Network Digital Twin®

The new frontier in data-driven network management 

A radical change in the way we manage our networks

Managing vegetation and other elements that can interfere with energy distribution networks is essential to guarantee reliable and continuous power to end customers and represents a key driver to optimize maintenance costs. In addition, climate change and increasingly extreme weather patterns pose a challenge in tackling possible fires and trees falling on electricity distribution lines and poles, which might cause outages. 

Gridspertise Network Digital Twin® replicates the entire electric infrastructure as well as its individual components and operations through a 3D virtual model, which enables simulated testing of the grid under all possible conditions as well as predictive maintenance through machine learning algorithms. 

The solution leverages AI and machine learning to efficiently process data from sensors installed on the actual grid and images acquired through Lidar cameras installed on helicopters or drones, to provide a holistic view of the status of the grid and enable timely decisions on inspections and interventions. 

The solution runs on a web interface and can also be adapted to your legacy systems through commercial or open source Geographic Information System (GIS) software. APIs are also available to provide system data to other applications.

 

Benefits

The Network Digital Twin® improves service quality by preventing power outages and optimizes costs because it facilitates smart predictive maintenance as well as remote grid inspections. 

For example, thanks to the Network Digital Twin®'s 3D modeling technologies, you can find out where a power cable is in relation to surrounding elements such as vegetation, buildings, and other power lines, and whether there are any critical issues in terms of distance. 

The system can also classify specific components, provide information on their condition, and predict whether external factors might interfere with the grid’s functioning, enabling technicians to solve problems in advance.

The solution also increases the safety and efficiency of field workers by leveraging aerial images from drones, helicopters and satellites, reducing the need for onerous and complex land-based patrols. 

 

Functions

  • 3D viewer: enables assessment of the status of the network infrastructure directly from the office by identifying distance issues and faults, and classifying assets
  • Vegetation management: enables evaluation of the situation of vegetation around the cables and effective planning of inspections and interventions (optimized tree pruning, predictive maintenance, monitoring execution by contractors). These activities can be done through input data represented by point clouds
  • ODIN: using machine learning it is possible to identify and predict faults at isolators, poles and cables and  to plan interventions effectively
  • 3D modeling repository (scans of power lines and primary and secondary substations)
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