Utility Finds Revenue Stream with Operations and Maintenance Solution

Sentient Science's DigitalClone Live software helps predict when cracks will appear in the microstructure of rotating mechanical components.

Duke Energy is a big U.S. player in the utility space and recently identified a new issue with some of its clean energy assets: aging wind turbines. The turbines’ warranties were ending and Duke Energy Renewables, a div. of the company, needed a solution for more than 4,000 megawatts (MW) of wind assets that the utility owned and managed. Duke Energy recognized a new business opportunity to create an operations and maintenance (O&M) service for its own assets but clients, too.

“We’ve estimated that these operators can save significant O&M costs by using an independent service provider,” says Jeff Wehner, vp of renewable operations at Duke Energy. We want to ensure that our technicians, replacement teams and asset managers have state-of-the-art tools and, after we have some experience in our own fleet, we expect to offer this enhanced service to other operators who contract with us.”

Duke Energy Renewables choose Sentient Science’s DigitalClone Live software to begin this journey and help diagnose gearbox failures and identify corrective actions for wind turbines. The software helps predict when cracks will appear in the microstructure of rotating mechanical components.

Duke Energy used the software to analyze 109 Winergy, 4410.4 GE 1.5MW-rated gearbox machines and benchmarked the new solution against its internal tools, such as oil debris and vibration monitoring. The company also examined “correlated gearbox predictions with borescope inspections conducted on the equipment.”

According to the Sentient Science, the software showed 11 specific wind turbines would show damage on the gearbox in the next 12 months in the first pilot. After Duke Energy Renewables Service climbed the tower to the borescope, the company confirmed all seven gearbox inspections had observable damage and the software also identified the correct damage subcomponents on five turbines.

Duke Energy’s existing condition monitoring system and historical data did not detect the damage. As a result of the project, Duke Energy proactively scheduled four uptower replacements

This case study and solution is another example of how new business outcomes are being shaped by Industrial Internet of Things platforms.

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