Abstract
Big data technology is more and more widely used in modern power systems. Efficient collection of big data such as equipment status, maintenance and grid operation in power systems, and data mining are the important research topics for big data application in smart grid. In this paper, the application of big data technology in fast image recognition of transmission towers which are obtained using fixed-wing unmanned aerial vehicle (UAV) by large range tilt photography are researched. A method that using fast region-based convolutional neural networks (Rcnn) convolutional architecture for fast feature embedding (Caffe) to get deep learning of the massive transmission tower image, extract the image characteristics of the tower, train the tower model, and quickly recognize transmission tower image to generate power lines is proposed. The case study shows that this method can be used in tree barrier modeling of transmission lines, which can replace artificial identification of transmission tower, to reduce the time required for tower identification and generating power line, and improve the efficiency of tree barrier modeling by around 14.2%.
Highlights
Big data technology has become a hot topic in recent years
This paper proposed a method of fast image recognition of transmission towers based on big data, which can greatly improve the efficiency of large-scale tree barrier modeling
Where T1 is the time of taking tilt photography photos of transmission lines, T2 is the time of artificial identification of transmission tower, T3 is the time of generating power line, T4 is the time of point cloud classification and T5 is the time of measuring the distance between trees and wires
Summary
Big data technology has become a hot topic in recent years. It refers to the technical system or technology framework of extracting, discovering and analyzing data from large numbers of different types and sources, and extracting their value using economic methods [1,2,3,4]. Machine deep learning can be applied to image recognition by first making training sets using massive pictures, and constantly training to get the weight of the neural network and forming the model. This paper proposed a method of fast image recognition of transmission towers based on big data, which can greatly improve the efficiency of large-scale tree barrier modeling.
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