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A Method of Transmission Conductor-Loosened Detect Based on Image Sensors

机译:基于图像传感器的传输导体松开检测方法

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The conductor-loosened fault of the transmission line caused by its own mechanical load, breeze vibration, and galloping will gradually worsen by the passage of time and even lead to a series of irreversible accidents such as conductor breakage and tower fall. In order to detect the loosened conductors as soon as possible and minimize the possible damages, a detection method for the loosened conductor based on the vision sensor is proposed. First, the conductor images are captured by the image acquisition system, using the inspection robot, and then, the energy gradient algorithm with guided filtering (GEG) is performed to highlight the foreground and strengthen the hierarchy, so that the conductor area can be obtained by the Otsu threshold segmentation. Second, the surface contour of the conductor is extracted, filtered, and numbered. Finally, according to the arrangement characteristics of the outermost Al-strands of the conductor, we established a conductor-loosened model that includes the analysis of the four feature quantities of contour length D, direction angle theta, curvature W, and spacing T and the thinning process based on the step-vertical limit (SVL) to judge the conductor fault and locate the loosened strands. We analyze the performance of the technology by tests, and the results demonstrate that the proposed method can quickly detect the conductor-loosened fault and achieve good performance.
机译:通过其自身机械负荷,微风振动和疾驰引起的传输线的导体松动故障将通过时间的推移逐渐恶化,甚至导致一系列不可逆转的事故,如导体破损和塔下降。为了尽快检测松动的导体并最小化可能的损坏,提出了基于视觉传感器的松开导体的检测方法。首先,使用检查机器人捕获导体图像,然后使用检查机器人捕获,然后执行具有引导滤波(GEG)的能量梯度算法以突出显示前景并加强层次结构,从而可以获得导体区域通过OTSU阈值分割。其次,提取导体的表面轮廓,过滤,并编号。最后,根据导体的最外侧股的布置特性,我们建立了导体松开模型,该​​模型包括分析轮廓长度D,方向角θ,曲率W和间隔T和间隔T的分析基于阶梯垂直极限(SVL)的减薄过程来判断导体故障并定位松散的股线。我们通过测试分析技术的性能,结果表明,该方法可以快速检测导体松动的故障并实现良好的性能。

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