Between the Poles

Canada plans to allow BVLOS drone operations in remote areas without a waiver

Canada plans to introduce in 2021 certification that would allow pilots to undertake beyond visual line of site (BVLOS) flights in isolated areas without the need for a waiver. This opens the possibility of more frequent, low cost inspection possibilities for transmission utilities with large stretches of line running through vast open areas or forest corridors.

Background

Transmission line inspections for vegetation management and other purposes are essential in ensuring grid reliability and resilience.  They are generally performed by manned helicopters or by a ground crew.  Data is collected with cameras and analyzed to detect diseased trees that could fall and hit a powerline (fall ins) or trees that were encroaching on power lines (grow ins).  These inspections are mandated by NERC in North America and are not optional.  Beyond visual line of sight (BVLOS) drones (UAVs) and software analytics using machine learning has made it possible to approach completely automating this expensive process.  Electric Power Research Institute (EPRI) has estimated that the reduction in cost with using an automated UAS compared to a helicopter inspection is between 55% and 28%. Automating transmission line inspections for vegetation management using BVLOS drones not only saves money but also could improve the resiliency and reliability of the transmission grid.

There are other important use cases as well including precision agriculture, public safety for search and rescue missions and delivery services to remote and rural communities.

Currently, there are two levels of drone pilot certification in Canada – basic and advanced – which roughly correspond to the FAA’s Part 107 waivers in the US. New rules for commercially operating UAVs in the U.S. were published in 2017 by the FAA as Part 107 of the Federal Aviation Regulations.  For non-standard flights, such as BVLOS missions, Canada require operators to acquire a Special Flight Operations Certificate (SFOC) which requires operators to fly their drones beyond visual line-of-sight in isolated areas outside of concentrated population centres and not near restricted airspace such as airports.  In addition there are special provisions for drones with onboard detect-and-avoid (DAA) solution which allows them to fly safely without human intervention even in populated areas.  For example, in March 2020 first approval for BVLOS drone flights was granted in Canada for Iris Automation’s Onboard Detect-and-Avoid technology.

However, Canada plans to introduce in 2021 a tier of certification that would allow pilots to undertake BVLOS flights in low-risk conditions without need for a waiver. This opens possibilities for transmission utilities with large stretches of line running through vast open areas or forest corridors.

Proposed routine BVLOS flights without a waiver

In January 2019, Transport Canada (TC) published new RPAS regulations for operations within visual line of sight (VLOS), and these regulations permit BVLOS operations under low-risk conditions with the issuance of a Special Flight Operations Certificate (SFOC). In April 2020 Transport Canada posted a new Notice of Proposed Amendment (NPA) to update Canadian regulations to allow for routine beyond visual line of sight (BVLOS) drone operations in “Low Risk” environments. The proposed regulatory amendments would be the first of a series of steps towards allowing more routine BVLOS operations. These amendments will not address more complex and higher-risk BVLOS operations in populated areas. Higher risk BVLOS operations will be subject to a separate regulatory process at a later date.

This proposal aims to begin to unlock the potential for beyond visual line-of-sight operations in Canada. Specifically Transport Canada is proposing the following amendments for isolated areas.

• 250 g to 25 kg BVLOS operations in isolated areas; within 1km of an area with a population of more than 25 people per square km; over an area with a population density of more than 25 people per square km; and, in controlled airspace;
• 25 kg to 150 kg BVLOS operations in Isolated Areas, and within 1km of an area with a population of more than 25 people per square km; and,
• 150 kg to 650 kg BVLOS operations in Isolated Areas with a max altitude of 400 feet above ground level.
  

Automated drone systems improve power line inspections and reduce costs

Transmission line inspections are mandatory in North America. Traditionally helicopters are used for these inspections, but with helicopters the risk of accidents is significant and helicopters are costly to maintain and operate.  Drone inspections dramatically reduce the risk of accidents.  Aeriosense Technologies has developed an automated drone inspection system that can reduce the cost of inspections by up to 55%. 

Background

Transmission line inspections are essential in ensuring grid reliability and resilience.  They are generally performed by manned helicopters or by a ground crew.  Data may be collected with cameras and analyzed to detect a variety of conditions including corrosion, evidence of flash over, cracks in cross arms, and right-of-way issues such as vegetation encroachment. Inspections every year or 18 months are mandated by NERC in North America and are not optional. 

