利用深度神经网络对电网高压铁塔绝缘子清洁度进行自动分类

Hericles Ferraz, Rogério Sales Gonçalves, Breno Batista Moura, Daniel Edgardo Tió Sudbrack, Paulo Victor Trautmann, Bruno Clasen, Rafael Zimmermann Homma, Reinaldo A. C. Bianchi
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引用次数: 0

摘要

组串绝缘子是高压电塔中的部件,负责防止能量通过电塔结构散失,即负责隔离电网电缆中的高压。这些组串绝缘子必须保持清洁,以获得最佳性能并避免出现故障。验证清洁/清洗的必要性通常是通过人的肉眼观察来完成的,这可能会导致解释错误,还会给这些电气系统附近的人的身体完整性带来风险。因此,本文旨在开发一种用于检测和分类这些绝缘体的算法。所提出的算法采用人工智能技术,对图像进行分析,推断出被分析绝缘体的清洁状态。由于脏绝缘子串的图像存在局限性,为开发该算法,有必要使用 CAD 软件(如 Inventor 和 Unity-3D)建立一个合成数据库。本文使用监督学习技术建立了两个不同的神经网络,第一个用于检测绝缘子链,第二个用于检测磁盘表面污垢的类型。在第一阶段,研究了使用监督学习的技术,更明确地针对语义分割网络;在第二阶段,使用分类深度神经网络检测杂质类型。在检测绝缘子串时,模拟图像的平均骰子系数为 0.95,自然图像的平均骰子系数为 0.92,学习参数基于仅有模拟图像的数据库。在污垢分类阶段获得的平均准确率为 0.98。
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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Automated classification of electrical network high-voltage tower insulator cleanliness using deep neural networks

String insulators are components in high-voltage towers responsible for preventing energy dissipation through the tower structure; that is, they are responsible for isolating the high voltage in the electrical network cables. These string insulators must be clean for best performance and to avoid malfunctions. Verifying the necessity for cleaning/washing is most often performed by human visual observation, which can lead to interpretation errors, in addition to bringing risks to the physical integrity of humans in the vicinity of these electrical systems. Thus, this paper aims to develop an algorithm to detect and classify these insulators. The proposed algorithm uses artificial intelligence techniques and analyzes the image, inferring the state of cleanliness of the analyzed insulator. For the development of this algorithm, it was necessary to build a synthetic database using CAD software such as Inventor and Unity-3D due to image limitations available from dirty insulator strings. In this paper, two distinct neural networks are built using supervised learning techniques, where the first one is for detecting the chain of insulators, and the second is for detecting the type of dirt on the disk surface. In the first stage, techniques that use supervised learning are studied, more aimed explicitly at semantic segmentation networks, and in the second stage, classification deep neural networks were used to detect the type of impurities. In detecting insulator strings, an average dice coefficient of 0.95 was achieved for simulated images and 0.92 for natural images, with learning parameters based on a database with only simulated images. The average accuracy obtained in the dirt classification stage was 0.98.

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来源期刊
CiteScore
3.80
自引率
5.90%
发文量
50
期刊介绍: The International Journal of Intelligent Robotics and Applications (IJIRA) fosters the dissemination of new discoveries and novel technologies that advance developments in robotics and their broad applications. This journal provides a publication and communication platform for all robotics topics, from the theoretical fundamentals and technological advances to various applications including manufacturing, space vehicles, biomedical systems and automobiles, data-storage devices, healthcare systems, home appliances, and intelligent highways. IJIRA welcomes contributions from researchers, professionals and industrial practitioners. It publishes original, high-quality and previously unpublished research papers, brief reports, and critical reviews. Specific areas of interest include, but are not limited to:Advanced actuators and sensorsCollective and social robots Computing, communication and controlDesign, modeling and prototypingHuman and robot interactionMachine learning and intelligenceMobile robots and intelligent autonomous systemsMulti-sensor fusion and perceptionPlanning, navigation and localizationRobot intelligence, learning and linguisticsRobotic vision, recognition and reconstructionBio-mechatronics and roboticsCloud and Swarm roboticsCognitive and neuro roboticsExploration and security roboticsHealthcare, medical and assistive roboticsRobotics for intelligent manufacturingService, social and entertainment roboticsSpace and underwater robotsNovel and emerging applications
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