Facing Accidental Wind Turbine Repairs? Digital Technologies Have...
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Facing Accidental Wind Turbine Repairs? Digital Technologies Have the Answer

By Energy CIO Insights | Tuesday, January 19, 2021

According to one study, the US wind farm's median O&M cost was approximately 48,000 dollars per Megawatt (MW) in 2016. Besides, the demand for wind turbines is rising increasingly as a whole.

FREMONT, CA: Operations and Maintenance (O&M) costs have long been a heavy burden on turbine operators. When maintenance and spare parts are taken into account and servicing visits, premiums, and more, this leads to a substantial proportion, about 30 percent of the total annual price tag of the turbine.

According to one study, the US wind farm's median O&M cost was approximately 48,000 dollars per Megawatt (MW) in 2016. Besides, the demand for wind turbines is rising increasingly as a whole.

Predictive Maintenance Helps the Industry Reduce the Cost of Wind Turbine Upgrades

The elimination of these costs is a core goal for one UK-based Offshore Renewable Energy (ORE), a science, innovation, and research hub for offshore renewables. Its service and performance unit partners with Original Equipment Manufacturers (OEMs) and owners to upgrade their current fleet and enhance the performance of the industry.

Artificial Intelligence, Drone robotics, and Data Analytics All Have a Role to Play

In reality, digital innovations of this type have been declared as a true game-changer, with the ability to increase productivity and expose minor issues before they become large problems. One technology firm projected that if operators were to incorporate emerging technology completely, they could benefit between 4 million euros and 13 million euros per GW per year.

Machine learning is also being used to evaluate tens of thousands of engineers and helicopters' photographs, along with datasheets from previous fixes. This aspect makes asset owners realize what is underneath their mistakes and weaknesses. For example, in the case of blades, knowing what a gel coat fails, leading-edge corrosion, a lightning strike, or an engineer's boot mark will help ensure that the proper technician and equipment is deployed on the right blade. AI can start pre-determining warning logs and error notifications around the control center to reduce the potential for human error, so the uses are vast and will continue to be used across the field.

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