@misc{315ef95361ea49158ddf7b33ea03adc9,
title = "Assessment of Accelerated Stress Testing Data for Silicon Photovoltaics Using Tensor Decomposition Methods",
abstract = "The photovoltaic (PV) industry is simultaneously targeting long warranties and new materials/designs for high-energy-yield modules, requiring an advanced methodology to forecast long-term durability of products with un-proven materials combinations. Extended, sequential, and combined stress testing methods are gaining popularity for assessing durability of PV modules/materials beyond the early-stage mortalities. Importantly, multiple degradation mechanisms can proceed simultaneously, and their separate contributions to the overall power loss should ideally be quantified. This work examines the use of data-driven tools towards developing a strategy for faster learning cycles in accelerated stress testing.",
keywords = "accelerated stress testing, PV degradation, tensor decomposition",
author = "Andrew Glaws and Dana Kern",
year = "2024",
language = "American English",
series = "Presented at the 52nd IEEE Photovoltaic Specialists Conference (PVSC52), 9-14 June 2024, Seattle, Washington",
publisher = "National Renewable Energy Laboratory (NREL)",
address = "United States",
type = "Other",
}