about down-welling long-wave radiation or sky temperature and increases the complexity of temperature model equations. In this work we show how radiative losses to the sky can be separated into two parts to improve the accuracy of modeling without additional complexity. We also predict and demonstrate the variation of these losses at different tilt angles and show that the effective view factor is reduced by the non- isotropic distribution of down-welling long-wave radiation. Finally, we demonstrate substantial reduction in bias (MBE) and scatter (RMSE) when the new radiative loss term is added to the Faiman model using one year of measurements at Sandia National Labs.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

The performance loss rate (PLR) is a vital parameter for the time-dependent assessment of photovoltaic (PV) system performance and health state. Although this metric can be calculated in a relatively straightforward manner, it is challenging to achieve accurate and reproducible results with low uncertainty. Furthermore, the temporal evolution of PV system performance is usually nonlinear, but in many cases a linear evaluation is preferred as it simplifies the assessment and it is easier to evaluate. As such, the search for a robust and reproducible calculation methodology providing reliable linear PLR values across different types of systems and conditions has been the focus of many research activities in recent years. In this paper, the determination of PV system PLR using different pipelines and approaches is critically evaluated and recommendations for best practices are given. As nonlinear PLR assessments are fairly new, there is no consent on how to calculate reliable values. Several promising nonlinear approaches have been developed recently and are presented as tools to evaluate the PV system performance in great detail. Furthermore, challenges are discussed with respect to the PLR calculation but also opportunities for differentiating individual performance losses from a generic PLR value having the potential of enabling actionable insights for maintenance.

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

Abstract not provided.

The precise estimation of performance loss rate (PLR) of photovoltaic (PV) systems is vital for reducing investment risks and increasing the bankability of the technology. Until recently, the PLR of fielded PV systems was mainly estimated through the extraction of a linear trend from a time series of performance indicators. However, operating PV systems exhibit failures and performance losses that cause variability in the performance and may bias the PLR results obtained from linear trend techniques. Change-point (CP) methods were thus introduced to identify nonlinear trend changes and behaviour. The aim of this work is to perform a comparative analysis among different CP techniques for estimating the annual PLR of eleven grid-connected PV systems installed in Cyprus. Outdoor field measurements over an 8-year period (June 2006-June 2014) were used for the analysis. The obtained results when applying different CP algorithms to the performance ratio time series (aggregated into monthly blocks) demonstrated that the extracted trend may not always be linear but sometimes can exhibit nonlinearities. The application of different CP methods resulted to PLR values that differ by up to 0.85% per year (for the same number of CPs/segments).

Geographic analysis of photovoltaic (PV) performance factors across large regions can help relevant stakeholders make informed, and reduced risk decisions. High temporal and spatial resolution meteorological data from the National Solar Radiation Database are used to investigate performance and cost as an effect of varying system characteristics such as the module temperature coefficients, mounting configurations and coatings. The results demonstrated the strong climatic dependence that these characteristics have on annual energy yield whereas the revenues were dominated by the electricity price.

Accurate quantification of photovoltaic (PV) system degradation rate (RD) is essential for lifetime yield predictions. Although RD is a critical parameter, its estimation lacks a standardized methodology that can be applied on outdoor field data. The purpose of this paper is to investigate the impact of time period duration and missing data on RD by analyzing the performance of different techniques applied to synthetic PV system data at different linear RD patterns and known noise conditions. The analysis includes the application of different techniques to a 10-year synthetic dataset of a crystalline Silicon PV system, with emulated degradation levels and imputed missing data. The analysis demonstrated that the accuracy of ordinary least squares (OLS), year-on-year (YOY), autoregressive integrated moving average (ARIMA) and robust principal component analysis (RPCA) techniques is affected by the evaluation duration with all techniques converging to lower RD deviations over the 10-year evaluation, apart from RPCA at high degradation levels. Moreover, the estimated RD is strongly affected by the amount of missing data. Filtering out the corrupted data yielded more accurate RD results for all techniques. It is proven that the application of a change-point detection stage is necessary and guidelines for accurate RD estimation are provided.

Operation and maintenance (OM) and monitoring strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to faults. An OM decision support system (DSS) was developed in this work for providing recommendations of actionable decisions to resolve fault and performance loss events. The proposed DSS operates entirely on raw field measurements and incorporates technical asset and financial management features. Historical measurements from a large-scale PV system installed in Greece were used for the benchmarking procedure. The results demonstrated the financial benefits of performing mitigation actions in case of near zero power production incidents. Stochastic simulations that consider component malfunctions and failures exhibited a net economic gain of approximately 4.17 €/kW/year when performing OM actions. For an electricity price of 59.98 €/MWh, a minimum of 8.4% energy loss per year is required for offsetting the annualized OM cost value of 7.45 €/kW/year calculated by the SunSpec/National Renewable Energy Laboratory (NREL) PV OM Cost Model.

