Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models
Journal Article
·
· IEEE Journal of Photovoltaics
- Stanford Univ., CA (United States); SLAC National Accelerator Lab., Menlo Park, CA (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)
A novel unsupervised machine learning approach for analyzing time-series data is applied to the topic of photovoltaic (PV) system degradation rate estimation, sometimes referred to as energy-yield degradation analysis. This approach only requires a measured power signal as an input--no irradiance data, temperature data, or system configuration information. We present results on a data set that was previously analyzed and presented by NREL using RdTools, validating the accuracy of the new approach and showing increased robustness to data anomalies while reducing the data requirements to carry out the analysis.
- Research Organization:
- SLAC National Accelerator Laboratory (SLAC), Menlo Park, CA (United States); National Renewable Energy Laboratory (NREL), Golden, CO (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Office of Renewable Energy. Solar Energy Technologies Office
- Grant/Contract Number:
- AC02-76SF00515; AC3608GO28308; AC36-08GO28308; 34911; 30311; 34348
- OSTI ID:
- 1836308
- Alternate ID(s):
- OSTI ID: 1575280; OSTI ID: 1660085; OSTI ID: 1861114
- Report Number(s):
- NREL/JA-5K00-75031; TRN: US2001192
- Journal Information:
- IEEE Journal of Photovoltaics, Vol. 10, Issue 2; ISSN 2156-3381
- Publisher:
- IEEECopyright Statement
- Country of Publication:
- United States
- Language:
- English
Cited by: 12 works
Citation information provided by
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