An experimental study on the identification of the root bolts' state of wind turbine blades using blade sensors

IF 5.4 3区材料科学 Q2 CHEMISTRY, PHYSICAL

ACS Applied Energy Materials Pub Date : 2024-01-24 DOI:10.1002/we.2892

Feng Gao, Chen Qian, Lin Xu, Juncheng Liu, Hong Zhang

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引用次数: 0

Abstract

Bolt looseness may occur on wind turbine (WT) blades exposed to operational and environmental variability conditions, which sometimes can cause catastrophic consequences. Therefore, it is necessary to monitor the loosening state of WT blade root bolts. In order to solve this problem, this paper proposes a method to monitor the looseness of blade root bolts using the sensors installed on the WT blade. An experimental platform was first built by installing acceleration and strain sensors for monitoring bolt looseness. Through the physical experiment of blade root bolts' looseness, the response data of blade sensors is then obtained under different bolt looseness numbers and degrees. Afterwards, the sensor signal of the blade root bolts is analyzed in time domain, frequency domain, and time‐frequency domain, and the sensitivity features of various signals are extracted. So the eigenvalue category as the input of the state discrimination model was determined. The LightGBM (light gradient boosting machine) classification algorithm was applied to identify different bolt looseness states for the multi‐domain features. The impact of different combinations of sensor categories and quantities as the data source on the identification results is discussed, and a reference for the selection of sensors is provided. The proposed method can discriminate four bolt states at an accuracy of around 99.8% using 5‐fold cross‐validation.

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利用叶片传感器识别风力涡轮机叶片根部螺栓状态的实验研究

暴露在运行和环境变化条件下的风力涡轮机（WT）叶片可能会出现螺栓松动，有时会造成灾难性后果。因此，有必要对风力涡轮机叶片根部螺栓的松动状态进行监测。为了解决这个问题，本文提出了一种利用安装在 WT 叶片上的传感器监测叶片根部螺栓松动情况的方法。首先通过安装加速度和应变传感器搭建了一个用于监测螺栓松动情况的实验平台。通过叶片根部螺栓松动的物理实验，获得不同螺栓松动数量和松动程度下叶片传感器的响应数据。然后，对叶片根部螺栓的传感器信号进行时域、频域和时频域分析，提取各种信号的灵敏度特征。从而确定了作为状态判别模型输入的特征值类别。应用 LightGBM（光梯度提升机）分类算法来识别多域特征的不同螺栓松动状态。讨论了作为数据源的传感器类别和数量的不同组合对识别结果的影响，并为传感器的选择提供了参考。通过 5 倍交叉验证，所提出的方法可识别四种螺栓状态，准确率约为 99.8%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

ACS Applied Energy Materials Materials Science-Materials Chemistry

CiteScore

10.30

自引率

6.20%

发文量

1368

期刊介绍： ACS Applied Energy Materials is an interdisciplinary journal publishing original research covering all aspects of materials, engineering, chemistry, physics and biology relevant to energy conversion and storage. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrate knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important energy applications.