Short-Term Wind Turbine Blade Icing Wind Power Prediction Based on PCA-fLsm

Fan Cai, Yuesong Jiang (), Wanqing Song, Kai-Hung Lu () and Tongbo Zhu
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Fan Cai: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Yuesong Jiang: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Wanqing Song: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Kai-Hung Lu: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Tongbo Zhu: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China

Energies, 2024, vol. 17, issue 6, 1-15

Abstract: To enhance the economic viability of wind energy in cold regions and ensure the safe operational management of wind farms, this paper proposes a short-term wind turbine blade icing wind power prediction method that combines principal component analysis (PCA) and fractional Lévy stable motion (fLsm). By applying supervisory control and data acquisition (SCADA) data from wind turbines experiencing icing in a mountainous area of Yunnan Province, China, the model comprehensively considers long-range dependence (LRD) and self-similar features. Adopting a combined pattern of previous-day predictions and actual measurement data, the model predicts the power under near-icing conditions, thereby enhancing the credibility and accuracy of icing forecasts. After validation and comparison with other prediction models (fBm, CNN-Attention-GRU, XGBoost), the model demonstrates a remarkable advantage in accuracy, achieving an accuracy rate and F1 score of 96.86% and 97.13%, respectively. This study proves the feasibility and wide applicability of the proposed model, providing robust data support for reducing wind turbine efficiency losses and minimizing operational risks.

Keywords: fan blades; principal component analysis; fractional Lévy stable motion; long-range dependence; ice prediction (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
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