Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models

Meyers, Bennet; Deceglie, Michael; Deline, Chris; Jordan, Dirk

doi:10.1109/JPHOTOV.2019.2957646

Title: Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models

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Abstract

A novel unsupervised machine learning approach for analyzing time-series data is applied to the topicofphotovoltaic 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 are required. We present results on a dataset that was previously analyzed and presented by National Renewable Energy Laboratory 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.

Authors:

;

Publication Date:: 2020-03-01

Research Org.:: SLAC National Accelerator Lab., Menlo Park, CA (United States); National Renewable Energy Lab. (NREL), Golden, CO (United States)

Sponsoring Org.:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office

OSTI Identifier:: 1836308

Alternate Identifier(s):: OSTI ID: 1575280; OSTI ID: 1660085; OSTI ID: 1861114

Report Number(s):: NREL/JA-5K00-75031
Journal ID: ISSN 2156-3381; 8939335

Grant/Contract Number:: 34911; 30311; 34348; AC02-76SF00515; AC3608GO28308; AC36-08GO28308

Resource Type:: Published Article

Journal Name:: IEEE Journal of Photovoltaics

Additional Journal Information:: Journal Name: IEEE Journal of Photovoltaics Journal Volume: 10 Journal Issue: 2; Journal ID: ISSN 2156-3381

Publisher:: Institute of Electrical and Electronics Engineers

Country of Publication:: United States

Language:: English

Subject:: 14 SOLAR ENERGY; computer-aided analysis; data analysis; distributed power generation; photovoltaic (PV) systems; computer aided analysis; photovoltaic systems; statistical learning

Citation Formats


                    Meyers, Bennet, Deceglie, Michael, Deline, Chris, and Jordan, Dirk. Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models.  United States: N. p., 2020. 
Web.  doi:10.1109/JPHOTOV.2019.2957646.

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                    Meyers, Bennet, Deceglie, Michael, Deline, Chris, & Jordan, Dirk. Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models.  United States.  https://doi.org/10.1109/JPHOTOV.2019.2957646

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                    Meyers, Bennet, Deceglie, Michael, Deline, Chris, and Jordan, Dirk. Sun .  
"Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models".  United States.  https://doi.org/10.1109/JPHOTOV.2019.2957646.

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@article{osti_1836308,

  title        = {Signal Processing on PV Time-Series Data: Robust Degradation Analysis Without Physical Models},

  author       = {Meyers, Bennet and Deceglie, Michael and Deline, Chris and Jordan, Dirk},

  abstractNote = {A novel unsupervised machine learning approach for analyzing time-series data is applied to the topicofphotovoltaic 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 are required. We present results on a dataset that was previously analyzed and presented by National Renewable Energy Laboratory 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.},

  doi          = {10.1109/JPHOTOV.2019.2957646},

  journal      = {IEEE Journal of Photovoltaics},

  number       = 2,

  volume       = 10,

  place        = {United States},

  year         = {2020},

  month        = {3}

}

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