RESEARCH ON WIND TURBINE BLADE FAULT DETECTION TECHNOLOGY BASED ON MACHINE VISION

Zhu Enlong; Feng Congcong; Sheng Zhenteng; Shi Tianyu; Qi Hao; Sun Bowen

doi:10.19912/j.0254-0096.tynxb.2022-0787

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Acta Energiae Solaris Sinica ›› 2023, Vol. 44 ›› Issue (4) : 209-215. DOI: 10.19912/j.0254-0096.tynxb.2022-0787

RESEARCH ON WIND TURBINE BLADE FAULT DETECTION TECHNOLOGY BASED ON MACHINE VISION

Zhu Enlong^1,2, Feng Congcong¹, Sheng Zhenteng³, Shi Tianyu¹, Qi Hao¹, Sun Bowen¹

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Abstract

A fault diagnosis method based on machine vision feature classification is proposed for wind turbine blades in operation. By circularly marking the blade tip, images of the blade tip are acquired periodically using an industrial camera and pre-processed on Halcon software, and images acquired in foggy weather are clarified using a dark channel defogging algorithm. A leaf tip marker detection algorithm is used to extract markers, calculation area features such as area roundness and area centre. The markers on the adjacent blades are then compared with the system's warning threshold to determine the degree of blade deformation and fault trend in the direction of torsion or deflection, thus enabling online detection and adaptive warning of the variable operating conditions of wind turbine blades.

Key words

wind turbine blades / fault detection / image processing / machine vision / feature classification

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Zhu Enlong, Feng Congcong, Sheng Zhenteng, Shi Tianyu, Qi Hao, Sun Bowen. RESEARCH ON WIND TURBINE BLADE FAULT DETECTION TECHNOLOGY BASED ON MACHINE VISION[J]. Acta Energiae Solaris Sinica. 2023, 44(4): 209-215 https://doi.org/10.19912/j.0254-0096.tynxb.2022-0787

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