WindESCo

WindESCo

Services for Renewable Energy

Burlington, Massachusetts 4,965 followers

Continuous Performance and Reliability Optimization for Wind Energy Assets

About us

WindESCo was founded in 2014 joining the renewable industry to complement OEM wind turbine technology with an intuitive, performance improvement solution that would function across a variety of platforms in the wind energy industry. Wind turbine owners are often faced with underperforming assets and excessive operational expenditure with limited ability to diagnose the root cause(s). WindESCo has developed a cutting edge IIoT system that leverages intelligent sensor technology and deep machine learning to address the many needs of an industrial scale wind farm. We’ve done this by partnering with, and hiring the brightest minds in their respective fields to make our vision a reality. Today, WindESCo is proud to be the technology solution provider in wind energy.

Industry
Services for Renewable Energy
Company size
11-50 employees
Headquarters
Burlington, Massachusetts
Type
Privately Held
Founded
2014
Specialties
Wind Turbines, Wind Energy, IIoT, Performance Improvement, Internet of Things, Analytics, Wind Farm optimization, and wind power

Locations

  • Primary

    800 District Ave

    Suite 180

    Burlington, Massachusetts 01803, US

    Get directions

Employees at WindESCo

Updates

  • View organization page for WindESCo, graphic

    4,965 followers

    🌬️ Dive into WindESCo's Main Bearing Pulse module, revolutionizing wind turbine performance optimization. By harnessing the power of advanced AI and machine learning, our innovative solution transforms existing operational data into actionable insights. At WindESCo, we prioritize reliability and cost-efficiency through early issue detection, significantly reducing maintenance expenses while enhancing turbine uptime. Our approach not only ensures peak performance but also maximizes customer benefits by proactively addressing operational challenges.🌍 Download the case study here: https://hubs.la/Q02HW-MX0 #WindEnergy #RenewableEnergy #AI #MachineLearning #Innovation

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  • View organization page for WindESCo, graphic

    4,965 followers

    🌬️ Learn how WindESCo's innovative Main Bearing Pulse module optimizes wind turbine performance. Our solution leverages existing operational data with advanced AI/ML for early detection of issues, ensuring reliability and reducing maintenance costs. Discover how our approach delivers significant customer benefits. Read the full case study here: https://hubs.la/Q02HBLlq0 #WindEnergy #Renewables #AI #ML #WindESCo #EnergyEfficiency

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  • View organization page for WindESCo, graphic

    4,965 followers

    💡Cooperative Control Basics: Cooperative control involves multiple wind turbines working together towards a shared objective, such as optimizing power production. Unlike isolated operations, this strategy mimics natural behaviors like flocks of birds or schools of fish to improve overall efficiency. 💡Challenges and Wake Mitigation: Traditional wind turbine operations generate wakes that can hinder downstream turbines' performance. Strategies like wake steering, which adjusts yaw positions to mitigate wake effects, are crucial. Learn how WindESCo dynamically optimizes yaw setpoints based on real-time data in Part 1 of this Mini-Blog Series. 🔗https://hubs.la/Q02H4JL40

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  • View organization page for WindESCo, graphic

    4,965 followers

    🌬️ Enhancing wind plant efficiency is key to meeting renewable energy goals. Learn how strategies like wake steering are revolutionizing turbine performance and reducing wake losses. In part one of this mini-blog series, we'll walk you through two approaches to wake steering and discuss the benefits and challenges of each. Read More: https://hubs.la/Q02GXz2c0 #RenewableEnergy #WindPower #GreenTechnology

    Are Lookup Tables Adequate for Real-World Wake Steering Applications?

