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BD4NRG Open call winners


Automating the data value chain to prevent wildfires generated by power lines - AutoDVC

Company/Organisation: FuVeX Civil SL

FuVeX is a spin-off of the Public University of Navarre founded in 2015 with 21 team members. It has participated in 8 EU R&D projects and 12 regional and national R&D projects. Moreover, FuVeX have 1.000 m2 of facilities and a segregated airspace area to perform flight tests.

There are plenty of drone companies in the market. However, current drones are legally limited to fly a maximum range of 500 m. As a result, crewed helicopters continue to be the man means to capture the aerial data. In FuVeX we are we are enabling the use of long-range drones with 20 times more range than conventional drones. To do so, we have developed a patented technology aircraft that has achieved the EU first-of-its-kind authorization to perform long-range power line inspections. With this authorization, we are providing inspection services to some of EU’s biggest utilities having the goal to achieve that Naturgy is the first European utility to digitize its assets using long-range drones.

Apart from the authorization, FuVeX have achieved other milestones among which stand out:

  • 3 Patents achieved, 
  • 2 M€ in sales already signed for 2023 and 2024,
  • 3 M€ in funding.

Project Description

In Europe, there are over 5 million of powerlines that are vital for the wellbeing of the European societies. However, these infrastructures can cause wildfires if the surrounding vegetation is too close to the power lines. These wildfires generate huge environmental costs and outages in the affected areas.

Furthermore, asset owners are held responsible for these wildfires and the consequent damages as was the case with the US utility PG&E.

To avoid causing these wildfires, power line owners are required by law to regularly cut and prune in areas where the vegetation is too close to powerlines. As these operations are very expensive (6587 € per 100 m.) powerline owners are using LiDAR data to assess the distance between the infrastructures and the surrounding vegetation to prune and cut only where there is risk of wildfire generation.

Nevertheless, detection of high-risk points of wildfire generation using LiDAR has a huge disadvantage: It takes time between the inspection of the power line (LiDAR data acquisition) and performing the cutting and pruning tasks to eliminate the risk of wildfire due to data processing and coordination among the stakeholders. As a result, up to 9 months of delay can happen between the inspection and maintenance works. During this time, high-risk areas can degenerate in a wildfire.

The idea of this project is to fully automate the data value chain in power line vegetation maintenance (Figure 1) taking advantage of the BD4NRG framework. Therefore, FuVeX aim to cut up to 94% costs and lowering the time to between inspection and maintenance actions from 9 months to less than a week thus reducing the risks of wildfire generation.

To achieve this vision, FuVeX proposes changes in different steps of the current value chain:

  • LiDAR data capturing (Step 2): Our core business is to develop long-range drones to replace crewed helicopters to reduce the LiDAR acquisition costs from 150€ per kilometer of power line to less than 20€/km.
  • LiDAR data processing (Step 3): FuVeX is working on an AI system based on neural networks to automatically generate the point cloud and analyze LiDAR data to detect risk areas between vegetation and power line. The system developed in this step is the basis of the project.
  • Data Integration between steps 1-5: BD4NRG framework integration will provide the functionality to share the different data with the different stakeholders (e.g., areas to capture LiDAR data, high-risk areas, pruning & cutting works approved by the owner and their schedule, etc.) in a secure and trusted way. Furthermore, the integration of the system in BD4NRG will allow the continuous refinement of the machine learning algorithms using the new LiDAR data that will be continuously uploaded in the platform.