Credit: NRG Systems

ZX TM is a nacelle-mounted Lidar that uses continuous wave technology to measure the full shear and veer wind profile across a turbine. The remote sensing device has been successfully evaluated by DNV, an independent engineering firm, and UL, a global safety science leader, to support wind turbine power performance evaluations.

This technology aims to minimize measurement uncertainty via its availability and sampling rates. In addition to power performance testing, ZX TM is also suitable for nacelle transfer function calibration, including yaw alignment and wake detection.

Working with DNV and UL, NRG Systems, will provide ZX TM as part of a turnkey solution that includes installation, field support and data management, with the option to calibrate using an IEC-compliant mast at UL’s test site in the United States or DNV’s test site in Janneby, Schleswig-Holstein, Germany.

“NRG Systems is always working to push the boundaries in wind technology,” said Gregory Erdmann, VP of global sales at NRG. “We have been doing it for 40 years and we are very selective in which technology we develop or partner with to offer the best possible solution. As the wind industry continues to evolve, that means providing cutting-edge technologies to optimize each stage of development and operation. We are excited to be broadening our relationship with ZX Lidars as well as building on our decade-long remote sensing experience by adding ZX TM to our turnkey wind measurement solutions.”

NRG Systems has been selling and supporting ZX Lidars’ technology since 2019, offering the ZX 300 onshore vertical profiler as well as ZX 300M for near-shore or platform-based offshore applications. The product portfolio is further supported by the ZX North American Service Center operated by NRG Systems.

“Our team installed the first nacelle-mounted Lidar in 2003. Today, we support clients globally with operational wind farm measurements, understanding what wind turbines actually see once constructed,” said Ian Locker, managing director of ZX Lidars. “Combining the unique measurements of ZX TM, with the customer support, care and attention from NRG Systems is a great partnership. Approved for use by DNV and UL, clients can be confident in their choice of Lidar and are in great hands in-country with NRG Systems.”

News item from NRG Systems

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Harnessing the wind is not exactly a perfect science.

From taller and more efficient wind turbines to the development of wind farms in increasingly complex onshore and offshore environments, the wind energy sector is constantly evolving, and new techniques and technologies are becoming available to ensure that turbine and wind farm performance meets expectations and contractual obligations. One such technology is the use of lidar sensors in power performance testing (PPT) to easily and efficiently adhere to industry standards and best practices.

Power Performance Testing

Wind energy industry best practices include comparing actual turbine performance on a site with the contracted performance often used as the basis for energy and revenue estimation. A key parameter of the economic value of a wind project, the power curve is determined through PPT.

The power curve shows the relationship between turbine power and hub height wind speed, essentially capturing turbine performance. By plotting the power versus wind speed, the power curve compares on-site results to that of the warranted power curve. During the commissioning phase of a project, PPT data helps verify the configuration and installation of turbines to ensure the wind turbine is set to deliver the expected power output. Beyond turbine performance verification, PPT is also conducted for regulatory compliance and warranty verification.

To verify turbine performance, PPT often happens once they are operational on a wind farm. A detailed, IEC-compliant test and analysis can ensure that projects are performing as they should, maximizing the annual energy production (AEP) and potential revenue of a wind farm.

Given the increasing size of turbines (both on- and offshore), even if there’s a slight difference in the real-world production compared to a turbine’s expected power production, that can mean a significant — and costly — change in the project’s economy. In fact, data from the Electric Power Research Institute (EPRI) reveals that just a 1% decrease in annual production can reduce the revenue of a wind farm with 100 2-MW turbines by up to $500,000 per year. While developers and operators expect to get the energy output promised, without accurate PPT data, it’s impossible for them to determine whether each turbine is performing as it should.

Meteorological evaluation towers — commonly called met towers or met masts — and calibrated turbine-mounted cup anemometers have traditionally been the primary options for conducting the wind measurements required to verify power performance. However, turbine anemometers are highly uncertain because of their location behind the turbine rotor, and as turbine rotor sweeps grow beyond 200-m in height, met towers are not able to deliver PPT data with the vertical measurement and accuracy required. Plus, installing a met tower onshore for PPT is expensive and needs time, while using met towers for offshore wind farm environments is impractical due to the requirement of a multimillion-dollar foundation being constructed out in the ocean. Because met towers have been the only option for some time, there are very few PPT campaigns performed in the field.

With turbines continuing to grow larger, performance testing and verification is becoming increasingly important as underperformance equates to reduced power output and significant lost revenue. That’s where lidar comes in.

Lidar for Power Performance Verification

Whether developing or operating a wind project, lidar sensors help decision-makers understand what the wind is actually doing at a given site. Ground-based vertical profiler lidars are already regularly used for PPT after the release of IEC 61400-12-1 Ed. 2 Standard. Alternatively, by accurately measuring the full wind regime and characteristics of the wind flow, including wind speed, wind direction and turbulence all the way up to 700 m in distance, the most advanced nacelle-mounted lidars allow for data that is easily attainable, accurate and in line with industry PPT best practices.

