g2o animated logo transition
Learn how we’re helping our clients through the COVID-19 disruption. Click here to learn more.
Notification bar close button
Unfortunately, Microsoft is no longer supporting new web standards for Internet Explorer and recommends that users upgrade their browser experience.

On the bright side, there are some awesome modern browsers like Microsoft Edge, Google Chrome, Firefox, and Safari that can enhance all your web experiences. What’s your next...browser?

Using machine learning to predict power grid equipment failure

We applied machine learning to help a company providing grid testing to major utilities to more effectively identify potential equipment failures.

Research & Analytics
Data Operations
Back button
photo of a man checking laptop near a power line


Exacter provides grid testing to major utilities to help them identify and maintain the equipment in the power grid.  Their business model involves deploying a fleet of vehicles to drive along the power lines and listen for signals from failing equipment. This model, while effective, can miss some problems since vehicles only travel the grid every few months.

Exacter wanted to install continuous sensors on the power poles and collect data at regular intervals throughout the day (and night). But the company didn’t have the in-house expertise to develop an internet of things (IoT) network, create a testing plan to deploy sensors at optimal locations to collect data, or build a machine learning algorithm to identify potential failures.

The company chose to partner with g2o because we had both the data and analytics expertise they needed.

photo of a man checking laptop near a power line


We helped Exacter develop this new method for supporting utility maintenance. The g2o team developed a sensor network to capture signals from deteriorating equipment. We created an analytic database to store the sensor data as it was collected each day.

Then our data science team developed a machine learning algorithm to find failing equipment and direct the maintenance crews to the trouble spots before failures and outages occur.

photo of a man checking laptop near a power line


Our machine learning model was able to detect defective devices that Exacter’s existing mobile line monitoring devices missed. As a result, g2o proved the value of Exacter’s new maintenance method and how it could improve efficiency for the company.

Related work