As wind turbine design advances and existing fleets age, there is a case to be made for implementing system upgrades, but how do you know if it is worth the initial investment?
VANCOUVER, BC: Clir Renewables has released a new software feature to support the validation of turbine upgrades. Whether it is a software or hardware upgrade validation needn’t only be theoretical.
As the design of wind turbines advances reviewing the performance of the existing fleet can highlight areas where improvements can be made by upgrading the current hardware or software. Original Equipment Manufacturers (OEMs) and third-party companies are regularly offering owners ways to improve the performance of current turbines. These upgrades can be physical changes to the turbine like aerodynamic enhancements such as vortex generators, gurney flap or leading edge tape. Software-based changes could include new blade pitching algorithms, improved yaw control or increased cut-out and re cut-in wind speeds. Loads on turbine components may be increased, depending on the nature of the upgrade. The benefit of increased Annual Energy Production (AEP) should be carefully weighed against the implications of increased loading.
As owners know, no two sites are the same so how can they be sure investing in these upgrades will increase AEP? By validating the impact of the upgrade against the claimed gain by the manufacturer. However, independent validation studies on specific upgrade options are scarce, or not applicable to every site. Without independent validation, owners may never know the true benefit of making the upgrade.
Independent validation is possible by implementing the upgrade on a small percentage of turbines on site, assessing the impact on AEP and evaluating it against the investment required to install the upgrade across the whole farm. Clir software has the most advanced data model in the industry which makes this validation easier to complete than via traditional methods. By integrating SCADA, CMS, meteorological, geospatial data and much more to build predictive models of turbine behaviour, Clir software can identify changes in a turbine’s performance and benchmark it against numerous models. An assessment of the cost-benefit of the potential upgrade is then performed before full implementation.
Additionally, Clir Renewables has extensive knowledge of upgrades offered by OEMs. The data science team has conducted in-depth reviews of documentation provided by OEMs as well as contributed to conversations about upgrade implementation at many wind farm sites. Clir Renewables provides independent and impartial information based on site-specific data and OEM documents.
Andrew Brunskill, Data Scientist at Clir Renewables, said “There are few simple answers to turbine upgrades as comparing two wind farm sites to each other is like comparing an apple to an orange.
“The impact of each upgrade on energy production and turbine loads depends on the local wind climate and the specific turbine model in use at the site, among other factors, that’s why independent validation is important,” Brunskill continued.
By using Clir software to validate upgrades owners avoid expensive consultancy fees and have the information to negotiate confidently with the OEM or third-party company on the cost.
About Andrew Brunskill: Andrew is a PEng from a major global consultancy and has taken on a wide variety of roles during his career including project engineer, energy analyst, modeling specialist, and project manager. In recent times he has undertaken performance analytics across Vestas, Siemens, GE, Suzlon, and Siemens wind turbine technology.
Image: Adobe Stock
Turbulent wind inflows increase fatigue loads on wind turbines, lowering life expectancy, impacting performance and reducing annual energy production (AEP).
VANCOUVER, BC: Clir Renewables has developed renewable optimization and reporting software to quantify the impacts of forestry on wind farms. The impact of forestry on inflow conditions is often poorly accounted for when developing wind farms, and once a project is operating, there can be a significant physical effect on the life expectancy of the turbines and an adverse effect on performance.
For wind farm owners, wind turbine blades effortlessly gliding through the air when met by smooth, undisturbed inflow conditions is easy on the equipment. However, projects situated in or near forested terrain typically present additional challenges. As the wind passes over forestry canopies, wind flow is disturbed, generating more complex conditions such as turbulence and shear, creating slower uneven loads on the blades. This turbulent air stresses the blades in differing proportions increasing the level of wear and tear across wind turbine, including but not limited to accelerated blade cracking and gearbox and generator failures, reducing life expectancy.
Until now owners have not had the tools to quickly and easily understand the impact of forestry at a wind farm, often relying on often prohibitive, expensive studies to assess the impact. Clir software is built on a domain-specific data model that takes account of geospatial considerations, such as forestry, that drive turbine inflow conditions. By utilizing the geospatial information and atmospheric stability, performance issues at the turbine itself, and in the inflow conditions, can be more accurately parsed out to enable owners, operators and other wind farm stakeholders to understand project underperformance and performance risk for specific items such as forestry.
“Many owners are likely experiencing generation losses due to forestry in the area of their wind farms; the degree to which they may be unaware. Additionally, the disturbed wind inflow may be increasing turbine loading, reducing their operational life. Additional operational costs associated with premature component failures and reduced turbine service life can surprise owners who may not have accounted for this in their financial model,” said Andrew Cameron, Data Scientist at Clir Renewables.