With over 200,000 miles of high-voltage transmission lines and 5.5 million miles of distribution lines in the United States, improving the efficiency and reducing the risk of inspections would have a major impact on the reliability of the power grid.  Drone technology, legislation and regulations enabling beyond visual line of sight (BVLOS) flights, software for planning flights and analytics for feature identification are making it conceivable that the expensive process of inspections could be completely automated.  Even now current technologies make it possible to dramatically reduce the cost of power line inspections. According to Navigant Research, globally electric utilities are expected to spend over $13 billion a year on drones and robotics by 2026, a dramatic increase from about $2 billion currently. 

Automated drone inspections

Flight plan for transmission tower inspection

Using automated unmanned aerial systems (UAS) Aeriosense Technologies can reduce the cost and improve the safety of vertical infrastructure inspection.  Unlike many other companies which offer drone services to many different industries, Aeriosense focuses only on the electric power sectors, currently transmission but in the future distribution.  I had a chance to chat with Alex Babakov, a co-founder of Aeriosense, who has many years of experience in the electric power sector, including working for BC Hydro Research/ Powertech’s High Voltage Laboratory (HVL). 

Alex explained to me that Aeriosense provides a complete visual inspection of pylons and poles, cables, and right of way including vegetation encroachment using a UAS typically with a photo camera.  Examples of the types of problems that can be identified include broken strands in lines crossing canyons and rivers, corrosion on insulators and other equipment, evidence of flash over, cracks in cross arms, and problems in the right of way such as human or vegetation encroachment.  Aeriosense also conducts inspections as part of storm response to identify damage resulting from extreme weather conditions.

Flight plan for transmission line inspection

Automation enables Aeriosense's drone systems to provide higher quality inspections at lower cost. In preparation for an inspection flight, the available GIS and LiDAR data is analyzed with the aid of machine learning algorithms to create an optimal flight path.  With a flight path optimized in this way cost reductions of about 36% can be achieved compared to manually operated drone-based inspections.  Furthermore compared to manual drone inspections automated inspections reduce pilot fatigue and safety risks while conducting inspections.

Aeriosense is also active in distribution inspections. The sheer size of the distribution network in terms of total number of kilometers and number of pieces of equipment makes it clear that an automated approach is required. With current technology and regulations, automated inspections can be applied to some but not all distribution circuits. In the future with new technology developments and regulations automated inspections will be able to be applied to more distribution circuits.

Cost reduction

Based on data of inspection efficiencies from our past projects and the data coming from the field trials done by Electric Power Research Institute (EPRI) the reduction in cost with using an automated UAS compared to a helicopter inspection is between 55% and 28%.  The challenge is doing these cost comparisons is that there are intangibles that are difficult to quantify. Safety is one of these. Helicopter accidents are not infrequent in the electric utility industry and more often than not they lead to fatalities.  BC Hydro, for example, has a requirement for helicopters to be dual engine for redundancy and rates for this equipment are much higher.  Other utilities may not be willing to pay for the extra security whichl reduces the rates of the helicopters.

Improved safety

One of the most important benefits of Aeriosense's automated UAS approach is safety. Because of the relatively high risk of helicopter inspections, utilities have been looking for alternatives. Drone-based inspections are much safer and this provides additional motivation for utilities to move toward adopting drone-based inspections.

Development of training datasets for machine learning

Another benefit of the drone-based inspections are that utilities get a historical, unbiased record with the imagery that is captured by the drones. This enables engineers to go back and review the condition of assets not from a written record but from photos.  This provides a more objective view of how the condition of an asset has changed over time.

Collecting historical data has another important benefit in that it can provide a body of imagery that can be used to train machine learning algorithms. Collecting the data now will allow them to apply the AI tools currently being developed in the future.

Future applications of automated drone systems

The impact of more frequent, less costly inspections is improved reliability. Combining the efficiency of automated UAS inspections with defect detection using machine learning will allow utilities to get a very clear, timely situation assessment of their distribution system.  Aeriosense is currently working with the Centre for Energy Advancement through Technological Innovation on a project to help utilities identify current opportunities of automated drone applications and to develop best practices in implementing automated drone inspection programs.  Aeriosense is currently working in the transmission and distribution sector, but is actively exploring applications in the substations and generation verticals.

Another area that Aeriosense is currently piloting is the use of beyond visual line of sight (BVLOS) UAS flights for transmission inspections. FAA rules permit beyond visual line of sight (BVLOS) UAVs through Part 107 waivers.  I have blogged about Xcel Energy which obtained one of the first three waivers to fly UAVs for beyond visual line of sight operations. Xcel Energy estimates that flying drones beyond line of sight will eventually cost between $200 and $300 per mile, compared to helicopter flights that cost an average of $1,200 to $1,600 per mile.  More than 30 of these waivers have been granted and more than 20 companies are operating beyond BVLOS drones, still with some restrictions.  FAA very recently began allowing BVLOS operation using radar to track the location of the UAV.

