Many public electric utilities have recently launched aggressive aerial inspection efforts to collect data regarding line maintenance issues. The cost versus coverage challenge with respect to inspection of linear assets with drone sensors had previously resulted in slow adoption and growth of this technology in inspection protocols, but the demands of aging infrastructure have multiplied the risks associated with this challenge, forcing utility organizations to revisit the use of drones to complete asset inspections. Considering the cost and safety factors in the utilization of helicopters, the upwards trend towards the use of drones for inspection is timely.

UAS technology not only promotes economy in power asset inspection, but this integration also eliminates risk to personnel. Additionally, the ability to deploy these sensors quickly results in more timely inspections and immediate intelligence regarding issues. This benfit can result in faster repairs and greater public safety.

Further, new advances in machine learning and artificial intelligence (AI) provide context and analytics to the inspection process. This technology provides faster, more accurate change detection and identification of anomalies, such as energy leakage and hot spots. AI algorithms can be utilized to convert imagery into actionable insights, creating value in the data. In addition, AI algorithms can be trained to recognize patterns in video feed streamed from drone cameras to the server (machine learning), which can provide\precise, automated issue identification. AI linked to a work management system allows simultaneous alerts regarding an issue, including GPS location, so that line crews can make repairs much more efficiently.

Examples of advanced drone sensor technology use cases include:

  • Capture Light Detection and Ranging (LiDAR) data to measure the elevation of T&D lines and identify where visual markers are needed to comply with FAA regulations
  • Capture LiDAR data to identify trees that are encroaching on power lines
  • Perform a detailed visual inspection of boilers at generating stations.
  • Identify meter-to-transformer connections. This is a serious issue for many utilities since the inability to identify the connections diminishes the effectiveness of transformer load management analyses and other grid analytics.
  • Assess remote storm damage. Many utilities already use predictive analytics to estimate where a storm is likely to impact the T&D network. In the future, they will use drones to gather actual damage-assessment data after the storm has passed to quickly confirm or adjust this storm recovery plan based on actual data.

Adopting technologies that provide an opportunity to complete power line development, maintenance, and state of good repair inspections will result in advance safety improvements for company personnel, improved services for the public, and more efficient use of internal company resources. The historical data produced by aerial inspections will allow these organizations to track risks and make more informed decisions regarding asset maintenance.