“By assessing this early in the wind farm’s operational life many of the costs and losses can be better understood, accounted for and potentially reduced.” Andrew continued.
Once an owner is aware of the forestry impact on AEP and loading conditions, a cost-benefit analysis can be formulated to identify the appropriate next steps. When implemented the optimization recommendations can produce increases of around 5% AEP for the wind farm and extend asset life by up to five years. In situations where a wind farm’s forestry is poorly managed gains of almost 10% AEP, and 5+ years of life extension are possible.
Clir CEO Gareth Brown says: “The wind industry is unique. It’s the only power source we don’t know what the inflow conditions are. The industry to date has tried to build digital and physics-based models, but they are limited in scope and capabilities as they fail to take into account the inflow conditions. By digitizing beyond an individual turbine and what drives their inflow conditions enables Clir to more accurately build effective advanced analytics and AI tools to identify where performance can be improved.”
Brown continues, “The challenges defining performance issues due to no inflow measurement are compounded by, poorly structured SCADA data, inadequate industry performance tools and metrics that focus on the question is the turbine spinning rather than performing, and conflicted service providers or OEMs who are focused on protecting their internal know-how rather than building products to enable effective performance management. The Clir platform circumvents this by building a domain-specific data model that gives owners the tools to maximize their understanding of the asset, where lost energy is occurring and get clear actions to mitigate the lost energy for items like forestry and many others.”
About Andrew Cameron: Andrew has a masters degree in mechanical engineering and over ten years’ experience in the wind energy industry. His expertise includes both pre-construction analysis as well as operational analysis for a wide variety of wind regimes, turbine technologies, data collection techniques, and analysis goals.
Image: Adobe Stock
Blade i has a major impact on wind turbines but has historically been difficult to assess. Clir Renewables developed software to detect icing and reduce associated losses.
VANCOUVER, BC: Clir Renewables has released a new software feature which detects icing on turbine blades and measures its impact on performance. Weather is uncontrollable but assessable, and that’s what is required when mitigating icing impacts.
There are thousands of wind turbines located in cold and icing climates around the world and the estimated market potential for wind farms in cold and icing climate is over 200 GW*. Losses due to icing can range from a few percentage points all the way up to over 40% during the icing season. Blade icing furthermore causes increased loads and reduced aerodynamics, as well as health and safety concerns. Most of the time icing isn’t included in warranties or service contracts meaning the effects are difficult for owners to quantify. A simple SCADA data analysis often isn’t sufficient and it takes sophisticated analysis to pinpoint icing occurrences and quantify losses. Owners are looking for means to quantify losses and make the investment case for icing mitigation systems, but often don’t have the required information to do so.
Clir recognized this gap in information and developed a solution to automatically detect icing and quantify the associated losses. The algorithm uses a probability analysis approach to flag deviations from historical, turbine-specific power curves, based on site-specific climatic conditions and historical icing events. The icing events are automatically flagged to the , including the losses related to the events, which enables the users to make informed decisions on how to proceed. The method is based on IEA Task 19’s standardized and widely approved method for ice loss calculations^ and has been further refined within the Clir system. Based on the results from the algorithm, Clir can make recommendations for optimization when the wind farm is experiencing icing. As part of this optimization work, an evaluation of any installed ice detection and mitigation system is undertaken, and the results are also useful for the evaluation of available third-party alternatives.
Rebecka Klintström, Data Scientist at Clir Renewables, says, “Significant losses are often experienced at wind farms in cold climates due to the impact of icing, but it is possible to regain some of this when there is a better understanding of the situation. With all the information, owners have the ability to take action and improve their output. One owner saw an increase of almost 5 % AEP after a manufacturer control update was implemented following assessment by Clir. While not all sites will see such an increase, it shows that icing is an issue that needs investigation.”
When it comes to decision making, knowledge is power and this what Clir’s software provides its users. The benefits to owners, operators and site managers of having this information are the quantification of the losses incurred, evidence for the manufacturer to optimize control algorithms, and decision-support for potential investment in a third-party ice mitigation system.
“Icing loss issues are compounded as they are excluded from the contractual obligations of the OEMs or the service providers under most service contracts. This has meant the issue has not been well managed in the turbine control design or operation phase of the assets, said Clir CEO, Gareth Brown.
“The Clir software has given owners actionable insights to increase output by reducing the number of false positive shutdowns inherent in the control assumptions for health and safety and loads on the blade. It also helps owners build the business case to change the control strategy of turbines and potentially shutdown turbines during icing conditions (freezing clouds) to minimize the build-up of ice on blades. The data model is set up specifically for renewables and Clir is the only platform in the market which can handle these types of issues,” he added.
