Overhead high-voltage power lines are key components of power transmission and their monitoring has a very significant influence on security and reliability of power system. Advanced laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of power system scenes. Nevertheless, power line corridors are found in increasingly complex environments (e.g., mountains and forests), and the multi-loop structure on the same power line tower raises great challenges to process light detection and ranging (LiDAR) data. This paper addresses these challenges by constructing a new collection mode of LiDAR data for power lines using cable inspection robot (CIR). A novel method is proposed to extract and reconstruct power line using CIR LiDAR data, which has two advantages: (1) rapidly extracts power line point by position and orientation system (POS) extraction model; and (2) better solves pseudo-line during reconstruction of power line by structured partition. The proposed method mainly includes four steps: CIR LiDAR data generation, POS-based crude extraction, voxel-based accurate extraction and power line reconstruction. The feasibility and validity of the proposed method are verified by test site experiment and actual line experiment, demonstrating a fast and reliable solution to accurately reconstruct power line.

With the growth of the national economy, there is increasing demand for electricity, which forces transmission line corridors to become structurally complicated and extend to complex environments (e.g., mountains, forests). It is a great challenge to inspect transmission line in these regions. To address these difficulties, a novel method of autonomous inspection for transmission line is proposed based on cable inspection robot (CIR) LiDAR data, which mainly includes two steps: preliminary inspection and autonomous inspection. In preliminary inspection, the position and orientation system (POS) data is used for original point cloud dividing, ground point filtering, and structured partition. A hierarchical classification strategy is established to identify the classes and positions of the abnormal points. In autonomous inspection, CIR can autonomously reach the specified points through inspection planning. These inspection targets are imaged with PTZ (pan, tilt, zoom) cameras by coordinate transformation. The feasibility and effectiveness of the proposed method are verified by test site experiments and actual line experiments, respectively. The proposed method greatly reduces manpower and improves inspection accuracy, providing a theoretical basis for intelligent inspection of transmission lines in the future.

Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from “layer” to “block” according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection.

Abstract. To address the problems of existing power line inspection robots (PLIRs), such as complex structural design, difficult landing on and off lines, short cruise times, and easy hitting lines, we propose a novel flying–walking power line inspection robot (FPLIR) with the ability to fly and walk. The structural design of an FPLIR is carried out, which mainly includes a flying mechanism and a walking mechanism. Compared with climbing PLIRs and unmanned aerial vehicles (UAVs), the FPLIR can quickly land on and off lines, easily cross obstacles, and have longer cruise times and steady inspection perspectives. In addition, a directing-push pressing component is designed to improve the walking stability along the line. We also investigate the walking stability of the FPLIR on the line when encountering working conditions with crossing wind. The dynamics model of the FPLIR on the power line using the Lagrangian equation is derived to analyze walking stability caused by wind loads, considering pressing force and walking speed. An optimized regression design with three factors (wind angle, walking speed, and pressing force) and five levels was adopted to reveal the effect of these factors on the walking stability of the FPLIR. The experimental results show that wind angle and pressing force significantly influence the walking stability of the FPLIR (P<0.05). The maximum swing displacement of the center of mass (COM) is 4.7 cm (when wind angle, walking speed, and pressing force are 90∘, 7.2 m min−1, and 0, respectively). The maximum swing displacement of the COM is 2.5 cm when the pressing force increasing to 39.4 N is reduced by 46.2 % (when wind angle and walking speed are 90∘ and 5.1 m min−1, respectively), which effectively reduces the influence of wind loads and improves the stability of the FPLIR. The proposed FPLIR significantly improves inspection stability, providing a theoretical basis for slipping control, collecting images of intelligence inspection robots in the future.