Summary

Automated drone technology is already impacting transmission line inspections by improving safety, reducing the cost and time required to complete inspections, and by collecting an objective historical record that can provide training data for future machine learning applications. It not only enables more frequent inspections and improved reliability, but it also enables more rapid inspections, which is especially critical after a storm or other emergency situation. Research is underway at Aeriosense to determine the applicability of automated drone technology in other sectors, such as substations and generation, of the electric power industry.

Automating transmission line inspections with BVLOS drones and machine learning

Transmission line inspections for vegetation management and other purposes are essential in ensuring grid reliability and resilence.  They are generally performed by manned helicopters or by a ground crew.  Data is collected with cameras and analyzed to detect diseased trees that could fall and hit a powerline (fall ins) or trees that were encroaching on powerlines (grow ins).  These inspections are mandated by NERC in North America and are not optional.  Beyond visual line of sight (BVLOS) drones (UAVs) and software analytics using machine learning has made it possible to approach completely automating this expensive process.  Automating transmission line inspections for vegetation management using BVLOS drones not only saves money but also could improve the resiliency and reliability of the transmission grid.

Mapping transmission lines with BVLOS UAVs

At ILMF 2019 in a very interesting talk Paolo Guzzini of Delair described how BVLOS drones has been deployed to map transmission lines in remote areas of Iceland.  Delair conducted two surveys on behalf of EFLA Consulting Engineers, a general engineering and consulting company. EFLA needed to map existing power lines and survey an area where Landsnet, the main transmission system operator in Iceland, planned to build a new power line between two cities. Iceland's airspace regulations allows BVLOS UAVs to be used in areas that are not densely populated. Delair deployed fixed-wing drones to gather two types of data: photography and LiDAR.  The drones were programmed to fly a predetermined flight plan so the pilot had to only monitor the drone's progress and be available for emergencies.  Delair’s drones were able to capture comprehensive data along the planned route and deliver the processed results to the client the next day — all at a fraction of the cost of hiring a helicopter or ground crew.  UAVs with a range of 110-120 minutes flew for up to 10 km per flight. In one case the UAV flew a total of 70 km with a 21.4 megapixel photo camera to capture a corridor of 200 m width to an accuracy of 0.5 m vertical and 0.2 m horizontal accuracy. The actual vertical accuracy achieved was 0.15 m.  In a second mission a UAV flew at 100 m a corridor 10 km in length with a LiDAR camera to capture a corridor 200 m wide with the objective of vertical accuracy of 0.5 m and horizontal accuracy of 0.2 m. The actual accuracy achieved was 4 cm horizontally and 2 cm vertically. The advantage of using LiDAR is that it can achieve better accuracy in location especially in the vertical dimension. In addition the LiDAR approach was able to detect detect conductors and poles.  In both cases the range of the flights was limited by weather conditions, radio range (15 km), and weight of the camera payloads.

Reality capture for vegetation management of transmission lines with BVLOS UAVs

Periodic transmission line inspection is mandated for North American transmission lines by NERC.  Inspections are currently carried out by manned helicopter or ground crews which are expensive and for that reason not done more often than is required by NERC.  Full automation, which is possible with BVLOS UAVs and automated feature extraction of lines, pylons, vegetation and ground, would dramatically reduce the cost of inspection and enable more frequent flyovers.  The major benefits would be lower costs and a reduction in the number and length of outages resulting from vegetation transmission line interactions.

In the U.S. on July 15, 2016, President Obama signed the FAA Extension, Safety, and Security Act of 2016. The 2016 Act requires the FAA to establish a process to allow certain UAV operations related to utilities, pipelines, and oil and gas production to be conducted beyond the visual line of sight (BVLOS) of the operator and either in the daytime or nighttime.  As a result FAA rules permit beyond visual line of sight (BVLOS) UAVs through Part 107 waivers.  I blogged over a year ago about Xcel Energy which obtained one of the first three waiver to fly UAVs for beyond visual line of sight operations.   Paolo related that 32 of these waivers have been granted and that more than 20 companies are operating beyond BVLOS drones but still with some restrictions.  Paolo related that the FAA very recently began allowing BVLOS operation using radar to track the location of the UAV.  Paolo expects to see many long-distance UAV operations in the near future, making drones fully competitive against helicopters.

Over 20 countries have regulations that permit BVLOS UAV flights. In some areas of the world, Delair has even been able to control flights via a 3G data connection, allowing a drone to inspect up to 30 miles in a single flight.

