Automated drone systems improve power line inspections and reduce costs
This story was originally published by Geoff Zeiss from Between the Poles. Link: https://geospatial.blogs.com/geospatial/2020/05/automated-drone-systems-improve-power-line-inspections-and-reduce-costs.html
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%.
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
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 f lash over, c racks 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.
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.
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 which reduces the rates of the helicopters.
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 FAA rules permit beyond visual line of sight (BVLOS) UAS flights for transmission inspections. 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.
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.
Originally published at https://geospatial.blogs.com.