Maintaining up-to-date customer connectivity is a constant challenge for cooperative utilities. Storms and severe weather cause disruptions that lead to emergency field repairs that may not always be properly reported by strained crews.

Moreover, the area served by Pedernales Electric Cooperative (PEC) in Texas is experiencing significant population growth—resulting in an immediate need to expand and reconfigure the existing network infrastructure. The manual operation of switching devices that are not SCADA-enabled is also a source of significant reporting errors.

In the past, to maintain an accurate connectivity map, PEC had to perform extensive and costly field inspections.

However, in the last few years, PEC has rolled out a new RF-based AMI system. This AMI system has the unique feature of providing voltage phase angle measurements once per day that are synchronized across all 400,000 network meters. The question is: can these few synchronized measurements be used to develop applications that can automatically correct the network topology?

This presentation provides the details and actual field results of the research initiative that was developed by PEC and Hubbell to answer this question. Advanced AI algorithms were developed and trained, allowing accurate meter-to-transformer and meter-to-feeder connectivity corrections.

As a result of these calculations, PEC recently completed a pilot stage of targeted field inspections over a set of feeders with very successful results. These initial few truck rolls confirmed the immense potential of the technology, paving the way for a wider technology deployment.