Dynamic RCS Statistical Model of Wind Turbine Blades Driven by Knowledge and Data

WANG Xiaoliang; SHI Yuxiang; HE Weikun

doi:10.11999/JEIT221242

Volume 45 Issue 11

Nov. 2023

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WANG Xiaoliang, SHI Yuxiang, HE Weikun. Dynamic RCS Statistical Model of Wind Turbine Blades Driven by Knowledge and Data[J]. Journal of Electronics & Information Technology, 2023, 45(11): 3887-3895. doi: 10.11999/JEIT221242

Citation:

WANG Xiaoliang, SHI Yuxiang, HE Weikun. Dynamic RCS Statistical Model of Wind Turbine Blades Driven by Knowledge and Data[J]. Journal of Electronics & Information Technology, 2023, 45(11): 3887-3895. doi: 10.11999/JEIT221242

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Dynamic RCS Statistical Model of Wind Turbine Blades Driven by Knowledge and Data

doi: 10.11999/JEIT221242

Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China

Funds: The National Natural Science Foundation of China (62141108), The Fundamental Research Funds for the Central Universities (3122022085)

Received Date: 2022-09-26
Rev Recd Date: 2023-07-05

Available Online: 2023-07-11

Publish Date: 2023-11-28

Abstract

Abstract

To address the problem of dynamic Radar Cross Section (RCS) estimation of a wind turbine when assessing the wind farm impact on radars and other equipment, a dynamic RCS statistical model of wind turbines driven by knowledge and data is proposed. First, the blades’ RCS variation function in a monotonic variation interval is established using the periodic variation characteristics of the wind turbine blades’ RCS with the blade rotation. The variation function comprises a peak RCS related to the blade geometry, a modulation function independent of the blade geometry and a multiplicative factor related to material and shape details. The peak RCS is calculated from the theoretical model. Furthermore, the modulation function and the multiplicative factor are estimated using the practically measured training data because of the complex characteristics of the RCS variation. Second, for the estimation of a wind turbine statistical model, the blades’ RCS variation function is obtained according to the geometric parameters. Then the statistical model of the probability density function is calculated using the parameter estimation method. The experimental results of the practically measured data of various types of wind turbines verify the dynamic RCS statistical model of the wind turbine blades proposed in this paper, and the model is in good agreement with the practically measured data results.
- RCS estimation,
- Wind turbine,
- Data-driven approach,
- Physical optics method,
- Kullback-Leibler divergence

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References(21)

References

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