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PV System Performance Evaluation by Clustering Production Data to Normal and Non-Normal Operation

Author

Listed:
  • Odysseas Tsafarakis

    (Copernicus Institute, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands)

  • Kostas Sinapis

    (Solar Energy Application Centre, High Tech Campus 21, 5656AE Eindhoven, The Netherlands)

  • Wilfried G. J. H. M. Van Sark

    (Copernicus Institute, Utrecht University, Heidelberglaan 2, 3584 CS Utrecht, The Netherlands)

Abstract

The most common method for assessment of a photovoltaic (PV) system performance is by comparing its energy production to reference data (irradiance or neighboring PV system). Ideally, at normal operation, the compared sets of data tend to show a linear relationship. Deviations from this linearity are mainly due to malfunctions occurring in the PV system or data input anomalies: a significant number of measurements (named as outliers) may not fulfill this, and complicate a proper performance evaluation. In this paper a new data analysis method is introduced which allows to automatically distinguish the measurements that fit to a near-linear relationship from those which do not (outliers). Although it can be applied to any scatter-plot, where the sets of data tend to be linear, it is specifically used here for two different purposes in PV system monitoring: (1) to detect and exclude any data input anomalies; and (2) to detect and separate measurements where the PV system is functioning properly from the measurements characteristic for malfunctioning. Finally, the data analysis method is applied in four different cases, either with precise reference data (pyranometer and neighboring PV system) or with scattered reference data (in plane irradiance obtained from application of solar models on satellite observations).

Suggested Citation

  • Odysseas Tsafarakis & Kostas Sinapis & Wilfried G. J. H. M. Van Sark, 2018. "PV System Performance Evaluation by Clustering Production Data to Normal and Non-Normal Operation," Energies, MDPI, vol. 11(4), pages 1-19, April.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:4:p:977-:d:141803
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    References listed on IDEAS

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    Cited by:

    1. Wilfried van Sark, 2019. "Photovoltaic System Design and Performance," Energies, MDPI, vol. 12(10), pages 1-6, May.
    2. Bala Bhavya Kausika & Panagiotis Moraitis & Wilfried G. J. H. M. Van Sark, 2018. "Visualization of Operational Performance of Grid-Connected PV Systems in Selected European Countries," Energies, MDPI, vol. 11(6), pages 1-10, May.
    3. Mariusz T. Sarniak, 2020. "Researches of the Impact of the Nominal Power Ratio and Environmental Conditions on the Efficiency of the Photovoltaic System: A Case Study for Poland in Central Europe," Sustainability, MDPI, vol. 12(15), pages 1-15, July.
    4. Laila Zemite & Jevgenijs Kozadajevs & Leo Jansons & Ilmars Bode & Egils Dzelzitis & Karina Palkova, 2024. "Integrating Renewable Energy Solutions in Small-Scale Industrial Facilities," Energies, MDPI, vol. 17(11), pages 1-19, June.
    5. Odysseas Tsafarakis & Kostas Sinapis & Wilfried G. J. H. M. van Sark, 2019. "A Time-Series Data Analysis Methodology for Effective Monitoring of Partially Shaded Photovoltaic Systems," Energies, MDPI, vol. 12(9), pages 1-18, May.
    6. Pedro Branco & Francisco Gonçalves & Ana Cristina Costa, 2020. "Tailored Algorithms for Anomaly Detection in Photovoltaic Systems," Energies, MDPI, vol. 13(1), pages 1-21, January.
    7. Julián Ascencio-Vásquez & Jakob Bevc & Kristjan Reba & Kristijan Brecl & Marko Jankovec & Marko Topič, 2020. "Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy," Energies, MDPI, vol. 13(9), pages 1-12, May.

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