Discovery and Innovation with Machine Learning

Source: Greg Edeson, Tom Bailek · T&D WORLD · | November 4, 2020

Program uses artificial intelligence to predict utility asset failures and prevent outages.

T-splices, the initial asset class studied by the SDG&E and PA Consulting teams.

Now more than 100 years old, the U.S. electrical grid is showing its age. In fact, 70% of transmission and distribution lines are more than 25 years old, according to the Department of Energy. Utility systems historically have been installed underground in high-density urban and suburban areas to preserve aesthetics and increase system longevity and safety. On the downside, undergrounding has made it more difficult to locate, access and repair failures in these densely populated areas.

San Diego Gas & Electric Co. was faced with this situation. Making up more than 60% of its total distribution grid, the utility’s underground assets are now approaching more than 40 years in age. In the last 10 years, T-splices were responsible for more than one-third of all increases in the utility’s asset failures. SDG&E manages more than 10,500 miles (16,900 km) of underground distribution lines and an estimated 150,000 T-splices on 700 underground circuits. The utility needed a better way to predict which of these assets would fail next.

Simple electrical components that join mainline underground cables, T-splices are like other assets in that they can fail routinely, causing unplanned outages. It can be hard to locate the outages immediately because T-splices are underground and not directly monitored by the control center, as they are considered minor assets. In an era of digital transformation, it may seem astonishing an electrical part costing less than US$60 can trigger outages that impact customers’ daily lives and cause utilities tens of thousands of dollars to resolve.