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Cross-wind state evaluation of wind turbine based on multi-sensor data fusion
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Yongbo Li1, Zhen Liu1, 2, 3, Jianwen Wang2, 4, Mengnan Zheng1, Hongyu Liu1
Renewable Energy Resources | 2026, 44(1) : 70 - 77
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Renewable Energy Resources | 2026, 44(1): 70-77
Cross-wind state evaluation of wind turbine based on multi-sensor data fusion
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Yongbo Li1, Zhen Liu1, 2, 3, Jianwen Wang2, 4, Mengnan Zheng1, Hongyu Liu1
Affiliations
  • 1.Mechanical Engineering College, Inner Mongolia University of Technology, Huhhot 010000, China
  • 2.Key Laboratory of Wind Energy and Solar Energy Utilization Technology, Huhhot 010000, China
  • 3.Inner Mongolia Key Laboratory of Robotics and Intelligent Equipment Technology, Huhhot 010000, China
  • 4.Energy and Power Engineering College, Inner Mongolia University of Technology, Huhhot 010000, China
Published: 2026-01-20
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In order to explore the strain characteristics of wind turbine blades during continuous crosswind, a multi -sensor data fusion method for wind turbine crosswind state evaluation is proposed in this study. In this method, ensemble Empirical Mode Decomposition (EMD) and composite multi-scale permutation entropy-wavelet algorithm are used to jointly reduce the noise of wind turbine blade strain signals, and then Kernel Principal Component Analysis (KPCA) is used to integrate the multiple groups of strain signals after noise reduction. The Squared prediction error (SPE) statistic was used as the evaluation index to effectively divide the wind turbine crosswind process. The results show that the proposed method is effective in reducing the noise of non-stationary wind turbine blade strain signal, and can accurately reflect the strain variation law in continuous crosswind process. In addition, through the combination of KPCA and SPE statistics, the process of wind turbine crosswind is segmtioned, and the operating state of wind turbine crosswind under different influencing factors is analyzed.

blade strain  /  continuous crosswind process  /  noise reduction processing  /  multi-sensor data fusion
李勇博, 刘珍, 汪建文, 郑梦楠, 刘鸿宇. 多传感器数据融合的风力机侧风状态评估. 可再生能源, 2026 , 44 (1) : 70 -77 .
Yongbo Li, Zhen Liu, Jianwen Wang, Mengnan Zheng, Hongyu Liu. Cross-wind state evaluation of wind turbine based on multi-sensor data fusion[J]. Renewable Energy Resources, 2026 , 44 (1) : 70 -77 .
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Year 2026 volume 44 Issue 1
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Article Info
  • Receive Date：2024-09-02
  • Online Date：2026-09-23
  • Published：2026-01-20
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  • Received：2024-09-02
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Affiliations
    1.Mechanical Engineering College, Inner Mongolia University of Technology, Huhhot 010000, China
    2.Key Laboratory of Wind Energy and Solar Energy Utilization Technology, Huhhot 010000, China
    3.Inner Mongolia Key Laboratory of Robotics and Intelligent Equipment Technology, Huhhot 010000, China
    4.Energy and Power Engineering College, Inner Mongolia University of Technology, Huhhot 010000, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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