Our smart and reliable drones are changing the game using advanced robotics and A, making inspections faster and more accurate than ever before.

What’s even more impressive? Dhalion can handle 11 inspections in a single day on average, depending on the turbine model. The majority of these 150 inspections were large machines of 93m rotor diameter. This means more turbines checked in less time, without sacrificing quality. Our customer is thrilled with the results, seeing huge improvements in their inspection processes. The efficiency of Dhalion helps spot potential issues quickly, allowing for faster fixes and better overall turbine performance.

The customer’s ability to maintain a high volume of inspections consistently over the two-month period emphasises Dhalion’s robust performance and reliability in real-world wind energy applications. This efficiency not only aids in the swift identification of potential issues but also accelerates the implementation of necessary maintenance, thus improving overall turbine efficiency and reducing downtime.

Want to know more about how Dhalion is revolutionising wind turbine inspections? Book a demo with us! 🌐✨ 

This success story is just the beginning of how cutting-edge tech can supercharge the wind energy sector!

The post Over 150 Inspections in 2 Months by a single drone! 🛰🌬️ appeared first on Perceptual Robotics.

This approach allows us to estimate the Annual Energy Production (AEP) loss and consequently, the associated revenue loss with much higher accuracy than traditional methods.

Simple estimates that rely solely on the severity of damage can significantly overestimate or underestimate true LE losses.

To derive sensible values, it’s crucial to consider: 

• The severity of the damage 
• The size of the damage 
• The location of each damaged region on the blade 

Aerodynamic analysis, which is the core of our methodology, addresses these factors comprehensively. This allows us to accurately calculate losses for an entire blade or even pinpoint losses associated with a specific region of damage. 

Our approach ensures that you have the precise information needed to prioritise repairs and optimise turbine performance. 

As an example for the LEE damage shown in the image, our estimations indicate that this damage alone, due to its severity, location, type, and size, will result in a yearly AEP loss of approximately 6.2% in our aerodynamic model. This particular damage is categorised as a level 5 and is about 3.6 meters long. Based on an average approach to incomes/production (customisable to reflect local pricing), this translates to an annual loss of about £4,300 (€5,117) in AEP. By turning the image of damage into actionable results, we can help prioritise repairs effectively.

The list of benefits for asset managers and owners/operators includes:

Improved Decision-Making

By understanding the precise impact of each damage on AEP loss, managers can prioritise repairs based on actual performance data rather than relying on generic severity estimates.

Enhanced Revenue Protection

By accurately estimating the AEP loss and associated revenue loss, asset managers can better understand the financial impact of turbine blade damage. This knowledge allows for more accurate budgeting and financial planning, helping to protect revenue streams. Additionally, precise calculations can support negotiations with insurance providers or warranty claims, ensuring that site managers receive appropriate compensation for damage-related losses.

Extended Turbine Lifespan

Effective prioritisation of repairs based on accurate damage assessments helps extend the lifespan of blades. By addressing critical damages promptly and efficiently, site managers can prevent minor issues from escalating into more severe problems that could lead to costly replacements or extensive downtime. This proactive maintenance approach contributes to the overall longevity and reliability of the wind farm.

The post Leading Edge Erosion (LEE) Progression: Precision with an Aerodynamic Model appeared first on Perceptual Robotics.