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Recovering Corrupted Data in Wind Farm Measurements: A Matrix Completion Approach

Author

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  • Mattia Silei

    (Dipartimento di Matematica ed Informatica “Ulisse Dini”, Università degli Studi di Firenze, 50134 Firenze, Italy
    INDAM-GNCS Research Group, 00185 Roma, Italy
    These authors contributed equally to this work.)

  • Stefania Bellavia

    (INDAM-GNCS Research Group, 00185 Roma, Italy
    Dipartimento di Ingegneria Industriale, Università degli Studi di Firenze, 500139 Firenze, Italy
    These authors contributed equally to this work.)

  • Francesco Superchi

    (Dipartimento di Ingegneria Industriale, Università degli Studi di Firenze, 500139 Firenze, Italy
    These authors contributed equally to this work.)

  • Alessandro Bianchini

    (Dipartimento di Ingegneria Industriale, Università degli Studi di Firenze, 500139 Firenze, Italy
    These authors contributed equally to this work.)

Abstract

Availability of reliable and extended datasets of recorded power output from renewables is nowadays seen as one of the key drivers to improve the design and control of smart energy systems. In particular, these datasets are needed to train artificial intelligence methods. Very often, however, datasets can be corrupted due to lack of records connected to failures of the acquisition system, maintenance downtime periods, etc. Several recovery (imputation) methods have been used to guess and replace missing data. In this paper, we exploit the matrix completion approach. The available measures of several variables referring to a real onshore wind farm are organized into a matrix in a daily range and the Singular Value Thresholding method is used to carry out the matrix completion process. Numerical results show that matrix completion is a reliable and parameter-free tuning tool to impute missing data in these applications.

Suggested Citation

  • Mattia Silei & Stefania Bellavia & Francesco Superchi & Alessandro Bianchini, 2023. "Recovering Corrupted Data in Wind Farm Measurements: A Matrix Completion Approach," Energies, MDPI, vol. 16(4), pages 1-32, February.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:4:p:1674-:d:1060989
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    References listed on IDEAS

    as
    1. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    2. Wang, Qiang & Luo, Kun & Yuan, Renyu & Wang, Shuai & Fan, Jianren & Cen, Kefa, 2020. "A multiscale numerical framework coupled with control strategies for simulating a wind farm in complex terrain," Energy, Elsevier, vol. 203(C).
    3. João Pacheco & Francisco Pimenta & Sérgio Pereira & Álvaro Cunha & Filipe Magalhães, 2022. "Fatigue Assessment of Wind Turbine Towers: Review of Processing Strategies with Illustrative Case Study," Energies, MDPI, vol. 15(13), pages 1-25, June.
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