America’s electric cooperatives provide energy to about 42 million people in more than 19 million homes, businesses, farms, and schools in 47 states, according to the National Rural Electric Cooperative Association (NRECA). In fact, over half of the landmass in the U.S. is powered by electric co-ops.
With all that ground to cover, co-ops have unique needs when it comes to asset management. We put together a guide specifically for electric co-ops outlining how a shift to drone- and data-powered inventory and inspection processes can help save time and costs while increasing crew safety and member satisfaction.
For co-ops, improving asset management is paramount: accurate inventory reduces downtime, increases worker safety, and increases profitability. But the common method of using ground crews to regularly inspect thousands of miles of assets, some of them inaccessible by foot, is an inefficient process. By switching to a drone-based solution, co-ops can streamline inspections and get a more precise view of the location and health of their assets.
Electric Co-Ops Face Unique Challenges
Co-ops are remote and widespread, with many of them serving rural areas of the U.S. Approximately 97% of the United States' land area is within rural counties, but only 60 million people—just 19% of the population—live in these areas. This means an electric co-op can have miles of feeder that serve only a few customers. “A co-op may only have two to seven customers per mile line,” says Stan McHann, Senior Research Engineer at NRECA.
Fewer customers per mile means less revenue coming in, and they also receive less funding: electric co-ops get less than half the amount of government assistance per customer than investor-owned utilities.
Thus a fast, flexible, cost-effective inspection workflow can turn any of these challenges into opportunities for improving member satisfaction, increasing safety, and growing revenue.
Drone-Based Solutions Increase Safety and Revenue
Thanks to co-ops’ technology-friendly attitude, they have an opportunity to use tech solutions to address these considerations. While it can cost $150 for two lineworkers and a bucket truck to inspect just four poles—a grand total of $37.50 per pole—those same lineworkers can use drones to perform the same task for a fraction of the time and cost: 15 seconds and about $6 per pole.
Even the best ground crew can’t see the top of a pole. “The top-down is really where you see the weather,” says McHann. “The pole takes a beating with the weather, and when you look top-down at it with a UAV, you see a lot more—you can see issues like bad bolts, loose bolts, missing bolts, and loose connections.”
By adding UAVs to the toolkits of ground and line crews, teams in the field can gather more data while spending fewer hazardous hours on manual inspections, decreasing the risk of accidents and injuries.
Implementing the Full PrecisionHawk Solution
Getting actionable pole analytics starts with collecting timely and accurate geospatial data. For many co-ops, drones are an ideal way to start using technology solutions to speed up and increase the accuracy of asset inspections. The next step is often to invest in more advanced technology to make the best use of all the data collected. Because you'll get a large amount of rich data, it’s key to consider how that data can later be integrated into your existing workflows.
By combining drone data collection with processing and reporting into a streamlined, end-to-end data solution, PrecisionHawk has made it easier for co-ops to turn a massive amount of data into actionable insights. It accelerates, automates, and amplifies data analysis to reduce human error, increase speed and efficiency, and decrease costs.
With PrecisionHawk’s drone- and data-driven solution, you'll gain unprecedented insight into the condition of your assets, and improve member satisfaction with lower costs and higher uptime. We are here to help you start with a small drone program, expand and integrate the program into your current workflow, and implement a full solution that turns drone data into actionable reports using machine learning (ML) and artificial intelligence (AI).