Many Electric Power Systems (EPS) already include geographically dispersed photovoltaic (PV) systems. These PV systems may not be co-located with highest-priority loads and, thus, easily integrated into a microgrid; rather PV systems and priority loads may be far away from one another. Furthermore, because of the existing EPS configuration, non-critical loads between the distant PV and critical load(s) cannot be selectively disconnected. To achieve this, the proposed approach finds ideal switch locations by first defining the path between the critical load and a large PV system, then identifies all potential new switch locations along this path, and finally discovers switch locations for a particular budget by finding the ones the produce the lowest Loss of Load Probability (LOLP), which is when load exceed generation. Discovery of the switches with the lowest LOLP involves a Particle Swarm Optimization (PSO) implementation. The objective of the PSO is to minimize the microgird's LOLP. The approach assumes dynamic microgrid operations, where both the critical and non-critical loads are powered during the day and only the critical load at night. To evaluate the approach, this paper includes a case study that uses the topology and Advanced Metering Infrastructure (AMI) data from an actual EPS. For this example, the assessment found new switch locations that reduced the LOLP by up to 50% for two distant PV location scenarios.

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 climatic-and 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.

Abstract not provided.

Corrective maintenance strategies are important for safeguarding optimum photovoltaic (PV) performance while also minimizing downtimes due to failures. In this work, a complete operation and maintenance (OM) decision support system (DSS) was developed for corrective maintenance. The DSS operates entirely on field measurements and incorporates technical asset and financial management features. It was validated experimentally on a large-scale PV system installed in Greece and the results demonstrated the financial benefits of performing corrective actions in case of failures and reversible loss mechanisms. Reduced response and resolution times of corrective actions could improve the PV power production of the test PV plant by up to 2.41%. Even for 1% energy yield improvement by performing corrective actions, a DSS is recommended for large-scale PV plants (with a peak capacity of at least 250 kWp).

The IEA PVPS Task 13 group, experts who focus on photovoltaic performance, operation, and reliability from several leading R&D centers, universities, and industrial companies, is developing a framework for the calculation of performance loss rates of a large number of commercial and research photovoltaic (PV) power plants and their related weather data coming across various climatic zones. The general steps to calculate the performance loss rate are (i) input data cleaning and grading; (ii) data filtering; (iii) performance metric selection, corrections, and aggregation; and finally, (iv) application of a statistical modeling method to determine the performance loss rate value. In this study, several high-quality power and irradiance datasets have been shared, and the participants of the study were asked to calculate the performance loss rate of each individual system using their preferred methodologies. The data are used for benchmarking activities and to define capabilities and uncertainties of all the various methods. The combination of data filtering, metrics (performance ratio or power based), and statistical modeling methods are benchmarked in terms of (i) their deviation from the average value and (ii) their uncertainty, standard error, and confidence intervals. It was observed that careful data filtering is an essential foundation for reliable performance loss rate calculations. Furthermore, the selection of the calculation steps filter/metric/statistical method is highly dependent on one another, and the steps should not be assessed individually.

Photovoltaic (PV) soiling profiles exhibit a sawtooth shape, where cleaning events and soiling deposition periods alternate. Generally, the rate at which soiling accumulates is assumed to be constant within each deposition period. In reality, changes in rates can occur because of sudden variations in climatic conditions, e.g., dust storms or prolonged periods of rain. The existing models used to extract the soiling profile from the PV performance data might fail to capture the change points and occasionally estimate incorrect soiling profiles. This work analyzes how the introduction of change points can be beneficial for soiling extraction. Data from nine soiling stations and a 1-MW site were analyzed by using piecewise regression and three change point detection algorithms. The results showed that accounting for change points can provide significant benefits to the modeling of soiling even if not all the change point algorithms return the same improvements. Considering change points in historical trends is found to be particularly important for studies aiming to optimize cleaning schedules.

The IEC 61853 photovoltaic (PV) module energy rating standard requires measuring module power (and hence, efficiency) over a matrix of irradiance and temperature conditions. These matrix points represent nearly the full range of operating conditions encountered in the field in all but the most extreme locations and create an opportunity to develop alternative approaches for calculating system performance. In this article, a new PV module efficiency model is presented and compared with five published models using matrix data collected from four different PV module types. The results of the comparative analysis demonstrated that the new model improves on the existing ones exhibiting root-mean-square errors in normalized efficiency well below 0.01 for all cases and PV modules. The analysis also highlighted its ability to interpolate and extrapolate performance between and beyond measured matrix points of irradiance and temperature, establishing it as a robust yet relatively simple model for several applications that are detailed throughout this article.

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.

Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in-field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Although common practice for estimating photovoltaic (PV) degradation rate (RD) assumes a linear behavior, field data have shown that degradation rates are frequently nonlinear. This article presents a new methodology to detect and calculate nonlinear RD based on PV performance time-series from nine different systems over an eight-year period. Prior to performing the analysis and in order to adjust model parameters to reflect actual PV operation, synthetic datasets were utilized for calibration purposes. A change-point analysis is then applied to detect changes in the slopes of PV trends, which are extracted from constructed performance ratio (PR) time-series. Once the number and location of change points is found, the ordinary least squares method is applied to the different segments to compute the corresponding rates. The obtained results verified that the extracted trends from the PR time-series may not always be linear and therefore, 'nonconventional' models need to be applied. All thin-film technologies demonstrated nonlinear behavior whereas nonlinearity detected in the crystalline silicon systems is thought to be due to a maintenance event. A comparative analysis between the new methodology and other conventional methods demonstrated levelized cost of energy differences of up to 6.14%, highlighting the importance of considering nonlinear degradation behavior.

A main challenge towards ensuring large-scale and seamless integration of photovoltaic systems is to improve the accuracy of energy yield forecasts, especially in grid areas of high photovoltaic shares. The scope of this paper is to address this issue by presenting a unified methodology for hourly-averaged day-ahead photovoltaic power forecasts with improved accuracy, based on data-driven machine learning techniques and statistical post-processing. More specifically, the proposed forecasting methodology framework comprised of a data quality stage, data-driven power output machine learning model development (artificial neural networks), weather clustering assessment (K-means clustering), post-processing output optimisation (linear regressive correction method) and the final performance accuracy evaluation. The results showed that the application of linear regression coefficients to the forecasted outputs of the developed day-ahead photovoltaic power production neural network improved the performance accuracy by further correcting solar irradiance forecasting biases. The resulting optimised model provided a mean absolute percentage error of 4.7% when applied to historical system datasets. Finally, the model was validated both, at a hot as well as a cold semi-arid climatic location, and the obtained results demonstrated close agreement by yielding forecasting accuracies of mean absolute percentage error of 4.7% and 6.3%, respectively. The validation analysis provides evidence that the proposed model exhibits high performance in both forecasting accuracy and stability.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

Optimum and reliable photovoltaic (PV) plant performance requires accurate diagnostics of system losses and failures. Data-driven approaches can classify such losses however, the appropriate PV data features required for accurate classification remains unclear. To avoid misclassification, this study reviews the potential issues associated with inabilities to separate fault conditions that overlap using certain data features. Feature selection techniques that define each feature's importance and identify the set of features necessary for producing the most accurate results are also explored. The experiment quantified the amount of overlap using both maximum power point (MPP) and current and voltage (I-V) curve data sets. The I -V data provided an overall increase in classification accuracy of 8% points above the case where only MPP was available.

It is a common approach to assume a constant performance drop during the photovoltaic (PV) lifetime. However, operational data demonstrated that PV degradation rate (R_{D}) may exhibit nonlinear behavior, which neglecting it may increase financial risks. This study presents and compares three approaches, based on open-source libraries, which are able to detect and calculate nonlinear R_{D}. Two of these approaches include trend extraction and change-point detection methods, which are frequently used statistical tools. Initially, the processed monthly PV performance ratio (PR) time-series are decomposed in order to extract the trend and change-point analysis techniques are applied to detect changes in the slopes. Once the number of change-points is optimized by each model, the ordinary least squares (OLS) method is applied on the different segments to compute the corresponding rates. The third methodology is a regression analysis method based on simultaneous segmentation and slope extraction. Since the 'real' R_{D} value is an unknown parameter, this investigation was based on synthetic datasets with emulated two-step degradation rates. As such, the performance of the three approaches was compared exhibiting mean absolute errors ranging from 0 to 0.46%/year whereas the change-point position detection differed from 0 to 10 months.

Abstract not provided.

Dust accumulation significantly affects the performance of photovoltaic modules and its impact can be mitigated by various cleaning methods. Optimizing the cleaning frequency is essential to minimize the soiling losses and, at the same time, the costs. However, the effectiveness of cleaning lowers with time because of the reduced energy yield due to degradation. Additionally, economic factors such as the escalation in electricity price and inflation can compound or counterbalance the effect of degradation on the soiling mitigation profits. The present study analyzes the impact of degradation, escalation in electricity price and inflation on the revenues and costs of cleanings and proposes a methodology to maximize the profits of soiling mitigation of any system. The energy performance and soiling losses of a 1 MW system installed in southern Spain were analyzed and integrated with theoretical linear and nonlinear degradation rate patterns. The Levelized Cost of Energy and Net Present Value were used as criteria to identify the optimum cleaning strategies. The results showed that the two metrics convey distinct cleaning recommendations, as they are influenced by different factors. For the given site, despite the degradation effects, the optimum cleaning frequency is found to increase with time of operation.

Abstract not provided.