Adhering to wind industry best practices and an upcoming IEC standard, advanced lidar sensors are able to mount temporarily on or fully integrate into the nacelle, enabling operators and wind turbine original equipment manufacturers (OEMs) to efficiently and accurately assess turbine performance. PPT can also be performed using a ground-based vertical profiling lidar, and the choice of which one to use is specific to each use case.

In addition, some out-of-the-box software platforms streamline the delivery of power performance data in a simplified fashion, making it accessible to wind industry companies of all types and sizes. By combining both lidar data and supervisory control and data acquisition (SCADA) turbine performance intelligence, these data analytics software tools simplify lidar data and rapidly deliver quick, easy and transparent IEC-compliant PPT calculations, empowering customers with the ability to focus on the most essential performance analysis work.

Today, nacelle-mounted lidars are increasingly mentioned or included in manufacturer turbine supply agreements (TSAs) as the means to verify offshore turbine performance. Both wind farm developers and wind turbine manufacturers have identified nacelle-mounted lidar as a unique alternative for the commissioning of wind turbines offshore. And the development of IEC standards that enable use of nacelle-mounted lidar in offshore environments will also advance the practice for onshore wind farms.

IEC standards are always evolving, and a new standard formalizing the use of nacelle-mounted lidar to conduct PPT is set to be released in 2021. The UniTTe project saw developers, wind turbine manufacturers, consultants and lidar manufacturers collaborate on the research and development of industry best practices using nacelle-mounted lidars before they were integrated into the IEC standard. Because leaders in the wind energy industry know the new standard is around the corner, they are actively preparing their companies for when it is released, testing the concept to ensure they can hit the ground running with lidar next year.

Advantages of Lidar in Wind Measurement

Lidar provides the most quantitative and accurate measurement technique for wind energy applications. When compared to a met mast, lidar is much faster to install, deploy and collect data as early as the prospecting phase of work through operation. Nacelle-mounted lidars have already been installed on over 100 types of manufacturer turbines, and customers around the world are actively using ground-based vertical profilers for PPT. From the technology’s ease of use and cost efficiency to the time savings it enables and ability to optimize wind collection, even at the tallest hub heights, lidar is crucial for power performance verification.

Time Savings

While it can take months to secure the permitting required to install a met mast, especially as they grow taller, there’s no permitting required or tower to build with lidar. When used in early stages of wind farm development, lidar sensors are not only mobile and compact, but they also deploy in a straightforward and easy manner, shaving weeks or months off a project’s completion date. Nacelle-mounted lidars continuously follow turbine direction and are then aligned with wind direction. Thus, there’s no need to wait for the turbine to be aligned with met mats, and PPT can be completed extremely rapidly.

Ease of Use

Lightweight and portable, lidar technology can be positioned virtually anywhere — if developers need to move the sensor for additional measurements on the site, doing so is relatively easy. Wind sensing lidars have small footprints and are turnkey to use and provide ongoing measurements at multiple heights. Installing a nacelle-mounted lidar on a wind turbine is also a developing practice thanks to wind stakeholders, in particular wind turbine and lidar manufacturers, facilitating lidar operations from growing field experience, precise lidar-mounting guidelines and overall logistic benefits.

Cost Efficiency

As hub heights and rotor planes grow, wind behavior is not representatively measured from the single hub height measurement point a met masts delivers. Thus, with turbines growing larger, met masts are becoming prohibitively expensive to install and maintain. Plus, by measuring at multiple heights and distances, lidar technologies provide a more robust view of the wind for no additional cost.

With wind turbines harvesting nearly half of the renewable energy used to create electricity in the United States, power performance verification is crucial in advancing wind energy projects. And with turbines increasingly growing in size, the traditional method of using a met tower alone to collect PPT data aligning with industry best practices is no longer viable. Lidar, however, enables the collection of data that is easily attainable, accurate and in line with industry PPT best practices. Yes, wind farms are an expensive, long-term investment, but the ease of use, time savings and cost efficiency lidar sensors unlock combine to create a more certain energy generation system.

Matthieu Boquet is Head of Products & Offering for Leosphere, a Vaisala company. In this role, he drives Leosphere’s WindCube lidar offerings to meet the industry’s high-level expectations while helping customers continually generate value from their lidars. Download this webinar to learn more about lidar sensing and PPT.

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New research released in 2019 showed wind speeds had increased across North America, Europe and Asia since 2010 — and the trend is expected to continue. This study featured in the journal Nature Climate Change offered a stark contrast to the majority of previous research, which has demonstrated a long-term reduction in wind speed. Naturally, this has resulted in some excited assertions of a possible boom for wind power in the near future.

It’s certainly stating the obvious to suggest that a strong wind resource is fundamental to the success of wind power as an energy generating technology. However, the assumption that this potential change in wind speed will have a significant effect on the wind industry must be taken with a pinch of salt — particularly as the increase in wind speeds plateaus at 3 m/s, far from the 5 to 7 m/s that turbines require to generate electricity.