    Are Lookup Tables Adequate for Real-World Wake Steering Applications?

    windesco.com

  • View organization page for WindESCo, graphic

    4,965 followers

    Cooperative control in wind plants refers to the strategy where multiple wind turbines work together to achieve a shared objective, such as power optimization. Cooperative control is common in nature (e.g., flocks of birds, school of fish, etc.), but not the norm in wind plants. Wind turbines typically operate in isolation and their operation generates wakes that can negatively impact the performance of downstream turbines, which limits wind plant production. Novel control strategies such as wake steering, designed to mitigate the wake effect through strategic yaw misalignment, have received increasing attention lately as operational data indicate that wake losses, in particular offshore wake losses, are higher than initially predicted. The impact of this is significant as even a 1% underestimation of wake losses will result in more than $2M of losses assuming typical offshore wind capacity factors and power prices. Novel control strategies for wake mitigation are needed to ensure the viability of offshore wind. Wake steering is one of the preeminent methods for wake mitigation. However, there are different ways to implement wake steering, either through a static lookup table or using model-based control (MBC). The static lookup table approach, wherein turbine yaw positions are pre-defined for a distinct set of wind conditions (e.g., wind speed and direction bins), is a common approach employed in the industry due to its simplicity and cost-effectiveness. However, their simplicity comes at a cost. Static lookup tables are limited in their ability to define the optimal yaw position for the range of atmospheric conditions that govern wind turbine wakes (e.g., atmospheric turbulence). Additional challenges arise when we consider turbines that are offline or derated for maintenance. When this occurs or whenever the actual conditions observed onsite differ from those used to generate the yaw table, the off-yaw wake steering setpoints become suboptimal and the benefit of wake steering is reduced. WindESCo Swarm™ employs MBC for wake steering. This approach leverages real-time operational data combined with engineering wake models and data-driven submodels to improve wake steering effectiveness. This advanced approach dynamically adjusts the optimal yaw setpoints for wake steering and wind plant optimization and considers the operational state of each turbine in the wind plant as well as a variety of atmospheric conditions that directly affect wake behavior, including atmospheric turbulence and heterogeneous flow conditions. While implementing and maintaining MBC requires sophisticated modeling capabilities and interfacing with the wind farm network, WindESCo is committed to this approach due its adaptability to site conditions and the promise of improved performance and wind plant optimization not possible using an industry-standard lookup table. Read More: https://hubs.la/Q02GygZM0

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  • View organization page for WindESCo, graphic

    4,965 followers

    🌬️ Harness the power of precise wind energy optimization with Swarm™ to ensure maximum efficiency through continuous yaw misalignment corrections, all seamlessly integrated into your operations. Why is continuous correction crucial? Wind patterns vary seasonally, affecting rotor-averaged wind vectors and potentially reducing power generation. Our advanced algorithm monitors these changes, ensuring turbines are always aligned for peak performance. Learn more: https://hubs.la/Q02G9Cqn0

    Automatically Correct Yaw Misalignment with WindESCo Swarm™

    Automatically Correct Yaw Misalignment with WindESCo Swarm™

    windesco.com

  • View organization page for WindESCo, graphic

    4,965 followers

    WindESCo's semi-supervised machine learning model analyzes your existing 10-minute SCADA and operational data to predict main bearing issues with over 90% accuracy. This isn't just about detecting problems—it's about preventing them before they impact your operations. 💪 In our latest case study, we delve into: 🔍 Main Bearing Pulse: Transforming predictive maintenance 🔍 Traditional vs. WindESCo's anomaly detection methods 🔍 Real-world success: What we uncovered at our client's site https://hubs.la/Q02FzJ1b0

    Case Study: Main Bearing Pulse

    Case Study: Main Bearing Pulse

    windesco.com

  • View organization page for WindESCo, graphic

    4,965 followers

    WindESCo Swarm utilizes high-frequency SCADA data to detect and correct yaw misalignment automatically. This means continuous optimization of turbine alignment without manual intervention, boosting power production. Why is this crucial? Wind conditions change seasonally, affecting turbine performance. Our algorithm ensures turbines are aligned with the rotor-averaged wind vector, adapting dynamically to maximize output. Read More: https://hubs.la/Q02DKxzN0

    Automatically Correct Yaw Misalignment with WindESCo Swarm

    Automatically Correct Yaw Misalignment with WindESCo Swarm

    windesco.com

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Funding

WindESCo 3 total rounds

Last Round

Series unknown

US$ 9.0M

See more info on crunchbase