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- Category: Articles
Using Drone Data to Calculate Blade Erosion AEP Loss
It is well-known that blade surface degradation, especially leading-edge erosion, leads to increased risk for turbine operators. The operational risk consists of structural and aerodynamic components, and both should be considered when planning the most effective site-specific operations and maintenance strategy.
By Nicholas Gaudern, Chief Technology Officer, PowerCurve, Denmark
- Category: Articles
Ensuring Precise Detection and Planning When Severe Weather StrikesImagine a severe storm system in the North Sea battering a wind farm, with offshore turbines experiencing excessive structural loads from high winds exceeding 160 kilometres per hour, heavy precipitation eroding multiple blades, and thick cloud cover leading to substantial ice build-up, forcing prolonged curtailment.
By Matthieu Boquet, Head of Wind Operations, Vaisala, France
- Category: Articles
AI-Assisted Blade Experts Achieve Best Damage Detection PerformanceEfficient wind turbine operation hinges on the condition of the blades, making effective damage detection crucial. Traditional methods are often slow and prone to errors. However, integrating artificial intelligence (AI) into blade inspections offers a promising solution. AI’s ability to process data and recognise patterns enables swift and accurate detection of defects, enhancing efficiency and reliability.
By Kevin Lind, Chris Simpson and Emma Hemus, Perceptual Robotics, UK
- Category: Articles
Analysing the Correlation Between Blade Noise and Erosion SeverityThe ability to detect and quantify leading edge erosion (LEE) on wind turbine blades is important to improve power efficiency and to develop predictive maintenance strategies that can be used to identify damage early, allowing more efficient, proactive repairs. LEE leads to an increase in roughness, an increase in airfoil drag, local variations in the boundary layer, and, above all, changes in the aerodynamic performance of the blade, which in turn affects the noise produced by the blade as the turbine operates.
By Obdulia Ley, Subject Matter Expert in Acoustic Emission, Mistras Group, USA
- Category: Articles
Wind Turbine Performance in Volatile Markets
This article explores the uncharted territory of wind turbine performance in volatile electricity markets. As wind energy becomes increasingly integrated into electricity grids, a new challenge arises – price volatility can negatively impact revenue, even when production is high. To address this issue, DTU Wind proposes shifting focus from ‘how much’ energy is produced to ‘when’ it is produced. The article introduces a new performance metric – the annual energy value (AEV) – which accounts for price volatility and demonstrates how wind turbines can be optimised for volatile markets.
By Andreas Bechmann, Senior Scientist, DTU Wind and Energy Systems, Denmark
- Category: Articles
Challenges in Offshore RenewablesThe offshore renewables industry is painfully aware of the status of the power cable as an essential but weakest link in the chain of generation. Cables represent about 10% of the capital cost of a wind farm but 80% of the insurance claims. This imbalance is precarious and represents a real threat to achieving an economic net zero. Fibre-optic sensing has proven its worth over the last decade in protecting the cable from over-temperature events, and distributed temperature sensing (DTS) is now very much essential kit. Industry advances in the deeper analysis of DTS and benefits from the adoption of sister technology – distributed acoustic sensing – are bringing some huge advances in understanding cable condition. This article explores the wealth of information that these new techniques can produce and highlights how previous barriers to adoption are being overcome.
By Dr Chris Minto, co-founder, Indeximate Ltd, UK
- Category: Articles
Vibration Anomaly of Wind Turbines Using Machine Learning and Statistical Methods
The global surge in wind energy adoption has propelled the proliferation of operational wind turbines, presenting a monumental challenge for O&M teams tasked with managing this expansive fleet of assets. This article sheds light on the transformative potential of leveraging vibration data alongside advanced data analysis, statistical techniques, and the power of machine learning models. As the wind energy landscape evolves, this holistic approach seeks not only to model but also to predict the nuanced behaviour of various wind turbine components.
By Thiago Kleis, Global Sales Executive, AQTech, Portugal
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