The safe operation of transmission lines is crucial for the transmission of electrical energy, and arc sag and safety distance have attracted extensive research as important parameters for measuring whether transmission lines are operated safely or not. In this paper, we present an innovative edge computation model for monitoring arc sag and safety distance in transmission lines. The model combines real-time data collection with a localized computational process to ensure timely and accurate detection of risks faced by transmission lines. By utilizing the distributed capabilities of edge computing, the model reduces latency and improves monitoring response time.
In this paper we first analyze the relevant transmission tower detection systems in China and abroad, understand the pain points and needs in the use of the system, then design an Internet of things-based transmission tower mechanical state monitoring system. The system adopts the hardware design of distributed host-worker machine, through the sensor of the worker or host machine to realize monitoring the tilt angle of each dimension of the pole tower and the climate environment information such as wind speed, temperature and humidity of the surrounding environment, transmitting them to the host machine by Bluetooth, and the host machine performs edge calculation to assess the operational fault risk of the transmission line pole tower using the improved analytic hierarchy process. We can achieve detection and prevention of risks much earlier. Then the host uploads the detected and processed data to Ali-Cloud Internet of things platform through MQTT protocol for data visualization. Risk data visualization enables the staff to grasp the operation of the transmission tower in real time and provide a reference for inspection of the tower operation mechanics state and the basis for inspection.
The morphology of high-voltage cable sealing layer has an important impact on the sealing characteristics. Aiming at the measurement problem of high-voltage cable sealing layer morphology, this paper uses a lattice laser to irradiate the target to form a laser lattice on the surface of the target, and then obtains the target image with a binocular polarization camera. The polarized light is used to overcome the influence of metal reflected flare, and then a pair of target images are matched. Then the parallax of each point is calculated. Finally, these points are used to reconstruct the point cloud to obtain the three-dimensional(3D) shape of lead sealing layer. The method in this paper provides a method for the measurement of the 3D shape of lead sealing layer, which is of great significance for the quality control of lead sealing layer.
Lead has good corrosion resistance and sealing property, which is often used in cable lead sealing process and various processes. In the production process, there may be small defects in the lead products, which may affect the quality of the products. Therefore, it is of great economic significance to inspect them to ensure their reliability. Due to the variety of lead products, the use of traditional ultrasonic detection is limited, and laser ultrasound has the ability to detect all kinds of surface samples. However, when the inner defect size of lead products is submillimeter or smaller, the echo signal-to-noise ratio(SNR) is low, so the traditional laser ultrasonic technology is difficult to locate the defects. In this paper, the synthetic aperture focusing (SAFT) technology is applied to the laser ultrasonic detection of lead internal defects. The minimum defect identification ability and resolution ability of laser ultrasonic combined with SAFT are studied, which provides reference for practical application.
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