An Image Processing Method for Dynamic Monitoring of Wind Turbine Blade Operation Based on Ground Multi-Synchronous Camera Capture

IF 1.5 4区 计算机科学 Q3 COMPUTER SCIENCE, SOFTWARE ENGINEERING
Wenbo Wu, Yanbin Liu, Qiming Yang, Shuang Zhou, Yinggu Wu, Derui Gao, Shouxiao Ma
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Abstract

With the increase in the capacity of wind turbine units, the length of their blades has significantly grown. Existing machine vision-based image acquisition methods are unable to capture the full view of the blades, leading to the inability to accurately monitor the operational status of wind turbine blades dynamically. Therefore, this study proposes an image processing method for dynamic monitoring of wind turbine blade operation based on point and line features from images captured by ground multi-synchronous cameras. After image dehazing filtering and edge detection, the method utilizes line detection, so as to extract line features. Additionally, on the basis of improved Harris and Scale-Invariant Feature Transform (SIFT) registration, constraints such as line feature constraints, parallel constraints, and equidistant intercept constraints are incorporated for blade stitching. This process involves stitching fragmented images into a complete image, along with image enhancement, and assessing seam smoothness using root mean square error. Results indicate that this method can effectively capture and retain complete blade information, particularly blade edge information and blade damage information. The method proposed in this paper integrates machine vision, image recognition, and trajectory tracking technologies to construct a database of blade operating conditions and images. It is capable of operating in hazy environments, enabling accurate monitoring of blade operating conditions, supporting intelligent maintenance of wind farms, improving resource utilization, and reducing operating costs. Compared with existing methods, it has certain advantages. The research findings are expected to provide valuable data support for detecting potential defects or damages on the blades using machine vision-based methods.

基于地面多同步摄像机捕获的风电叶片动态监测图像处理方法
随着风力发电机组容量的增加,其叶片的长度也有了显著的增长。现有的基于机器视觉的图像采集方法无法捕捉到叶片的全貌,导致无法准确动态监测风机叶片的运行状态。因此,本研究提出了一种基于地面多同步摄像机采集图像的点线特征的风力发电机叶片运行动态监测的图像处理方法。该方法在对图像进行去雾滤波和边缘检测后,利用线检测,提取线特征。此外,在改进的Harris和尺度不变特征变换(Scale-Invariant Feature Transform, SIFT)配准的基础上,引入了线特征约束、平行约束和等距截距约束等约束进行叶片拼接。该过程包括将碎片图像拼接成完整的图像,以及图像增强,并使用均方根误差评估接缝平滑度。结果表明,该方法可以有效地捕获和保留完整的叶片信息,特别是叶片边缘信息和叶片损伤信息。本文提出的方法将机器视觉、图像识别和轨迹跟踪技术相结合,构建叶片工况和图像数据库。能够在雾霾环境下运行,准确监测叶片运行状态,支持风电场智能维护,提高资源利用率,降低运行成本。与现有方法相比,具有一定的优势。研究结果有望为使用基于机器视觉的方法检测叶片的潜在缺陷或损伤提供有价值的数据支持。
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
Concurrency and Computation-Practice & Experience
Concurrency and Computation-Practice & Experience 工程技术-计算机:理论方法
CiteScore
5.00
自引率
10.00%
发文量
664
审稿时长
9.6 months
期刊介绍: Concurrency and Computation: Practice and Experience (CCPE) publishes high-quality, original research papers, and authoritative research review papers, in the overlapping fields of: Parallel and distributed computing; High-performance computing; Computational and data science; Artificial intelligence and machine learning; Big data applications, algorithms, and systems; Network science; Ontologies and semantics; Security and privacy; Cloud/edge/fog computing; Green computing; and Quantum computing.
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