Post-processing

Paolo told me that Delair's post processing software uses a combination of machine learning and other technologies to classify LiDAR point clouds with 85 to 90 % reliability in order to identify potential vegetation encroachments.  I have blogged about Enview and other companies that also offer software solutions for this problem.

Conclusion

Rapidly evolving regulation to permit BVLOS operations, Delair's successful deployment of BVLOS drones with radio or 3G communications and automated post-processing, often cloud-based, to identify potential vegetation encroachment indicate that we are on the cusp of being able to completely automate this type of transmission line inspection.  Using drones and automated feature extraction software not only dramatically reduces the cost of these inspections but makes possible more frequent inspections including on-demand - for example immediately after storms - which would impact positively the reliability and resilience of the transmission grid.

Automating vegetation management for electric power transmission lines using deep learning with LIDAR

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.

3D visualization of a 230kV span (blue lines) with potential grow-in (yellow) and fall-in (red) vegetation that falls within client-determined clearance boundaries.

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.

Completely automated classification of 3D scans by next year ?

Today's 3D scanners are able to generate 3D models in which humans can easily recognize objects such as vegetation, ground, buildings, power transformers, utility poles and cables, and windows and doors.  Automating this process of classifying point clouds or meshes, also known as feature extraction, will enable automation of many currently time consuming workflows in many industries.  In a panel discusion at the SPAR3D 2017 conference this year Greg Bentley, CEO of Bentley Systems, alluded to a Bentley-Microsoft project to apply machine learning to classifying reality meshes and hinted that we might see fully classified reality meshes demonstrated at next year's joint SPAR3D + AEC ST conference in Anaheim.

One example where automating feature extraction would provide big benefits is automating vegetation management for transmission lines.  Periodic transmission line inspection is mandated for North American transmission lines by NERC.  It is currently carried out by helicopter-born LiDAR which is expensive and for that reason not done more often than is required by NERC.  Full automation, which is possible with beyond-visual-line-of-site (BVLOS) autonomous UAVs and automated feature extraction of lines, pylons, vegetation and ground, would dramatically reduce the cost of inspection and enable more frequent flyovers.  The major benefits would be lower costs and a reduction in the number and length of outages resulting from vegetation transmission line interactions.

The latest 3D scanners, which are capable of creating high precision photorealistic models of just about anything, from digital terrain models and cities to detailed models of cathedrals, are getting much more affordable and accessible.  The current lowest price point is about $400 to $450 for adding 3D scanning capability to a tablet.  In the future 3D scanning and imaging may simply be built into smart phones.  Automating feature extraction from the exponentially increasing volume of 3D data, whether point clouds or meshes, is going to grow in importance.

But automating feature recognition and extraction from a point cloud or mesh remains a challenge.  Researchers have attempted to use machines to recognize features in imagery ever since computers were able to manipulate and analyze raster data.  There are cases where imagery contains a restricted set of types of objects in a known environment which makes feature recognition simpler.  For example, identifying icebergs in satellite or overflight imagery of the North Atlantic.  As another example, in a refinery or a chemical plant most of the objects are pipes, structural steel, and ducts.  For this type or environment with well-defined geometrical objects, software such as Trimble RealWorks (TRW) can identify walls, floors and ceilings inside buildings and ground, buildings, vegetation, and curbs and gutters outside.  At SPAR3D it was announced that the latest version of TRW 10.3 can even resolve power lines, which is critical for automating vegetation management for transmission lines.

I have blogged about Indoor Reality who have developed technology that can track accurately the location and orientation (six degrees of freedom) of a backpack containing LIDAR and other sensors as the operator walks through a building going into rooms, halls, up and down stairs and into other human accessible spaces.  Software tools are provided to generate intelligent triangulated mesh surfaces.  The package includes analytical software tools for automatically generating floor plans, distinguishing rooms, identifying staircases, and identifying doors and windows, in fact, everything needed for a Revit model of the interior of the building.

But for general imagery automating feature extraction typically follows the Pareto principle - extracting 80% of the features is easy, but the remaining 20% is difficult and requires most of the effort.  Currently companies such as Clearedge offer specialized software, Edgewise, for classification of 3D imagery.  Trimble has licensed Edgewise from Clearedge for construction applications and it can be used to extract pipes, conduits, structural steel, ducts, structural concrete, walls, windows and doors from 3D scans.  But the current algorithms are not able to classify everything and the process remains semi-automated - Clearedge claims that in general Edgewise can speed up the process of feature extraction by 70 %.  I am looking forward with extreme interest to seeing fully classified reality meshes demonstrated, perhaps as early as next year's joint SPAR3D + AEC ST conference in Anaheim.

