Prediction of Icing on Wind Turbines Based on SCADA Data via Temporal Convolutional Network

Yujie Zhang, Nasser Kehtarnavaz, Mario Rotea () and Teja Dasari
Additional contact information
Yujie Zhang: Center for Wind Energy, Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
Nasser Kehtarnavaz: Department of Electrical and Computer Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
Mario Rotea: Center for Wind Energy, Department of Mechanical Engineering, University of Texas at Dallas, Richardson, TX 75080, USA
Teja Dasari: Xcel Energy, Minneapolis, MN 55401, USA

Energies, 2024, vol. 17, issue 9, 1-13

Abstract: Icing on the blades of wind turbines during winter seasons causes a reduction in power and revenue losses. The prediction of icing before it occurs has the potential to enable mitigating actions to reduce ice accumulation. This paper presents a framework for the prediction of icing on wind turbines based on Supervisory Control and Data Acquisition (SCADA) data without requiring the installation of any additional icing sensors on the turbines. A Temporal Convolutional Network is considered as the model to predict icing from the SCADA data time series. All aspects of the icing prediction framework are described, including the necessary data preprocessing, the labeling of SCADA data for icing conditions, the selection of informative icing features or variables in SCADA data, and the design of a Temporal Convolutional Network as the prediction model. Two performance metrics to evaluate the prediction outcome are presented. Using SCADA data from an actual wind turbine, the model achieves an average prediction accuracy of 77.6 % for future times of up to 48 h.

Keywords: wind energy; prediction of icing on wind turbines; Temporal Convolutional Network (TCN) (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/17/9/2175/pdf (application/pdf)
https://www.mdpi.com/1996-1073/17/9/2175/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:17:y:2024:i:9:p:2175-:d:1387672

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().