Turbines blowing in the wind? Use service data to de-risk remote assets

Sara Cerruti Profile picture for user Sara Cerruti March 26, 2024
Summary:
Predicting the future using service data is fundamental to reducing downtime.

Wind Turbines in Oiz Eolic Park © Mikel Martinez de Osaba - Canva.com

There are more than 8,000 parts to one wind turbine, according to National Grid, and they can have an operational lifespan of up to 25 years. It doesn’t take a genius to realize that this represents a considerable maintenance challenge.

With so many parts to maintain and almost certainly upgrade over a considerable lifetime, it’s no surprise that questions are consistently asked as to the commercial viability of wind turbines. Ensuring uptime is increasingly key, not just to the success of renewable energy companies but also to the global Net Zero goals.

Like many energy sources, wind turbines are also often situated in remote locations, further adding to their complexity of maintenance and operations - and they’re getting bigger too. As one report found last year, the push for larger turbines will mean increased mechanical breakdown issues, component failures and serial defects. Spare a thought for Hywind Scotland, the world’s first floating offshore wind farm, which is coming offline for three to four months for “heavy maintenance” this summer. Among other things, this will mean that five giant turbines will be towed back to shore in Norway this summer.

Renewable energy is an exciting arena, but it’s also an industry where so much is seemingly stacked against its success. Aside from reliance on weather, the maintenance challenges alone are some of the most difficult across any industry in the world. And yet, 2024 is signalled for growth. While this is good news, of course, the focus surely has to be on maintenance. Availability, reliability and uptime of each and every turbine in operation is key to ensuring a smoother transition to renewable energy supplies. Operators need excellent visibility of each asset but also accurate data about people, parts, tooling and equipment to guarantee uptime. To reduce risk and plan outages proactively, detailed service data is needed.

There’s plenty of research proving how service data can increase uptime and improve cross functional collaboration across organizations supporting turbine maintenance and operations. Through asset performance management frameworks, as well as asset-centric service tools, organizations are now taking an asset-centric view of their businesses, identifying problems before they occur. Unplanned downtime is not just being tackled, but in many cases reduced by over 5%, with false alarms decreasing by as much as 75%. This has a knock-on effect on minimizing maintenance costs, in some cases by 25%.

A key part of this is service optimization – if organizations can predict better, they can plan better. IoT networks are increasingly key here, but the data derived from these networks has to have relevance. It has to be managed in a wider context, recognizing that each and every maintenance issue is actually an organizational issue that impacts people, purchasing and finance, supply chains, logistics, marketing and sales.

As Deloitte suggested in its report Next Generation Customer Service: The Future of Field Service, to transform to next generation field service, businesses need a 360-degree view of customers and assets, but that could also be applied to the broader business. Unifying organizations, bringing departments together and collaborating regardless of location, demands centralized and easily accessed service and customer data.

Remote first

A remote first approach, with the ability to diagnose issues before they occur and then plan accordingly, makes a lot of sense for wind farms and other areas of the energy industry, given their often-remote locations and large maintenance challenges. Reducing unnecessary and costly truck rolls is important here, as well as giving engineers all the intelligence and tools they need to improve first-time-fix rates.

Of course, breakdowns happen but that ability to predict and optimize responses can massively reduce risk in wind farm maintenance (and maintenance of other similarly remote assets in locations such as oil rigs and power plants). While weather understandably plays a key role in the scheduling of maintenance plans, it needs to be better coordinated with available resources.

This is where the value of service data comes into its own. It can be leveraged in multiple ways. It enables supply chain visibility, for example, ensuring the right parts are on hand at the right moment to execute jobs. Reliable asset history data can ensure an organization is providing the right level of maintenance to the correct assets. This clear visibility into the equipment is needed to manage any outages, to minimize impact from downtime. For example, in the case of heavy maintenance on an offshore wind farm, will the supply ships be available to deliver resources to the turbines needing maintenance? When will towboats be needed to bring major components back to shore for maintenance?

Like most large projects, coordination is key. In the case of complex outages where crews of workers with different skills need to be on hand at different moments in time to execute these outages, do businesses have all the data available to know exactly when workers are available? How many workers are needed and with what skillsets? Also, does the data show what other resources will be required, such as vendor support? These are all key considerations that service data can answer accurately.

It’s also what makes digital checklists invaluable in the maintenance process, as part of a data-driven approach to planned downtime. This can mitigate execution risks, as well as improve decision making and project control. Checklist data captured during previous outages can be utilized to predict what challenges current projects could be subject to. Data combined with AI and analytics can allow businesses to be both more predictive and proactive. Through a comprehensive understanding of assets and risks, businesses can be better placed to prevent issues during outages, as opposed to responding to issues once they have already occurred.

Predictive and proactive maintenance have become fundamental to de-risking operation and maintenance of remote assets. The service data derived from these assets is the new lifeblood of business and the sooner that is gathered and effectively utilized, the better it will be, not just for individual wind farm and energy operators, but the whole industry in the drive to NetZero.

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