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Title: Data processing and quality verification for improved photovoltaic performance and reliability analytics

Abstract

Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in-field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification ofmore » the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.« less

Authors:
ORCiD logo [1]; ORCiD logo [2];  [3]; ORCiD logo [1];  [1];  [4]; ORCiD logo [2]; ORCiD logo [1]
  1. PV Technology Laboratory, FOSS Research Centre for Sustainable Energy, Department of Electrical and Computer Engineering University of Cyprus Nicosia 1678 Cyprus
  2. Sandia National Laboratories Albuquerque NM 87185 USA
  3. SolarCentury 90 Union Street London SE1 0NW UK
  4. Gantner Instruments GmbH Montafonerstraße 4 Schruns 6780 Austria
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
European Regional Development Fund (ERDF); Cyprus Research & Innovation Foundation; USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Solar Energy Technologies Office; USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1670826
Alternate Identifier(s):
OSTI ID: 1691440; OSTI ID: 1779979
Report Number(s):
SAND-2020-10988J
Journal ID: ISSN 1062-7995
Grant/Contract Number:  
AC04-94AL85000; P2P/SOLAR/0818/0012; 34364; NA0003525
Resource Type:
Published Article
Journal Name:
Progress in Photovoltaics
Additional Journal Information:
Journal Name: Progress in Photovoltaics; Journal ID: ISSN 1062-7995
Publisher:
Wiley
Country of Publication:
United Kingdom
Language:
English
Subject:
14 SOLAR ENERGY; analytics; data fidelity; data inference; data quality; invalid values; performance; photovoltaics

Citation Formats

Livera, Andreas, Theristis, Marios, Koumpli, Elena, Theocharides, Spyros, Makrides, George, Sutterlueti, Juergen, Stein, Joshua S., and Georghiou, George E. Data processing and quality verification for improved photovoltaic performance and reliability analytics. United Kingdom: N. p., 2020. Web. https://doi.org/10.1002/pip.3349.
Livera, Andreas, Theristis, Marios, Koumpli, Elena, Theocharides, Spyros, Makrides, George, Sutterlueti, Juergen, Stein, Joshua S., & Georghiou, George E. Data processing and quality verification for improved photovoltaic performance and reliability analytics. United Kingdom. https://doi.org/10.1002/pip.3349
Livera, Andreas, Theristis, Marios, Koumpli, Elena, Theocharides, Spyros, Makrides, George, Sutterlueti, Juergen, Stein, Joshua S., and Georghiou, George E. Wed . "Data processing and quality verification for improved photovoltaic performance and reliability analytics". United Kingdom. https://doi.org/10.1002/pip.3349.
@article{osti_1670826,
title = {Data processing and quality verification for improved photovoltaic performance and reliability analytics},
author = {Livera, Andreas and Theristis, Marios and Koumpli, Elena and Theocharides, Spyros and Makrides, George and Sutterlueti, Juergen and Stein, Joshua S. and Georghiou, George E.},
abstractNote = {Data integrity is crucial for the performance and reliability analysis of photovoltaic (PV) systems, since actual in-field measurements commonly exhibit invalid data caused by outages and component failures. The scope of this paper is to present a complete methodology for PV data processing and quality verification in order to ensure improved PV performance and reliability analyses. Data quality routines (DQRs) were developed to ensure data fidelity by detecting and reconstructing invalid data through a sequence of filtering stages and inference techniques. The obtained results verified that PV performance and reliability analyses are sensitive to the fidelity of data and, therefore, time series reconstruction should be handled appropriately. To mitigate the bias effects of 10% or less invalid data, the listwise deletion technique provided accurate results for performance analytics (exhibited a maximum absolute percentage error of 0.92%). When missing data rates exceed 10%, data inference techniques yield more accurate results. The evaluation of missing power measurements demonstrated that time series reconstruction by applying the Sandia PV Array Performance Model yielded the lowest error among the investigated data inference techniques for PV performance analysis, with an absolute percentage error less than 0.71%, even at 40% missing data rate levels. The verification of the routines was performed on historical datasets from two different locations (desert and steppe climates). The proposed methodology provides a set of standardized analytical procedures to ensure the validity of performance and reliability evaluations that are performed over the lifetime of PV systems.},
doi = {10.1002/pip.3349},
journal = {Progress in Photovoltaics},
number = ,
volume = ,
place = {United Kingdom},
year = {2020},
month = {10}
}

Journal Article:
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https://doi.org/10.1002/pip.3349

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