WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data

Gao, Yao; Zeng, Qiangyu; Liu, Yin; Zhang, Fugui; Wang, Hao; Ren, Zhicheng

doi:10.3390/rs17162763

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Open AccessArticle

WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data

by

Yao Gao

¹,

Qiangyu Zeng

^1,2

,

Yin Liu

^3,4,*

,

Fugui Zhang

¹,

Hao Wang

¹

and

Zhicheng Ren

¹

College of Electronic Engineering, Chengdu University of Information Technology, Chengdu 610225, China

²

China Meteorological Administration Tornado Key Laboratory, Beijing 100871, China

³

Jiangsu Meteorological Observation Center, Nanjing 210041, China

⁴

Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China

^*

Author to whom correspondence should be addressed.

Remote Sens. 2025, 17(16), 2763; https://doi.org/10.3390/rs17162763 (registering DOI)

Submission received: 12 June 2025 / Revised: 31 July 2025 / Accepted: 6 August 2025 / Published: 9 August 2025

(This article belongs to the Section AI Remote Sensing)

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Abstract

With the rapid expansion ofWind Parks (WPs),Wind Turbine Clutter (WTC) has become a significant challenge due to the interference it causes with data from next-generation Doppler weather radars. Traditional clutter detection methods struggle to strike a balance between detection accuracy and efficiency. This study proposes a deep learning model named WTC-MobResNet, which integrates the architectures of MobileNet and ResNet and is specifically designed for WTC detection tasks. The model combines the lightweight characteristics of MobileNet with the residual learning capabilities of ResNet, enabling efficient extraction of WTC features from weather radar echo data and achieving precise identification of WTC. The experimental results demonstrate that the proposed model achieves an ACC of 98.21%, a PRE of 97.52%, a POD of 98.99%, and an F1 score of 98.25%, outperforming several existing deep learning models in both detection accuracy and false alarm control. These results confirm the potential of WTC-MobResNet for real-world operational applications.

Keywords: weather radar; Wind Parks; wind turbine clutter; deep learning; object detection

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MDPI and ACS Style

Gao, Y.; Zeng, Q.; Liu, Y.; Zhang, F.; Wang, H.; Ren, Z. WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data. Remote Sens. 2025, 17, 2763. https://doi.org/10.3390/rs17162763

AMA Style

Gao Y, Zeng Q, Liu Y, Zhang F, Wang H, Ren Z. WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data. Remote Sensing. 2025; 17(16):2763. https://doi.org/10.3390/rs17162763

Chicago/Turabian Style

Gao, Yao, Qiangyu Zeng, Yin Liu, Fugui Zhang, Hao Wang, and Zhicheng Ren. 2025. "WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data" Remote Sensing 17, no. 16: 2763. https://doi.org/10.3390/rs17162763

APA Style

Gao, Y., Zeng, Q., Liu, Y., Zhang, F., Wang, H., & Ren, Z. (2025). WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data. Remote Sensing, 17(16), 2763. https://doi.org/10.3390/rs17162763

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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WTC-MobResNet: A Deep Learning Approach for Detecting Wind Turbine Clutter in Weather Radar Data

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