Contributed by Ian Prowell, Principal Engineer, ONYX Insight
According to a recent report, the wind turbine component market size is set to be worth $82 billion by 2030, growing at a CAGR of more than 7% between 2023 – 2030. While this figure is driven by the number of new wind farms set to come online, upgrades to existing fields also plays a significant role in this growth.
Predictive maintenance techniques are delivering significant benefits to new and existing wind farms and condition monitoring systems and solutions, in particular, provide accurate analytical insight that helps owners make crucial informed data-driven decisions about their operations and maintenance (O&M) early to future-proof their investments.
However, many strategies to maximize turbine life and output are challenging to optimize. Often, for legitimate reasons of expediency or time pressure, over-simplification often results in sub-optimal decisions and disappointingly short-term fixes.
Relying exclusively on simulations of future turbine and foundation health; prescriptive and inflexible approaches to component maintenance; or sacrificing overall turbine life by only optimizing short-term power production are all commonly seen and it is only when alternative, data-driven predictive maintenance solutions are explored does it become clear that there better, more holistic approaches to developing the right repowering strategy.
Most wind turbines’ life expectancy is estimated at approximately 20 years, and owner/operators place great precedence on ensuring their assets operate at maximum capacity for the duration of life. Part of this process can involve either partial or full repowering of essential components to extend life or increase output.
Partial vs full repower
A repower typically increases energy production by 10-30%. Many existing strategies to maximize turbine life and output are challenging to optimize. When production is a concern, operators can generate greater output but ultimately impact the overall structural lifetime of the equipment. It can be a difficult balancing act to determine if short-term gains are worth the long-term impact. When approaching any form of repowering, accurate data, and realistic expectations are critical to decision-making.
However, repowering isn’t an easy option. Supply chain challenges affect all renewables projects creating volatile OEM pricing. In addition, CAPEX costs have increased by around 10%-15% – due largely to steel and transportation price hikes.
Regional contrasts to repowering
The uptake of repowering varies regionally and current government policy also plays a role in owners’ decisions to invest in repowering. In the United States, for example, there is a significant increase in companies seeking to take advantage of production tax credits (PTC) available via the Inflation Reduction Act (IRA).
The IRA also reduced the amount of PTCs for wind energy projects. Under the new rules, the credit is set at 60% of the value of the credit for the first 10 years of a project, and 40% for the following 10 years. This change will make it more difficult for companies to fully recoup their costs on wind energy projects, but it is still considered to be a significant incentive for companies to invest in wind energy. In California specifically, full repowering is attractive due to relatively high energy prices. Often, existing power purchase agreements (PPA) can also be leveraged in the negotiation of a new agreement or even an extended one.
In European wind markets such as Spain, where assets are highly matured, full repowering is less common. Owners tend to continue operating their wind farms with minimal additional investment as long as power production is continuing rather than shell out capital on life extension procedures.
Size does matter
As the wind industry has grown, so too has the scale of the turbines being deployed. Bigger, more efficient turbines are always likely to be perceived as the go-to option when repowering established sites, but this brings its own issues that require additional vigilance.
For example, rotors are getting larger and more dynamic (less stiff), and occasionally these will see unexpected deflections and resonances resulting in cracks and occasional premature component failures. It can also be the case for blades; larger blades can lead to increased wakes and decreased clearance from boundaries and properties.
While drive train and foundation monitoring is well established, new technology is being developed to gain greater insight into them. Rotor and blade monitoring could provide the answer to understanding the deflections and motions of the large flexible blade structures better and their potential longer-term consequences.
We are close to the point where turbines have reached their optimum scale and already the focus is much more on maximizing the efficiency of what we have, ensuring wind turbine fleets can deliver maximum returns, reducing LCoE and are optimally primed for performance for operators’ windy seasons around the world.
Similarly, when it comes to tower and foundation monitoring, there are also potential pitfalls, considering the additional fatigue demand put on foundations when repowering and the effects of installing larger blades than were initially installed. Fatigue demand becomes substantially higher on the foundations that are typically designed for only 20 years and are now expected to operate well beyond 30 years.
With many wind projects designed and built before 2010, and changes in design practice, there is a real risk of foundation and tower failure due to unforeseen causes in foundation elements that may not be visible or routinely inspected.
Owner/operators need to inspect the severity of the damage and decide whether the turbine can continue to be safely operational or be repaired or retrofitted.
Data-driven decision-making
As investment in wind energy ramps up, there is a growing need for data-driven monitoring as part of an ongoing maintenance program so operators can analyze where and when to invest in repowering and what strategy to adopt
Taking a predictive maintenance approach that includes the use of CMS systems is increasingly critical in optimizing full and partial repowering programs. For full or partial repowering, CMS can help owners receive accurate and sustained analytics to understand their asset health and make empowered decisions to optimize their remaining useful life and prioritize repairs during O&M.
This can prevent owners from paying for costly upgrades, such as foundation retrofits, on a site-wide basis and allows them to capitalize on their investments whilst securing productivity.
Technology adoption and digitizing are essential in the future of repowering. It’s cost-effective and data integration and an engineering-first approach could be the answer to understanding how to implement a repowering strategy and maintain it optimally.
Advanced sensing technologies and monitoring of towers, foundations, and blades, plus complete turbine predictive maintenance (PdM), all play a crucial role in providing accurate analytics. This massively supports visual inspections, providing a fuller, clearer picture that enables owner-operators to optimally future-proofing their assets. The key is to put in place a robust predictive maintenance strategy that monitors as much of a turbine’s health as possible, and as early as possible.
About the author
Ian Prowell has worked professionally in multiple engineering disciplines before focusing primarily on structural engineering in wind energy since 2008. He received his BS degree in engineering from Harvey Mudd College and completed his MS and PhD degrees in Structural Engineering at the University of California, San Diego.
Dr. Prowell’s PhD research focused on experimental and numerical research into the seismic behavior of wind turbines and has informed updates to both IEC 61400-1 and IEC 61400-6, among other wind energy standards. His expertise includes structural design, dynamic modeling, soil-structure interaction, and physical measurement. He has worked in onshore and offshore wind, solar, and other novel structures.
