Striking the balance: how to run wind assets at maximum productivity, for maximum lifetime

Credit: Enel
Whitney Hill Wind Farm. Credit: Enel

More than ever, wind farm owners are looking to run their projects as hard as possible for as long as possible. However, a misconception shared by many asset owners across the industry is that any effort to increase asset output in the short term will decrease asset longevity.

A key question that owners are now asking is whether energy production and project returns can be maximized whilst optimizing operations to support an extended turbine lifetime. In order to answer this question, we have to look at how and why wind assets are failing to meet their full potential.

It’s probably not unfair to suggest there has been a history of operating wind farms without optimizing each turbine for the real-world environmental conditions the turbines are deployed in. This is because the industry’s standard operations and maintenance (O&M) contract structures and data collection systems are built around achieving maximum “turbine availability” and idealized operating assumptions. In short, they do not set turbine performance data in its real-time, real-world context.  

One effect of this lack of in-depth asset understanding is that many owners are hesitant to increase asset output due to fears that optimization will shorten the asset’s lifetime – but this assumption leads to potential profits being left on the table.

An industry held back by assumptions

The O&M contracts and data collection systems used across the wind industry are a legacy of power industries where turbine performance is simple to understand. For example, across the oil, gas, and coal industries intake conditions are strictly controlled and therefore highly predictable. However, in the wind industry there is no way to accurately measure resource conditions and turbine loading.  This uncertainty leads to all kinds of assumptions about performance that influence turbine design, build, and operation, ultimately hiding underperformance.

Uncertainty of asset performance therefore leads to the industry accepting contracts and data management plans that depend too much on assumptions, rather than real-world conditions and turbine data. This sees many highly profitable wind assets lose out on potential energy generation and returns. In order to maximize turbine output whilst maximizing lifespan, it is crucial for owners to base their decisions on real data, rather than assumptions.

One assumption held by many in the industry is that that any efforts to increase asset output will decrease the asset’s longevity. However, for a number of factors, this is not the case.

Striking a balance

A common approach to maximizing asset lifetime is to take a cautious outlook towards O&M. This often involves running the wind turbine to the pre-construction design assumptions. While it’s true that cautious asset operation may increase overall operating lifetime and reduce the need for repairs, over-cautious operations can significantly decrease an asset’s annual energy production (AEP) and drive down project profitability.

For example, a wind farm may suffer grid restrictions that require the turbines to be frequently curtailed from the wind – otherwise known as derating. Derating protects the turbine and limits the wear-and-tear it is exposed too – a strategy which increases project life but ultimately reduces AEP. However, this additional protection can be an opportunity to run the turbine more aggressively than the pre-construction design assumptions allowed. This could be through optimizing pitch, yaw, or increasing the cut-out wind speed – all of which lead to an increase in turbine output.

In-depth analysis of wind farm data is crucial to understanding how cautious O&M strategies need to be for assets to continue to generate energy at maximum efficiency throughout their expected lifetime and beyond. However, to do this, owners must examine the relationships between multiple streams of data. For example, environmental and turbine data should play a central role in informing derating strategies. A broad-brush derating strategy chosen with the intent of maximizing turbine lifetime across the whole wind farm can be appealing for protecting asset health – but many asset owners could do with taking a more nuanced approach to curtailing their assets.

By analyzing data to understand the relationship of the wind resource to individual assets across the wind farm, it is possible to strike a balance between turning down performance to maximize the life of each asset and ensuring productivity doesn’t take an unnecessary hit.

Follow the data

Forested sites often see large decreases in available wind resource as the wind is slowed down by the drag of the forest canopy, which also increases the fatigue load cycles the turbine is exposed to as a result of gusts and turbulence. By digitizing the environment around the turbines, it is possible to understand the impact of forestry on each asset. Owners can then carry out a targeted felling strategy that could see the windfarm achieve a 10%+ AEP increase whilst simultaneously protecting the asset from fatigue loads by smoothing the wind. These joint benefits of increased output and decreased wear-and-tear apply to many other factors, such as icing and yaw misalignment.

This level of asset understanding is almost impossible to achieve through traditional data analysis methods. However, advanced, deep domain methods of data analysis using machine learning and artificial intelligence (AI) can analyze and compare data streams from within the turbine and the surrounding environment, pinpointing the reasons behind underperformance and identifying faults. This means that operators can ensure downtime for O&M is used effectively, efficiently, and only scheduled for when necessary.

Early identification of turbine faults and set-up errors via comprehensive data monitoring and automated analysis can prevent long periods of suboptimal energy generation, as well as support a longer turbine lifetime. If faults and errors are not identified early, they can lead to increased fatigue loads, unscheduled downtime, increased maintenance costs, and lost production.

Evidence of static yaw error. Credit: Clir.

At Clir, we recently monitored and analyzed data for a subset of turbines at a 200MW wind farm. Through our artificial intelligence-supported platform, we were able to identify minor patterns of underperformance which indicated that the turbines were misaligned to the incoming wind direction. By fixing these misalignments, production across these turbines increased by up to 3% each year and removed unbalanced load on the turbines, decreasing wear-and-tear.

Run assets harder, for longer

The misconception that any efforts to increase asset output will impact longevity is clearly outdated. A comprehensive understanding of how the surrounding environment affects each asset can generate actionable insights into how to maximize wind farm AEP whilst simultaneously decreasing wear-and-tear.

Owners and operators who update their outdated operations strategies to reap the best possible returns from wind projects will often also secure an increase in asset lifetime. By analyzing turbine data in its environmental context and acting on the results, production will be maximized without requiring investment in new turbines before the initial lifetime is up.

In-depth analysis driven by AI is necessary to quickly find and compare all turbine and environmental data and indicate underperformance and faults. With this level of insight, owners and operators are able to apply the right level of caution to operating their wind assets – striking the balance between maximizing performance and supporting production across the full turbine lifetime.

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Gareth Brown is CEO and co-founder of Clir Renewables, a renewable energy AI software company. He is an entrepreneur and a chartered engineer with the IMechE. Gareth has over a decade of experience in the industry which spans the life-cycle of renewable energy projects from identification, development, construction, to financing and operation. In 2005 Gareth started with Scottish renewable energy technical consultancy SgurrEnergy (now Wood) in Glasgow. He brought their operation to Canada, setting up the Vancouver office and leading the expansion across the Americas. Gareth began to notice a trend in the wind industry - most, if not all, wind energy asset owners and operators do not have an accurate view of asset performance. After parting ways with technical consultancy, Gareth and his co-founder, Jake Gray, launched Clir Renewables at the beginning of 2017, offering an innovative software solution to help asset managers and owners maximize production, and give owners clarity on performance. Gareth believes that utilizing the advances in machine learning and artificial intelligence is an effective way to analyze and optimize wind turbine performance. Headquartered in Vancouver, Canada, the company opened its European office in Glasgow, Scotland, in 2018, and now supports 5 GW of assets globally.

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