To address complex work conditions incredibly challenging to the stability of power line inspection robots, we design a walking mechanism and propose a variable universe fuzzy control (VUFC) method based on multi-work conditions for flying-walking power line inspection robots (FPLIRs). The contributions of this paper are as follows: (1) A flexible pressing component is designed to improve the adaptability of the FPLIR to the ground line slope. (2) The influence of multi-work conditions on the FPLIR's walking stability is quantified using three condition parameters (i.e., slope, slipping degree and swing angle), and their measurement methods are proposed. (3) The VUFC method based on the condition parameters is proposed to improve the walking stability of the FPLIR. Finally, the effect of the VUFC method on walking stability of the FPLIR is teste. The experimental results show that the maximum climbing angle of the FPLIR reaches 29.1°. Compared with the constant pressing force of 30 N, the average value of slipping degree is 0.93°, increasing by 35%. The maximum and average values of robot's swing angle are reduced by 46% and 54%, respectively. By comparing with fuzzy control, the VUFC can provide a more reasonable pressing force while maintaining the walking stability of the FPLIR. The proposed walking mechanism and the VUFC method significantly improve the stability of the FPLIR, providing a reference for structural designs and stability controls of inspection robots.

Background The coronavirus disease 2019 (COVID-19) pandemic continues to expand. Herein, we report the epidemiological and clinical features of 478 patients with confirmed COVID-19 from a multicenter study conducted in four cities in China excluding Wuhan. Methods A total of 478 patients transferred by emergency medical services to designated hospitals in four major cities in China (Beijing, Chongqing, Jinan, and Nanning) were enrolled. We compared the characteristics of imported and indigenous cases and calculated the frequencies of fatal, severe, mild, and asymptomatic disease. The results were used to generate a pyramid of COVID-19 severity. Results The mean age of patients with COVID-19 was 46.9 years and 49.8% were male. The most common symptoms at onset were fever (69.7%), cough (47.5%), fatigue (24.5%), dyspnea (8.4%), and headache (7.9%). Most cases (313, 65.5%) were indigenous, while 165 (34.5%) were imported. Imported cases dominated during the early stages of the pandemic, but decreased from 1 February 2020 as indigenous cases rose sharply. Compared with indigenous cases, imported cases differed significantly in terms of sex (P = 0.002), severity of disease (P = 0.006), occurrence of fever (P < 0.001), family clustering (P < 0.001), history of contact (P < 0.001), and primary outcome (P < 0.001). Conclusions Within the population studied, imported cases had distinct characteristics from those of indigenous cases, with lower fatality rates and higher discharge rates. New infections shifted from imported cases to local infection gradually, and overall infections have declined to a low level. We suggest that preventing import of cases and controlling spread within local areas can help prevent SARS-CoV-2 infection spread.

Objectives To explore the potential of SARS-CoV-2 spread during air travel and the risk of in-flight transmission. Methods We enrolled all passengers and crew suspected of being infected with SARS-CoV-2, who bounded for Beijing on international flights. We specified the characteristics of all confirmed cases of COVID-19 infection and utilised Wells-Riley equation to estimate the infectivity of COVID-19 during air travel. Results We screened 4492 passengers and crew with suspected COVID-19 infection, verified 161 confirmed cases (mean age 28.6 years), and traced two confirmed cases who may have been infected in the aircraft. The estimated infectivity was 375 quanta/h (range 274–476), while the effective infectivity was only 4 quanta/h (range 2–5). The risk of per-person infection during a 13 h air travel in economy class was 0.56‰ (95% CI 0.41‰–0.72‰). Conclusion We found that the universal use of face masks on the flight, together with the plane's ventilation system, significantly decreased the infectivity of COVID-19. KEY MESSAGES The COVID-19 pandemic is changing the lifestyle in the world, especially air travel which has the potential to spread SARS-CoV-2. The universal use of face masks on the flight, together with the plane's ventilation system, significantly decreased the infectivity of COVID-19 on an aircraft. Our findings suggest that the risk of infection in aircraft was negligible.

To address power transmission line (PTL) traversing complex environments leading to data collection being difficult and costly, we propose a novel auto-synthesis dataset approach for fitting recognition using prior series data. The approach mainly includes three steps: (1) formulates synthesis rules by the prior series data; (2) renders 2D images based on the synthesis rules utilizing advanced virtual 3D techniques; (3) generates the synthetic dataset with images and annotations obtained by processing images using the OpenCV. The trained model using the synthetic dataset was tested by the real dataset (including images and annotations) with a mean average precision (mAP) of 0.98, verifying the feasibility and effectiveness of the proposed approach. The recognition accuracy by the test is comparable with training by real samples and the cost is greatly reduced to generate synthetic datasets. The proposed approach improves the efficiency of establishing a dataset, providing a training data basis for deep learning (DL) of fitting recognition.