About Rebecka Klintström: Rebecka has over six years’ experience working in the wind industry, both as a consultant and for a utility. She has worked in all phases of a wind project, from pre-construction flow modeling, met mast installations and noise modeling to operational analysis, icing loss modeling, and optimizations, as well as managed research projects and project portfolios. Rebecka was a member of IEA’s task 19 for over five years and has an active role in developing the ice detection and loss algorithms. She’s been at Clir from the start and has worked with most turbine technologies, as well as starting up and heading Clir’s Inclusion and Diversity initiative.
With an increased number of assets nearing the end of their life expectancy continued assessment of component health is imperative for strategic asset management.
As Seen In: North American Clean Energy, Windtech International, ReNEWS
VANCOUVER, BC: With installed wind energy capacity now over 539 GW*, many turbines are reaching the mid to late stage of their operational life expectancy. Typically wind turbines have an operational life expectancy of 20 years, but many only operate for the duration of the Power Purchase Agreement (PPA) under which they were constructed, sometimes just 15 years. Creating a strategic plan for asset life extension is critical for efficient management of wind farms. Clir Renewables recently launched a new product feature that allows for variable scenario analysis to determine the best way to operate assets.
As turbines age, owners and operators more urgently need to know how long their turbines are going to last. They often end up setting aside increasing amounts of budget for unplanned maintenance to ensure the asset is operating in a safe and profitable manner as various components approach the end of their design life. However, an in-depth analysis of turbine data allows for the creation of strategic maintenance plans. These maintenance plans are created to prevent unexpected component failures and extend the operational life of the turbines. This is often undertaken as a one-off study assessing existing site data which can’t take into consideration true site conditions of the future. Clir Renewables has tackled the industry issue of life extension analysis by developing detection tools enabling clients to assess and quantify the potential for life extension of their assets beyond 20 years and provide guidance for their maintenance planning.
Clir’s software regularly analyses wind farm data, providing clients clear access to the information they need to make operational decisions in real time. Analytical models consider a wide range of data, including wind flow conditions, component temperatures, and vibrational data. Each data set is then compared to the turbine design and loading parameters to quantify the operational risks turbines and individual components are exposed to. The software takes design loading conditions on a certification and site basis, compares it to real-world conditions derived from turbine data to quantify what risks turbines are exposed to and to what degree the turbines are operating within themselves. This understanding gives Clir’s clients actionable information to assist in long-term strategic maintenance planning and avoid unexpected component failures.
With Clir’s software, continual assessment of component health ensures owners and operators are better informed about the condition of their assets and can build operational strategies in line with their long-term plans. The data can be fed into financial models, O&M strategies and assist in deciding whether to ‘sweat’ the assets during the PPA or extend their operational life past the agreement.
“Using automated algorithms and machine learning to assess wind turbine performance is an important advancement in the optimization of the wind industry. Continually assessing multiple data points across a site or portfolio means decisions are based on up-to-date rather than historical data. Better informed decisions reduce risk which can only be positive”, said Selena Farris, Data Scientist at Clir Renewables.
*Source GWEC: 2017 figure was 539,123MW – http://gwec.net/global-figures/graphs/
Vancouver based renewable energy software startup Clir Renewables announced as a recipient of funding from Sustainable Development Technology Canada
As Seen In: North American Clean Energy
VANCOUVER, BC: Climate change is the largest existential threat to the world today. Changing climates and extreme weather brings changes to ecosystems that are often destructive such as hurricanes, typhoons, blizzards, and floods. Climate change is caused by the unnatural amount of greenhouse gases in the atmosphere. According to studies by The World Resources Institute and the EPA, the largest contributor to those greenhouse gases is the energy sector. To mitigate the effects of climate change, innovation in the energy sector is imperative.
Renewable energy has been exponentially growing in recent years and with this growth has come major technological improvements. Wind turbines are larger and more efficient than ever before and are now the cheapest source of new electricity generation. Although hardware in the renewables industry has seen massive improvements, turbines are still underperforming. Wind turbines are highly complex machines and optimizing their control parameters to maximize the energy production of a wind farm requires lengthy technical analysis. To date, analysis has mostly been performed by consultants and engineers. The work consultants and engineers do undoubtedly help the turbines perform well. The issue in the industry is that the suite of information required to optimize turbines goes beyond the ability of individuals.
Gareth Brown was a consultant in the wind industry for 10 years, repeatedly witnessing the problem of under performing turbines. He wanted to find a way to address turbine optimization in a scalable way. His solution was to create software that incorporates machine learning with domain expertise so detailed technical analysis can be supported by technological development. With this, the vision for Clir Renewables was born and in early 2017 Gareth formed a team of renewable energy and software experts to build a tool that would allow for creative destruction in the renewable energy industry.
