Electricity Distribution Network Monitoring to Resist Natural Disasters and External Attacks

Hardware: reComputer J2021 with NVIDIA Jetson Xavier NX

Application: Abnormal Detection

Industry: Smart Transmission Facilities

Deployment Location: Germany

Ensuring the operation, maintenance, and safety monitoring of high-voltage transmission line facilities stands as a pivotal task for the national power grid. The surge in electrical equipment and the unpredictability of electricity consumption impose significant pressure on the grid. Factors like the widespread adoption of electric vehicles further intensify the grid load.

Challenge

Complicating matters, the environments surrounding circuit cables and transformer sites are intricate, with construction machinery and plastics showing potential threats to circuit facilities and wires. Currently, the detection and security monitoring of transmission lines and facilities heavily rely on traditional methods, including staff regular tower inspections, daytime observations, and measurements. However, the substantial equipment volume coupled with an aging workforce in the power grid results in notable deficiencies in manual detection methods, manifesting as low efficiency, extended cycles, the necessity for power outage maintenance, lack of real-time decision-making, and operational challenges during nighttime. These shortcomings present formidable challenges to the effective operation, maintenance, and safety monitoring of high-voltage transmission lines.

Solution

In response to the issues in power grid monitoring, most of the current solutions involve uploading collected video or image data to servers for analysis. However, this approach results in a large volume of video image data with minimal relevant information, consuming and wasting substantial network resources. Due to cost considerations, such solutions tend to reduce the frequency of data transmission, hindering real-time monitoring and early warning capabilities. Edge Jetson Xavier NX-based solutions, on the other hand, enable comprehensive, round-the-clock intelligent monitoring of the power grid with synergy between the front-end and back-end.

This solution utilizes high-definition night vision camera systems for real-time monitoring of national power grid high-voltage transmission lines. Front-end devices integrate artificial intelligence modules, capturing and detecting anomalies in real-time, and transmitting only pertinent results to the back-end, minimizing the transmission of vast amounts of unnecessary data. The back end is equipped with powerful computing units, leveraging transmitted data for deep learning and model establishment. The system can periodically update the models on front-end devices, enhancing anomaly detection capabilities.

As for external attracts like potential threats from construction machinery on site, the solution also deploys the object detection model to recognize hidden dangers such as forklifts, cranes, and excavators that stay on the cable channel for a long time. When several excavators overlap in the image, the method of image segmentation combined with the attention mechanism can be used to extract the first one separately for clear detection.

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