New rules for UAV operation by electric power utilities including beyond visual line of sight

New rules for commercially operating UAVs in the U.S. have been published by the FAA as Part 107 of the Federal Aviation Regulations.  These cover commercial uses of drones weighing less than 55 pounds.  First and foremost the rules requires that the UAV be kept within unaided sight.  However,  you can request a waiver of most operational restrictions if you can show that your proposed operation can be conducted safely.  As I blogged a year ago Xcel Energy obtained a waiver to fly UAVs for beyond visual line of sight (BVLOS) operations.  Furthermore on July 15, 2016, President Obama signed the FAA Extension, Safety, and Security Act of 2016. The 2016 Act requires the FAA to establish a process to allow certain UAV operations related to utilities, pipelines, and oil and gas production to be conducted beyond the visual line of sight (bvlos) of the operator and either in the daytime or nighttime.

The maximum speed is 87 knots and you can’t fly a small UAV over people who are not directly participating in the operation.  But also interestingly, operations are allowed from a moving vehicle if flying over a sparsely populated area.  Since transmission lines traverse sparsely populated corridors, utilities are now using UAVs piloted from moving vehicles for vegetation management for transmission lines.

Secondly, to operate a small UAV under Part 107, you need a remote pilot airman certificate with a small UAV rating.  The drone itself does not have to be registered with the FAA.  The maximum allowable altitude is 400 feet above the ground.  But interestingly it can fly higher if the drone remains within 400 feet of a structure. This allows drones to be used on construction sites of tall structures, for example.

Tree contacts account for 30% of outages in North America.  For example, trees caused the 2003 blackout of the Northeast. The new regulations mandated in the 2016 Act would enable low cost, fully automated scanning of transmission lines for vegetation management as required by NERC (NERC FAC-003-3).  By combining LiDAR scanning on a UAV platform with automated extraction of transmission lines spans, pylons and vegetation and identification of vegetation encroachment risks, it is now conceivable that in the near-future outages due to vegetation interaction with transmission lines can be dramatically reduced because scanning is becoming much less expensive.

DistribuTECH2016: Dramatically cutting utility costs with unmanned aerial vehicles (UAVs) flying beyond visual line of sight

If you would ask me among all the new technologies on display here and at other events which will likely dramatically reduce the bottom line of utilities in the transmission business I would say unmanned aerial vehicles (UAVs) or drones.  For example, every utility which has transmission lines spends large sums on overflying, typically with very expensive helicopter flights, transmission lines for vegetation management to monitor the trees to prevent them from interfering with transmissions lines.  If instead this could be done with much less expensive UAV flights, the savings would be huge. But right now this is not possible, because commercial operation of UAVs is only allowed by the Federal Aviation Authority (FAA) with visual line of sight (VLOS) rules.

 

The potential ROI of using UAVs for vegetation monitoring are expected to be sizable, but this requires the FAA to change regulations to permit operating of UAVs with beyond visual line of sight (BVLOS) rules.  Today at DistribuTECH2016 Eileen Lockhart of Xcel Energy with partners Environmental Consultants Inc and Flot Systems gave a presentation that showed just how close we are to UAVs operating under BVLOS rules becoming a commercial reality for electric power utilities.

 

Xcel Energy, which has electric and gas assets in eight states, is the fourth largest utility in the U.S.  They have partnered with EEI, EPRI, INL and others in the utility sector to show the way in the application of UAVs in the utility sector.   This includes working with the FAA to push forward the practical application of UAVs in the utility sector.  Their objectives are improved safety for utility employees and the public, reducing risk to and improving reliability of the grid, and reducing the cost of operating and maintaining grid infrastructure.  The technologies involved include GIS and geospatial analytics in addition to the UAVs themselves.  Expected sources of major savings are reducing the need for expensive helicopters and plane flights and the time utility employees have to spend in the field.

 

Xcel launched their UAV Proof of Concept project in 2015 with seven UAV missions in transmission, gas operations, energy supply, and electricty distribution.

• Transmission line and tower inspections
• Substation facility rating data collection
• Pipeline bridge and river crossings inspections
• High pressure pipeline inspections
• Coal/ash storage inventory (volumetric)
• Wind turbine blade inspections
• Storm damage assessment

Just last week (Feb 3, 2016) Xcel with its partners completed the first beyond visual line of sight mission with UAV flights over 20 miles of transmission lines.  They are convinced that BVLOS flights will become a commercial reality in the near future.   The UAV flights will not  be flown by Xcel itself but by contractors such as Flot Systems operating with FAA licences.  Xcel has not yet calculated ROIs for the applications using UAVs, but plan to do so soon.