Among others, one of the major challenges for a new company is cash flow and financing. Clir Renewables has been fortunate to receive funding and support from Sustainable Development Technology Canada (SDTC). SDTC helps Canadian entrepreneurs accelerate the development and deployment of globally competitive clean technology solutions. Clir Renewables is dedicated to addressing climate change by creating software that will increase energy production of renewable energy assets. The funding from SDTC enables Clir Renewables to commercialize the software tools, bringing this crucial new technology to the marketplace.
Zoë Kolbuc, Vice President, Partnerships at Sustainable Development Technology Canada said, “SDTC is thrilled to support Clir Renewables as they make wind power even more efficient by leveraging the power of data and automation to optimize energy production. This is a great example of how cleantech is disrupting, and improving renewable energy production.”
“It is great for our software to get this support from SDTC as a step in the right direction in the fight against climate change. The more energy that can be produced from sustainable sources reduces the dependency on fossil fuels for energy generation,” said Jake Gray, COO of Clir Renewables.
Clir has been operating for two years from its head office in Vancouver and this year expanded into Europe with a UK office. In those two years, the company has gone from strength to strength working on over 2GW of assets, and staff numbers have increased threefold.
Yaw misalignment is causing significant production losses and is often ignored, Clir’s software allows owners to mitigate its effects.
As Seen In: Wind Power Engineering, ReNEWS
VANCOUVER, BC: Renewable energy company Clir Renewables has developed software that uses leading wind industry expertise, data science and machine learning to quantify and provide actionable insights to help mitigate the lost energy production caused by both static and dynamic yaw misalignment.
The concept of yaw misalignment is unique to the wind industry; if the wind always blew in the same direction, turbines would not need to yaw, and the process for maximizing wind farm output would be drastically simpler. Unfortunately, that is not the case!
Wind is a highly variable resource. A turbine’s control system gathers information from the nacelle instrumentation and directs the turbine to yaw accordingly. However, turbines do not track the wind perfectly. Tracking the wind perfectly would require constant yawing and eventually burn out the yaw drive. Instead, wind turbines yaw once the deviation of the wind from the nacelle position is significant enough to justify yawing.
Ideal parameters for yaw lag vary between industry players and are often defined at the turbine design phase without considering the real-world site-specific wind conditions. Control parameters may be defined to only yaw when there is a significant deviation of the wind from the nacelle, which would extend the yaw drive life at the cost of lost energy. Alternatively, control parameters may dictate constant yawing, and result in a short yaw drive lifespan but increased energy. In service agreements, turbines’ performance in both accurate alignment and tracking of the wind is not considered. This leads to the issue of yaw lag being ignored as contractual requirements are prioritized over value. The lack of attention from service providers, poor turbine design assumptions and inadequate tools to assess lost energy have all contributed to what has become an industry-wide problem. Clir’s recent technology development addresses the issue of yaw lag by detecting poorly-specified yaw parameters and physical misalignment, bringing the inefficiencies to light as they occur.
“To maximize production, wind farms owners need the ability to proactively monitor and understand the lost energy impact of inappropriate control parameters and yaw faults in their wind turbines. The tools available on the market have been discouraging for wind farm owners. They make it difficult to understand if the turbines know where the wind is coming from, if they are tracking it effectively, and don’t provide recommendations for how to combat misalignment with service providers. We’ve seen many of our clients ignoring the issues as they didn’t have the tools to monitor or understand them, and it’s costing them significant amounts of money.” said Gareth Brown, CEO of Clir Renewables.
The data from wind farms is inherently poorly-structured and noisy which makes it difficult to parse out yaw alignment impact from items like other turbine control changes, leading edge erosion, environmental conditions, and other performance degrading conditions, thus making it very difficult to understand the negative impact. To solve this problem, Clir created a data architecture and product feature that enables the early detection of yaw alignment issues. Clir’s software applies domain expertise, advanced analytics, and machine learning to separate static yaw misalignment from other underperformance issues to quantify the financial impact and provide recommendations on how to fix directly or address the issue with the service provider.
Jake Gray, a previous Vice President of a national IPP and current COO of Clir Renewables comments on the yaw feature saying, “The benefits of dynamic and static yaw monitoring and correction are substantial. Wind farm owners can use the yaw misalignment calculations to correct turbine faults, pressure OEMs to change the underlying control parameters, and to evaluate the work of their O&M provider. Where yaw misalignment has been an issue, owners have seen up to a 3% increase in AEP from the yaw correction alone.”
About Clir Renewables: Clir is a renewable energy AI software company whose industry-leading cloud-based tools help asset managers and owners maximize production, and give owners clarity on performance. Founded in late 2016, the company now serves over 2 GW of assets, raised over $5M funding in 2018, and boasts alumni from major owners, investors, developers, consultancies and software developers among its staff (DNV GL, Wood, Vattenfall, Natural Power, Amazon, among many others).