WindESCo

Exciting News Alert 🎉 Introducing WindESCo's #Pulse - a comprehensive end-to-end solution for performance analytics and asset health through the entire asset lifecycle. Request a demo today! 🔗https://hubs.la/Q02rDPRh0

🌬️ 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

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

🌬️ 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

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

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

With advanced monitoring of Main Bearings through AI/ML and existing data, we ensure early detection of issues, minimizing downtime and maximizing operational efficiency. Leverage Pulse for: - Improved uptime by 1-3% - Reduced O&M costs by 5-10% - Increased output by 1-3% Learn more about how WindESCo is transforming wind asset management: https://hubs.la/Q02DKzkC0

🌟 We are honored and excited to announce that WindESCo has been recognized at the Massachusetts State House for our contribution to the ClimateTech industry! Yesterday, Governor Maura Healey and other state leaders celebrated our inclusion in TIME Magazine’s Top GreenTech Companies for 2024, honoring WindESCo as well as 27 other incredible MA-based companies. This recognition underscores our commitment to driving innovation in renewable energy and sustainability. We're proud to be part of Massachusetts' climate innovation hub and excited about the future of ClimateTech. Thank you to all who support us on this journey! https://hubs.la/Q02Dy0vT0

🌍🌿 Beyond thrilled that WindESCo has been recognized among TIME's Top 100 America's GreenTech Companies for 2024! This accolade celebrates our commitment to sustainable technology and leadership in the clean energy sector. We look forward to celebrating this achievement with 27 other GreenTech leaders in MA at the State House on June 26th! Read more: https://hubs.la/Q02C_28x0 #GreenTech #RenewableEnergy #Sustainability #ClimateInnovation

WindESCo has pioneered the detection of yaw misalignment from high-frequency (HF) SCADA data. However, in many cases implementing yaw misalignment corrections can be cumbersome, requiring manual turbine controller parameter updates or even climbing up towers to make mechanical adjustments. With WindESCo Swarm, HF SCADA data is collected as part of normal system operation for executing and monitoring our collective control applications such as wake steering. Once in the cloud, yaw misalignment is calculated for all turbines on a rolling basis, so turbines can be corrected continuously with no intervention required. Why would continuous correction be required, or even helpful? Firstly, “yaw misalignment” is sometimes misunderstood as simply “make all turbines point in the same direction.” What we’ve seen is that yaw misalignment can sometimes be seasonal due to changing wind characteristics, e.g., shear and veer, which alter the rotor-averaged wind vector (RAV), as seen in Figure 1. The turbine needs to be aligned with that vector in order to maximize power production. See Figure 2 for an example of a site whose yaw misalignment, or RAV varies significantly throughout the year, meaning one static adjustment, though beneficial, leaves some potential performance on the table. In WindESCo’s yaw misalignment algorithm, a turbine is aligned with the wind vector averaged across the rotor, not just at the axis height. This rotor-averaged wind vector will therefore change height at different levels of shear and veer. Additionally, a wind sensor may be replaced, negating any previous yaw misalignment adjustments. With WindESCo Swarm, these differences are detected and corrected automatically, providing on the order of 1% additional AEP in typical cases. Reach us today if you’d like to effortlessly monitor and correct yaw misalignment at your site! Read More: https://hubs.la/Q02CQ5S20