KEYWORDS: Point clouds, LIDAR, Data transmission, Inspection, 3D modeling, Remote sensing, Data modeling, Optical transmission, 3D image processing, Laser scanners
Facing the extraction of hidden danger information of external breakage to transmission lines, we first introduce lidar and optical remote sensing image data acquisition, processing, and information extraction technologies, then analyze the characteristics of transmission corridors obtained by these two sensing methods, and summarize their respective advantages and disadvantages. On this basis, we propose a method for external breakage hidden danger information of transmission lines by combining remote sensing images and LiDAR point clouds. This method can simultaneously acquire the texture and spatial three-dimensional data of the external breakage target in the transmission corridor, and form high-quality spatial three-dimensional model data, which is conducive to the effective identification and accurate extraction of external breakage hidden danger.
In recent years, due to the shortage of transmission line channel resources, power companies have begun to expand the capacity of existing important transmission lines. The safe and stable operation of important transmission lines is critical to the power supply status of users. Timely monitoring of potential hazards of construction machinery near the transmission lines has become a hot topic of transmission line protection against external damage. Aiming at the technical bottleneck of high false alarm rate of the current transmission line using online monitoring camera, this paper proposes a construction vehicle detection method based on the fusion of radar and visual features, which uses the physical features and geometric features of the target. The physical features such as velocity and acceleration are selected from radar. After the fusion of the radar data and camera data, the region of interest (ROI) of the radar target on the image is obtained, and the gradient direction histogram feature is extracted on the ROI. The visual features are calculated by the statistical features of gradient direction histogram, including standard deviation, median and average. This paper constructs a neural network R-V-DenseNet whose input is the fusion feature of radar and vision. Then a data set is made to train the network. The experimental results on the test set prove that the accuracy of R-V-DenseNet is improved compared with the traditional HOG-SVM method and the single sensor based detection method, which means the proposed method gains more accurate detection.
Wind-induced deflection of overhead transmission line is a phenomenon of conductor non-synchronous swing caused by strong wind. Serious wind deflection will cause flashover and lead to line trip. It is of great significance to strengthen the monitoring of wind deflection of transmission line insulator. For transmission lines located in cold and strong wind areas, due to the need for on-site power supply, the long-term operation reliability of monitoring devices in low temperature environment is difficult to ensure. The monitoring scheme based on Fiber Bragg grating technology has the advantages of no on-site power supply, anti-electromagnetic interference and good insulation performance. It has a good application prospect in wind deflection monitoring of transmission lines in cold areas. This paper analyzes the characteristics of wind load, the causes of wind deflection of transmission line, and introduces the basic principle of measuring insulator inclination when using fiber Bragg grating sensor, which provides technical support for wind deflection monitoring of transmission line in cold areas and ensures the structural safety, safe and stable operation of transmission line.
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