EPRI Develops AI Model to Reduce Wind Turbine Operations Costs

Source: Raja V. Pulikollu, Jeremy Renshaw· T&D WORLD · | September 8, 2021

EPRI has been working with utilities in the United States to digitalize wind assets and develop a physics-based machine-learning hybrid model to identify gearbox damage and extend the life of gearboxes.

Source: T&D World

Wind turbine gearbox replacement cost can be as much as US$350,000. But EPRI’s physics-based AI early damage detection model, tested on WEC Energy Group, Southern Company and other utility’s wind fleets, may reduce repair cost to US$15,000-US$70,000.

As utilities work to support decarbonization goals, and incorporate or maintain more renewables into their portfolios, the reliability of those sources becomes vital to grid sustainability. So, when a major wind turbine component - like a gearbox - prematurely fails, production is lost, downtime can be prolonged and utilities’ operation and maintenance (O&M) costs increase.

In 2020, wind energy generation worldwide was 730 GW. It is predicted to reach 1455 GW by 2030 and 2434 GW by 2050, according to the 2020 International Renewable Energy Agency Global Renewables Outlook: Energy Transformation 2050. 

With deployment of more wind turbines worldwide, R&D activities are increasingly important to ensuring the long-term reliability of turbine components, including the wind turbine gearbox. The gearbox converts low-speed rotations received from blades into higher speeds required by generators for electricity production (Figure 1).