A Machine Learning Evaluation of Maintenance Records for Common Failure Modes in PV Inverters

Journal Article · Wed Jan 01 00:00:00 EST 2020 · IEEE Access

DOI:https://doi.org/10.1109/ACCESS.2020.3039182· OSTI ID:1811161

Gunda, Thushara; Hackett, Sean; Kraus, Laura; Downs, Christopher; Jones, Ryan; McNalley, Christopher; Bolen, Michael; Walker, Andy

Not Available

• Research Organization: National Renewable Energy Laboratory (NREL), Golden, CO (United States); Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA); USDOE Office of Energy Efficiency and Renewable Energy (EERE); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office
• Grant/Contract Number: AC04-94AL85000; AC36-08GO28308
• OSTI ID: 1811161
• Alternate ID(s): OSTI ID: 1738917; OSTI ID: 1755746; OSTI ID: 1861218
• Report Number(s): NREL/JA--5C00-78736; SAND--2020-13529J; 9272625
• Journal Information: IEEE Access, Journal Name: IEEE Access Vol. 8; ISSN 2169-3536
• Publisher: Institute of Electrical and Electronics Engineers
• Country of Publication: United States
• Language: English

Related Subjects

14 SOLAR ENERGY
97 MATHEMATICS AND COMPUTING
failures
inverters
machine learning
natural language processing
photovoltaics
weibull