Corona Detection and Power Equipment Classification Based on GoogleNet-AlexNet: An Accurate and Intelligent Defect Detection Model Based on Deep Learning for Power Distribution Lines
Corona Detection and Power Equipment Classification Based on GoogleNet-AlexNet: An Accurate and Intelligent Defect Detection Model Based on Deep Learning for Power Distribution Lines
复制标题
DOI：
10.1109/tpwrd.2021.3116489
复制
发表时间：
2022-08-01
影响因子：
4.4
通讯作者：
Mashhour, Elaheh
中科院分区：
文献类型：
--
作者：
Davari, Noushin;Akbarizadeh, Gholamreza;Mashhour, Elaheh
本文提出了一种基于深度学习的配电线路缺陷检测与分类方法。第一阶段是数据集准备，CoroCam 6D2摄像头从配电线记录不同的视频，并根据缺陷类型和缺陷严重程度进行标记。然后，以有限数量的帧用于处理的方式进行处理，并使用Faster R-CNN在每个帧中检测功率设备。通过增加训练数据集和改变训练超参数来提高检测器的性能。接下来，通过视频帧应用设备跟踪技术。在下文中，提出了一种基于电力设备分类的方法。在该方法中，通过在所有帧中进行电晕颜色阈值化并随着时间的推移应用中值滤波器，识别表示电晕的连接分量。然后，选择包含与中值图像中的分量最接近的分量的帧。在选定的框架中，组件周围的区域被切割并提供给AlexNet或GoogleNet以确定设备类型，并确定缺陷的严重程度。此外，基于在视频中检测到的绝缘子来确定故障设备相。所提出的方法不仅表现得比最先进的，但也是一种实用的方法，并与最小的依赖于环境条件，可以自动识别配电线路中的缺陷，即使在视频包含几个可能的缺陷设备。
This paper presents a deep learning-based method for defect detection and classification of power distribution lines using video analysis. The first stage is dataset preparation in which different videos are recorded by a CoroCam 6D2 camera from distribution lines and labeled based on the defect type and the severity level of the defect. Then, a process is followed in such a way that a limited number of frames are used for processing, and power devices are detected in each frame using Faster R-CNN. The detector performance is improved by increasing the training dataset and changing the training hyper-parameters. Next, an equipment tracking technique is applied through the video frames. In the following, a method based on the classification of power equipment is presented. In this method, by corona color thresholding in all frames and applying a median filter over time, the connected components representing the corona are identified. Then, the frame containing the closest component to the component in the median image is selected. In the selected frame, the area around the component is cut and given to AlexNet or GoogleNet to determine the equipment type and the severity level of defect is determined. Also, the defective equipment phase is determined based on the insulators detected in the video. The proposed method not only performs better than the state-of-the-art but also is a practical method, and with the least dependence on environmental conditions, can automatically identify defects in distribution lines, even in videos containing several possible defective devices.