Revolutionizing Renewable Energy: How Automation is Transforming Wind Turbine Maintenance

Automation has found a way to embed itself within the renewable energy ecosystem, promising to make wind and solar power generation more efficient than ever before. Sustainable energy generation is already something that many consider to be intrinsically linked to a brighter future for all.

It’s easy to imagine how automation technology can be applied to things like energy distribution, storage, and automatic shadow flicker recognition - to name a few - thereby improving the overall operation of a wind farm. It does beg the question however: how does automation technology, when applied to wind turbine maintenance activities, create efficiencies?

The purpose of this article is to examine the aggregate impact of automation technology, AI, and machine learning has made on the wind power industry, with a specific focus on wind turbine maintenance.

How Has Automation Impacted the the Wind Power Industry?

As if the wind power industry wasn’t already making great strides in the efficient generation of clean, renewable wind energy, the integration of automation technology has helped to usher in vastly superior optimizations. Nearly every facet of operations - from the improvement of performance efficiencies of the turbine itself, to the enhancement of predictive maintenance capabilities, to the advancement of the distribution of the energy produced -  have greatly benefited from automation. These positive impacts, of course, have helped to further solidify the viability of wind power as a current and future mainstay of the global energy mix. 

Through a performance lens, sophisticated control and positioning algorithms are now widely used to continuously adjust the position and rotation of a wind turbine’s blades based on evolving wind conditions. These real-time adjustments allow the wind turbine to maximize its energy generating potential. Further - and perhaps less obvious - this level of control can contribute to the longevity of the equipment by reducing the wear and tear that can occur when a turbine is not correctly oriented relative to wind conditions. In terms of power distribution, automated systems that control wind farms can adjust turbine operations to achieve a closer balance with the supply and demand forces of the power grid. Meaning, automated systems have the ability to throttle the output of each turbine if it seems likely that, if left unchecked, a wind farm’s output could lead to an overload of the power grid. 

AI and Machine Learning have also contributed to the overall efficacy of wind turbines through  data collection and real-time decision making, particularly when it comes to predictive maintenance.

Automation Improves Maintenance of Wind Turbines

The growing number of wind turbines and wind farms - located both on and offshore - represent a significant capital investment. Financial cost aside, wind power generation is increasingly relied upon to support the power grid, meaning that any unplanned disruption of service could have a cascading negative impact on the regions they serve. Fortunately, AI and Machine Learning, as alluded to in the previous section, is being leveraged in order to mitigate these disruptions while keeping the infrastructure up and running. 

Thanks to these technologies, mechanical and electrical faults are detected, triaged, and addressed much more quickly and efficiently than those uncovered during physical examinations during a regularly scheduled maintenance check. More precisely, AI and Machine Learning can determine if and when physical intervention by a wind turbine technician is warranted versus a remedy that can be actioned remotely by the system without the need for human intervention. AI can assist with resource allocation, scheduling technicians to work the most urgent tasks first, rather than completing service on a “first identified, first served approach”. 

Inspecting blades for damage can be difficult given their size and location. Now, AI-powered robots can be leveraged to inspect and repair damage identified on the blade’s leading edge. This AI assisted inspection and repair not only keeps human wind turbine technicians safe, it can also be completed up to four times faster than conventional repair methods. Robots can also inspect locations within the wind turbine that are simply inaccessible to wind turbine technicians. These robots are used to inspect, photograph, and tag the location of deficiencies in the blade and turbine materials, giving maintenance crews a much needed head’s up in their ability to perform preventative maintenance in such areas. 

Lastly, automated systems that are configured to monitor the operation metrics of a wind turbine or wind farm can apply the data to predict any potential failure of the equipment. Since technicians can be alerted to an issue prior to failure, the incidence rate of lost energy generation caused by unplanned downtime is greatly mitigated. Since equipment can be repaired before calamitous failure, energy providers can often reduce expenditures on replacement parts which positively impacts the performance of the operation.

Automation: An Inevitable Gamechanger of the Wind Energy Sector

As with any other industry that is leveraging automation, the wind energy sector is about to embark on a new,  evolutionary journey the result of which will be a more stable, sustainable, and cost-effective energy source.

Everything, from the energy that is gathered and distributed on a wind farm, to distribution, storage, and maintenance activities can be made more efficient through the use of automation technologies. It will be interesting to see how, as automation technology improves, the operations and maintenance activities performed on wind farms will benefit. 

If you would like to learn more about the wind energy sector or how to transition to a career as a Wind Turbine Technician, speak with a program consultant at George Brown College today by dialing 1-888-553-5333.