With FAA rules permitting beyond visual line of sight (BVLOS) UAVs available through Part 107 waivers the cost of capturing the data required for vegetation management for power lines, whether transmission or distribution, could drop dramatically. However, manual post processing of the imagery to identify areas of vegetation encroachment, grow-ins and fall-ins, remains an error prone, tedious and time consuming process. I have come across several firms (for example EPRI, SiteSee, Trimble RealWorks) over the years that have been working towards automating the process for identifying vegetation encroachments including grow-ins and fall-ins, but I have not encountered a utility that is using this technology operationally. At Distributech 2019 in New Orleans I had a chance to chat with a startup Enview, based in San Francisco, who described to me how their software has been implemented over 7,000 miles of transmission lines last year to automate the identification of vegetation encroachment from LiDAR point clouds. Furthermore, they expect the total distance covered to double this year.
Vegetation is a significant source of outages for many utilities. In some regions from one quarter to one half of all outages can be ascribed to vegetation. Vegetation encroachment initiated the massive North American northeastern outage of 2003. It is so critical for utility operations that utilities even have a vice president responsible for vegetation management. It can be costly for utilities to identify areas of high risk vegetation encroachment which typically are carried out by manned helicopters. Scheduling and routing crews to prune the trees that require attention is often sub-optimal adding to the cost of vegetation management. Optimizing these processes can produce significant benefits in the form of fewer outages and reduced costs. I have blogged about Energisa in Brazil who used satellite stereo imagery to prioritize and optimize tree pruning and ComEd's pilot project in Chicago that uses LiDAR imagery to not only prioritize and optimize but also to change the pricing structure for procuring distribution tree trimming.
Enview applies machine learning, computer vision, and geospatial big data analytics to highly reliable feature extraction from LiDAR point clouds to identify the exact location, height and extent of trees and then to determine proximity to power lines and other utility equipment. Utility GIS data can be integrated and results visualized using colour-coding to identify the level of risk for each tree. This enables the utility to prioritize their tree trimming and optimize contractor routing. In environmentally sensitive areas the visualization can be used to inform discussions with the various stakeholders responsible for different land uses.
At Distributech I had a chance to chat with Krassimir Piperkov, COO at Enview, who described how this technology has been deployed on more than 7,000 miles of infrastructure last year, and is on track to be deployed on over twice that much this year. He gave a very recent example of annual vegetation management analytics performed on 230kV transmission lines in California. These lines run through suburban and environmentally-sensitive, heavily forested areas.
LIDAR data was acquired by a third party through a helicopter-mounted Teledyne Optech LiDAR sensor package. Four band imagery was co-collected during the LIDAR survey and registered into an orthomosaic of RGB and NIR bands. The data collection generated almost 2GB per linear mile. The Enview data analytics engine processed this data to (i) first classify the massive amounts of LiDAR data and (ii) then automatically calculate potential grow-in and fall-in vegetation points with high speed and accuracy. Insights and data were delivered to the operator not only via GIS-compatible file format for incorporation into existing workflows but also via an intuitive web portal accessible across silos. As a result of the superior accuracy and speed, the client was able to confidently prioritize vegetation prescription work to the areas that need it most and do so much sooner than otherwise possible with traditional methods.
The results being achieved with Enview's technology suggest that we are on the cusp of the widespread adoption and application of deep learning technology to automate specific tasks. The combination of UAVs for data collection and machine learning for feature extraction dramatically reduces the cost of inspections for utilities. This would permit them to be done more frequently than with manned aircraft and would lead to improvement in the reliability of above ground electric power and communications networks.
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