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Evaluation of Component Reliability in Photovoltaic Systems using Field Failure Statistics

Gunda, Thushara G.; Homan, Rachel

Ongoing operations and maintenance (O&M) are needed to ensure photovoltaic (PV) systems continue to operate and meet production targets over the lifecycle of the system. Although average costs to operate and maintain PV systems have been decreasing over time, reported costs can vary significantly at the plant level. Estimating O&M costs accurately is important for informing financial planning and tracking activities, and subsequently lowering the levelized cost of electricity (LCOE) of PV systems. This report describes a methodology for improving O&M planning estimates by using empirically-derived failure statistics to capture component reliability in the field. The report also summarizes failure patterns observed for specific PV components and local environmental conditions observed in Sandia's PV Reliability, Operations & Maintenance (PVROM) database, a collection of field records across 800+ systems in the U.S. Where system-specific or fleet-specific data are lacking, PVROM-derived failure distribution values can be used to inform cost modeling and other reliability analyses to evaluate opportunities for performance improvements.

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TYPE SAND Report YEAR 2020
DOIOSTI

An assessment of the value of principal component analysis for photovoltaic IV trace classification of physically-induced failures

Conference Record of the IEEE Photovoltaic Specialists Conference

Hopwood, Michael W.; Gunda, Thushara G.; Seigneur, Hubert; Walters, Joseph

Principal component analysis (PCA) reduces dimensionality by generating uncorrelated variables and improves the interpretability of the sample space. This analysis focused on assessing the value of PCA for improving the classification accuracy of failures within current-voltage (IV) traces. Our results show that combining PCA with random forests improves classification by only ~1% (bringing the accuracy to >99%), compared to a baseline of only random forests (without PCA) of >98%. The inclusion of PCA, however, does provide an opportunity to study an interesting representation of all of the features on a single, two-dimensional feature space. A visualization of the first two principal components (similar to IV profile but rotated) captures how the inclusion of a current differential feature causes a notable separation between failure modes due to their effect on the slope. This work continues the discussion of generating different ways of extracting information from the IV curve, which can help with failure classification - especially for failures that only exhibit marginal profile changes in IV curves.

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TYPE Conference Poster YEAR 2020
OSTIScopus

The Grey Zone Test Range Integrated Urban Simulation Environment

Kelic, Andjelka; Beyeler, Walter E.; Mitchell, Roger M.; Bernard, Michael L.; Doyle, Casey L.; Rogers, Alisa N.; Frazier, Christopher R.; Gunda, Thushara G.; Klise, Katherine A.

Sandia National Laboratories is part of the government test and evaluation team for the Defense Advanced Research Projects Agency Collection and Monitoring via Planning for Active Situational Scenarios program. The program is designed to better understand competition in the area between peace and conventional conflict when adversary actions are subtle and difficult to detect. For the purposes of test and evaluation, Sandia conducted a range of activities for the program: creation of the Grey Zone Test Range; design of the data stream for a user experiment conducted with U.S. Indo-Pacific Command; design, implementation, and execution of the formal evaluation; and analysis and summary of the evaluation results. This report details Sandia's activities and provides additional information on the Grey Zone Test Range urban simulation environment developed to evaluate the performer technologies.

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TYPE SAND Report YEAR 2020
DOIOSTI

Predicting Solar Plant Generation with Machine Learning Techniques

Gunda, Thushara G.

Accurately predicting power generation for PV sites is critical for prioritizing relevant operations & maintenance activities, thereby extending the lifetime of a system and improving profit margins. A number of factors influence power generation at PV sites, including local weather, shading and soiling losses, design of modules, DC mismatches, and degradation over time. Other external factors such as curtailment and grid outages can also have a notable impact on power generation. Machine learning techniques can be used to provide more accurate predictions of PV power production by accounting for important weather and climate information neglected by current industry methods. This article will cover the deficiencies of those methods and will show how machine learning can dramatically improve power generation predictions.

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TYPE Other Report YEAR 2020
DOIOSTI
Results 76–100 of 134
Results 76–100 of 134