The suggestion of increasing wind speeds in the near future cannot be depended upon as a means to increase annual energy production (AEP). To maximize AEP across their portfolios, asset owners must focus instead on increasing their understanding of each asset and how they relate to the surrounding environment. Taking an AI-driven, contextual approach to asset optimization will maximize and secure project returns.

Wind vs. turbine

Wind power differs from oil, gas or coal power generation in countless ways, but a key contrast is in terms of consistency of resource intake. For coal, oil and gas, an operator has control over intake. This makes it far simpler to identify underperformance. When generating energy from wind, however, there’s no control over resource intake. Wind resource can change completely in a short period of time, making it much more difficult to understand the reason for underperformance: has there been a drop in wind speed, or is there a fault within the turbine?

The indicator most operators use to assess asset performance is “turbine availability.” Availability is a simple indicator of an asset’s reliability and potential for energy generation — after all, if the turbine is turned off it will not produce energy. However, “availability” tells owners nothing about whether the asset is performing as it should be with the current wind resource, or why it isn’t.

When availability is used to benchmark performance, longstanding underperformance issues are labeled as wind speed variation or missed entirely. This means that operators do not recognize low-level performance issues caused by faults within the turbine or misalignment, for example. A complete understanding of underperformance — and the extent to which it is due to resource versus technology — will empower asset owners and operators to increase AEP by up to 5%.

Context is key

Low wind speed is not the only environmental factor that can reduce energy production. There are a number of other contextual influences acting on a wind turbine that can impact performance:

• Ice buildup on turbines during colder months leads to increased loads on the blades, making it more difficult for the rotor to turn.
• Turbulence and blockage caused by nearby buildings, trees or other wind turbines can drastically alter the energy yield in comparison to initial forecasts.
• Imposed curtailment due to noise, the grid or local wildlife.

While SCADA data can indicate whether a turbine is underperforming, asset owners must understand how the environment is affecting each of their assets in order to find the root cause. To identify environmental issues such as blade icing or blockage as the source of any anomalies or patterns of minor underperformance, data collected from the turbine must be set in context.

Identifying and analyzing these environment-based issues requires digitizing the surrounding environment. 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 AI can analyze and compare data streams from within the turbine and the surrounding environment and flag problems and quickly advise on their solutions. This allows operators to act on issues before they significantly curtail performance.

A fine line

A wind farm’s power curve, using Clir software.

Misalignment of pitch and yaw are common sources of turbine underperformance. This should make resolving misalignment a priority for asset owners looking to maximize portfolio performance — after all, angling the nacelle away from the wind by as little as 4º can reduce AEP by 1%.

Yaw misalignment is one of the simplest fixes to increase AEP as it is often due to sensor or controller error that will be rectified by replacement of the faulty part. However, identifying misalignment can be a complex, long-winded process, requiring operators to sift through a significant volume of data comparing the performance of individual turbines to others across the wind farm.

In contrast, using digital tools to compare power curve data between peer turbines to identify whether pitch or yaw has been misaligned massively reduces the time-cost involved in analyzing the data. As such, owners can identify and fix misalignment before it has a significant effect on AEP.

Power up

Sometimes wind farm underperformance is not due to the influence of environmental factors or issues within the turbine itself, but is the fault of an overly conservative derating strategy that curtails turbine output at a fleetwide level. While derating is a useful strategy to prevent blockage effects and increase asset lifetime, many derating strategies lower turbine performance to an excessive extent.

In order to find the optimal balance between preventing blockage effects and maximizing production, advanced analytic methods evaluate data from multiple sources within and around the turbine to provide asset-by-asset recommendations. By taking a tailored approach to their assets, owners can significantly improve annual energy production while maximizing lifespan.

Financial certainty

Understanding how assets interact with the environment and finding potential opportunities for optimization will improve far more than turbine output. A complete understanding of assets will lead to greater certainty around performance, supporting increased risk management capabilities. This can lead to lower insurance premiums, the ability to borrow more capital and achieve greater financial returns. Greater understanding of asset performance allows for certainty around predicted output for investors, setting asset owners up to secure more favorable project financing.

In short, wind resource — whether it is increasing or decreasing — isn’t the be-all and end-all of energy production. Asset owners need to move on from the predictions and claims long-term wind speed studies generate and focus on optimizing their portfolio — now. By understanding their assets’ relationship with whatever wind speed each is afforded, owners will future-proof their portfolios and ensure they perform at their best, whichever way the wind blows.

Gareth Brown is CEO 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, through to financing and operation. Gareth set up Clir Renewables in 2017 alongside Jake Gray, offering an innovative, AI-driven software solution to help owners and operators better understand how their assets are performing and provide actionable insights on how to optimize their output. Headquartered in Vancouver, Canada, the company opened its European office in Glasgow, Scotland, in 2018, and now supports 6 GW of assets globally.

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