With the rapid development of the economy, the scale of transmission networks has been expanding, bring higher demands and challenges on transmission line operation and maintenance. In this paper, the multi-rotor unmanned aerial vehicles (UAVs) safety inspection rules and automatic detailed inspection methods for transmission towers are studied. The theoretical model of multi-rotor UAVs of transmission lines inspection is established. The imaging calculation range of multi-rotor UAV-equipped cameras is determined. According to the inspection theory model, the flight safety judgment standard is designed, and the key parts of the transmission lines that need to be inspected are determined according to the accurate model of the transmission tower. Then the inspection waypoints are manually operated, and the photographing position and angle of each waypoint from the flight control are recorded through the waypoint planning function. Finally, the waypoints are connected in order, and the inspection route is automatically generated to achieve automatic detailed inspection. The inspection efficiency, error analysis and position accuracy comparing the proposed method with some state-of-the-art methods have been evaluated. The results show that the position error of UAVs automatic detailed inspection is less than 10 cm. The error of height is between 1.26 and 1.76 meter. Compared with the traditional manual inspection, the efficiency of multi-rotor UAVs automatic detailed inspection can be increased by 57.98%∼62.88% and can be applied to large-scale inspection of transmission lines. INDEX TERMS Transmission lines, unmanned aerial vehicles, inspection, automatic control.

Energy systems, which include energy production, conversion, transportation, distribution and utilization, are key infrastructures in modern society. Interactions among energy systems are generally studied under the framework of energy trade. Although such studies have generated important insights, there are limitations. Many distant interactions (e.g. the Fukushima nuclear crisis) are not in the form of trade, but affect energy sustainability. Even when distant interactions are related to energy trade, they are not systematically analyzed. Environmental impacts of trade are often not integrated with economic analysis of trade. In this paper, to identify and fill important knowledge gaps, we apply an integrated framework of telecoupling (socioeconomic and environmental interactions over distances). The framework of telecoupling, which is more comprehensive and cross-disciplinary than the energy trade framework, is a useful theoretical and methodological tool for analyzing distant interactions among coupled human and natural systems (including energy systems). Telecouplings widely exist in energy systems with various forms and link energy sustainability of different countries closely, so we proposed some methods for energy sustainability analysis under the framework of telecoupling. From the aspect of causes, a method is proposed to judge whether the telecoupling driven by economic factors is conducive to energy sustainability. From the aspect of effects, a method is proposed to assess whether an event is conducive to energy sustainability. The telecoupling framework presents opportunities for more profound and comprehensive understanding of energy sustainability.

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%.

No abstract

No abstract