skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation

Abstract

Our objective is to develop a set of lifetime performance prediction models for PV modules. Models were developed based on common I-V, Pmp datastreams from modules under outdoor (real-world) exposure and indoor (laboratory-based) accelerated exposures and temporal analytics. These common datastreams that span both indoor and outdoor exposures were supplemented with additional information such as meteorology data, climate and PV module make/model and module specifications. The predictive models were developed using a stressor/mechanism/response framework in which all data are categorized as stressor, mechanism or performance variables and are represented as time-series datastreams. We develop and validate these accelerated indoor exposures and evaluation tests and models and cross-correlate the temporal signatures of PV module degradation mechanisms in the I-V, Pmp time-series between outdoor and accelerated indoor tests. The predictive test and model specify indoor and outdoor exposure and data acquisition criteria, variable selection, and temporal duration and variance.

Authors:
 [1];  [1];  [1]
  1. Case Western Reserve Univ., Cleveland, OH (United States)
Publication Date:
Research Org.:
Case Western Reserve Univ., Cleveland, OH (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Solar Energy Technologies Office (EE-4S)
Contributing Org.:
Sandia National Laboratory, Terraform Power, UL, Fraunhofer-ISE
OSTI Identifier:
1529093
Report Number(s):
DOE-CWRU-0007140
2163680374
DOE Contract Number:  
EE0007140
Resource Type:
Technical Report
Country of Publication:
United States
Language:
English
Subject:
14 SOLAR ENERGY; reliability; degradation; data analytics

Citation Formats

French, Roger, Braid, Jennifer L., and Liu, JiQi. Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation. United States: N. p., 2019. Web. doi:10.2172/1529093.
French, Roger, Braid, Jennifer L., & Liu, JiQi. Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation. United States. doi:10.2172/1529093.
French, Roger, Braid, Jennifer L., and Liu, JiQi. Sun . "Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation". United States. doi:10.2172/1529093. https://www.osti.gov/servlets/purl/1529093.
@article{osti_1529093,
title = {Module Level Exposure and Evaluation Test (MLEET) for Real- world and Laboratory-based PV Modules: Common Data and Analytics for Quantitative Cross-correlation and Validation},
author = {French, Roger and Braid, Jennifer L. and Liu, JiQi},
abstractNote = {Our objective is to develop a set of lifetime performance prediction models for PV modules. Models were developed based on common I-V, Pmp datastreams from modules under outdoor (real-world) exposure and indoor (laboratory-based) accelerated exposures and temporal analytics. These common datastreams that span both indoor and outdoor exposures were supplemented with additional information such as meteorology data, climate and PV module make/model and module specifications. The predictive models were developed using a stressor/mechanism/response framework in which all data are categorized as stressor, mechanism or performance variables and are represented as time-series datastreams. We develop and validate these accelerated indoor exposures and evaluation tests and models and cross-correlate the temporal signatures of PV module degradation mechanisms in the I-V, Pmp time-series between outdoor and accelerated indoor tests. The predictive test and model specify indoor and outdoor exposure and data acquisition criteria, variable selection, and temporal duration and variance.},
doi = {10.2172/1529093},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {3}
}