Digital intelligent monitoring of power lines based on three-dimensional data
DOI:
https://doi.org/10.17308/sait.2020.2/2920Keywords:
monitoring, infrastructure facilities, laser scanning, intelligent information systems, functional modellingAbstract
Deformations of power lines, often caused by anthropogenic or natural negative impacts, can result in emergency power cuts and pose a threat to people’s safety. These risks indicate the need to monitor power lines and develop algorithms for the optimisation of this process. The article considers the creation of an information system for monitoring infrastructure facilities, namely overhead power lines. The structural elements of overhead lines are indicated, their main types of deformations, as well as the existing methods (remote, contact) and tools for obtaining information about their parameters are considered. The article demonstrates the advantages of using three-dimensional data. The systems used to store, process, and visualise this data (Autodesk InfraWorks, Autodesk BIM 360, and Bentley Microstation Unigine) are analysed and their drawbacks are indicated. The article describes the sources of three-dimensional data and presents the advantages of using the technology of three-dimensional laser scanning. The results of the three-dimensional scanning of power lines in Krasnodar are presented. The article evaluates the possibility of using this technology for monitoring purposes. The study demonstrated that it is necessary to create a regional intellectual monitoring system. The article describes the functioning of the created system by developing the main blocks: the import/export block, the visualisation block, the modelling block, the information storage block, the computational block, and the analytical block. The initial, intermediate, and final states of the system are indicated. Using the IDEF0 methodology we described the principle of the system’s operation, as well as the process of intelligent search for deformation (poles tilting) and determination of the dimensions of overhead lines based on convolutional neural networks.
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