Southern California Edison's service area contains over 1.4 million power poles, some of which are decades old. Monitoring these assets and their conditions has been challenging.

Although utilities have digitized their analog records, the legacy grid data is not always consistent and manual processes are still required. Using machine learning to analyze the available data can verify its accuracy and simplify the update process.

“The electric grid has been built over more than a century,” said Steve Powell, SCE president and CEO. “That means that its records, in many cases, are a century old, and they are not perfect. Having high-quality data has become more and more important to managing a grid and supporting our customers.“

Manually managing digital asset data can be expensive. It would cost an estimated $16 million and involve 300,000 worker hours over multiple years to complete a service area-wide review and update all of SCE’s pole locations. The question was how to do it faster and more efficiently.

“Outdated or inaccurate data within the digital systems can lead to inefficiencies in grid planning and operations needed to ensure employee and public safety as well as system reliability,” said Eric Nunnally, SCE senior manager for Asset Data Solutions & Implementation. “An SCE team of software developers, electric grid experts and field personnel developed an in-house application called the data remediation tool that addresses these issues.”

The tool substantially reduces the need for manual processes to evaluate millions of photos and provides reliable data that is easily searchable. By incorporating the data more efficiently and using an automated process, the company is expected to save $8 million and 170,000 worker hours across SCE’s service area.

The data remediation tool solves a similar problem that today’s television viewers experience navigating video content across dozens of content providers. Just as these digital media players’ algorithms sift through user data to extrapolate and suggest content the user might like, the remediation tool similarly sifts through utility photo data to accurately predict equipment locations within 30 feet.

“Inaccurate asset locations can pose downstream safety risks because of increased time searching for equipment in the field or missing inspection and maintenance activities entirely,” said Noe Bargas, a principal manager in SCE’s Asset Management Program. “It can also lead to significant system challenges when inaccurate location data extends repair outages or increases the possibility of ignition from utility equipment during high-fire risk weather.”

“If a repair outage were to occur, troublemen — SCE’s first responders to a service interruption — dispatched to the vicinity of the outage will have increased confidence in knowing they are at the correct point of failed equipment without having to search or trace a pole or line,” Nunnally said. “This translates to reduced outage durations for customers.”

In April, the Edison Electric Institute (EEI) named SCE one of five U.S. and three international electric companies as finalists for the 2023 Edison Award, which is presented annually to electric companies for their distinguished leadership, innovation and contribution to the advancement of the electric power industry. The data remediation tool was submitted as a case study for the 95th annual industry award.

"Our team’s ability to leverage new technologies like machine learning and AI in new ways is putting us in a lot better position to make sure that the grid is reliable, resilient and ready for a clean energy future,” Powell said.

Learn more about advanced technologies and their benefits by visiting sce.com/clean-energy.