A new scientific paper has been published by AIRE’s partner CENER in collaboration with Universitat de Barcelona and Universitat Politècnica de Catalunya. The objective of this paper is to address how precipitation affects wind energy at various levels, including wind flow, blade condition, wake development, and energy production. In this blog, we break down the essentials of these findings for anyone curious about scientific advances.

The basics

Wind turbines are vital for clean energy, but how do we ensure they work efficiently? Currently, wind turbines and wind farms are designed using models based on standard wind conditions defined using the standards of European Northern climates.

Why is that a problem?

These models don’t account for the complex physics and aerodynamics of atmospheric wind flows at high altitudes and varied terrains. Additionally, these models often overlook climate effects like precipitation and sand, which can significantly impact the design, durability, and performance of wind turbines. Understanding these deviations is crucial for designing more reliable and efficient wind turbines, as it helps address the challenges posed by real-world conditions and improves overall performance and longevity. Therefore, our researchers took on the challenge to address these critical issues.

Paper details & Process

The study was conducted at CENER’s experimental wind farm in Alaiz, Navarre, Spain, located at a high altitude of 1,100 meters in complex terrain. Researchers used various instruments, including a pluviometer, a laser disdrometer, and a Micro Rain Radar, to measure precipitation characteristics such as fall speed, droplet size spectra, and precipitation type. These measurements were taken over nine months and recorded different types of precipitation like rain, snow, and graupel. The data collected will help optimize models and tools for wind turbine and wind farm design, particularly in challenging climate conditions.

Alaiz 3-D profile (left), met mast and wind turbine distribution (top-right), MRR and disdrometer located by the MP6 (bottom-right).

Process

Researchers have uncovered diverse subjects throughout the research; here are the key highlights:

  1. Precipitation Data: The study recorded 3514.1 hours of observations, with 251.5 hours containing various types of precipitation, including drizzle, rain, snow, and hail. This comprehensive dataset is valuable for understanding the impact of different precipitation types on wind turbine blades.
  2. Blade Erosion: The data helps wind turbine manufacturers and wind farm owners understand the drivers of blade erosion in complex terrains and high-altitude sites. This information is crucial for improving the design and durability of wind turbines.
  3. Case Studies: The study included detailed observations of precipitation transitions, such as from liquid to solid precipitation. These transitions highlight the challenges in characterizing precipitation at high temporal resolution and the impact on kinetic energy calculations.
  4. Kinetic Energy: The research provided estimates of kinetic energy derived from precipitation data, which is essential for assessing the erosivity of rainfall on wind turbine blades. The study explored relationships between kinetic energy and rainfall rate, offering insights for better erosion risk management.

Main results

The study showed that rain was the most frequent type of precipitation, occurring for over 200 hours. Snow followed with 20.9 hours, while soft hail and hail were much less common, observed for 8.1 hours and 7 minutes, respectively.

Ongoing analysis compares MRR (Micro Rain Radar) estimates with data from a pluviometer and disdrometer, which could lead to improved calibration of the MRR.

Accurate classification of precipitation types proved essential. There was an agreement between precipitation types identified by the MRR and disdrometer, except for hail and mixed precipitation.

The team also defined fitting functions to estimate precipitation kinetic energy. This could help enhance the performance of standard meteorological instruments.

Next steps include combining precipitation, wind, and turbine rotation data to assess impacts on wind turbine output and blade wear over time.

Wrap up

If you want to know how advanced precipitation characterization can improve wind turbine and wind farm design, optimize models and tools, and assess the impact on turbine production and blade material longevity, read the full article.

For the full publication go to: Journal of Physics: Conference Series 2767 (2024) 042016

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Author: Sofiia Savchenko
Editor: Lucía Salinas
April, 2025