LiPowerline, Detecting Power Line Vegetation Encroachments Faster.

Recent fires again put many of us living in the SF Bay Area through a very difficult time. The loss was enormous, and the cause is still under investigation. But wildfires are nothing new here. During my 13 years with the USFS, I participated in projects on this very issue. One of my last tasks before leaving the job was to help create and update the California Fire Hazard Severity Map. This was a major multi-agency collaboration involving a lot of data-intensive geospatial analysis and modeling. The map provides important information and is being used by many across governments and industries.

There are a number of causes for wildfires. One of the identified causes is power line. A power line can start a fire if it breaks in the wind. It can start a fire when a tree or a branch falls across it, or when lines slap together, or when equipment gets old and fails without anyone noticing. The cause(s) for any specific fire may still be under investigation. But some of the investigations have sharpened the focus on wildfire prevention strategies and the role of electric utilities, and pushed for stronger vegetation management programs by the utility companies which already are costing many millions of dollars each year.

As discussed in a recent article, the first steps of any vegetation management program should be inspection, data collection and risk assessment. While traditional visual and invasive inspections are still essential, the results of those inspections must be adequately recorded and reported within an appropriate data platform that enables infrastructure conditions to be evaluated. In addition, to obtain better data, utilities should expand the use of light detection and ranging (LiDAR) and aerial imagery. If applied correctly, technologies such as LiDAR improve the efficiency and effectiveness of identifying dangerous trees and vegetation encroachments, aid in the predictive modeling of vegetation growth patterns, provide comprehensive geospatial geographic information system (GIS) right-of-way inventories and assist in identifying high fire risk areas.

GVI LiAir 250 with Riegl miniVUX-1UAV

Today, there are good number of LiDAR systems available for efficient acquisition of power line corridor data, such as the LiAir 200 and LiAir 250 from GreenValley International (GVI). These systems can be used with both SUASs and large aerial vehicles. But processing the vast amount of LiDAR point cloud data and extracting critical information from the data have been quite a challenge. The new LiPowerline from GVI could change that.

Power Line Corridor Point Cloud from GVI LiAir S200

LiPowerline

LiPowerline was previously a module within the LiDAR360 suite. Due to significant demand from industry users, LiPowerline is now a standalone software with the release of LiDAR360 v3.0, featuring industry specific tools for identifying, analyzing, and reporting potential hazards that could impact the operation of the grid and the safety along the corridor.

Key Capabilities

• classifying power line infrastructure and corridor features through Machine Learning Classifier (MLC).
• detecting vegetation encroachments based on user-defined clearance zones under Observed and Simulated Conditions.
• generating detailed clearance violation reports with exact locations where mitigation may be needed.

Classifying Power Line Infrastructure and Corridor Features Through Machine Learning Classifier (MLC)

LiPowerline implements a variation of the MLC engine used in LiDAR360. The algorithm is optimized for classifying key power line assets, e.g. conductors, towers, and insulators, etc. This effective MLC can significantly reduce the amount of manual identification and editing needed, translating directly to the saving of labor costs for data processing.

The classification results can be converted in LiPowerline to polyline vector objects and exported as ESRI shapefiles for further analysis in other geospatial software programs.

Detecting Vegetation Encroachments Based On User-defined Clearance Zones Under Observed and Simulated Conditions.

The detection of Vegetation Encroachments/Danger Points within the targeted corridor is fully automated. User can define customer/project-specific clearance requirements. The results are immediately visualized in the software with easy-to-use tools available for operator verification and correction if needed.

LiPowerline's Early Warning Analysis (EWA) further expands Danger Points Detection to under Simulated Conditions, by user-defined Wire Temperature, Ice Thickness, and Wind Speed. The EWA also includes models for Tree Growth Analysis and Tree Fall Analysis, allowing the detection of potential danger points as the results of tree growth and/or tree fall.

Generating Detailed Clearance Violation Reports With Exact Locations Where Mitigation May Be Needed

Final danger points detection results can be exported as .kml files to be used in other compatible programs and platforms.

LiPowerline can also generated detailed reports containing the exact coordinates of detected danger points, measured distances, and company and/or client-specific information such as contact info, project name, tower ID, span ID, etc.

With these powerful tools and an intuitive workflow designed specifically for detecting power line vegetation encroachment, industry users can now really simplify and speed up the otherwise tedious and time-consuming work, and make better use of LiDAR.