Big data in WIND POWER


The success of a wind farm is based on the maximum available power associated to wind power. The characteristics of the wind change due to the farm location and the altitude, which is responsible of the density and temperature of the air.

In order to achieve their production and revenue goals, wind farm operators must understand and manage the risk associated with the degraded performance of wind farms. The acquisition and storage of data are fundamental in gaining insight into the highest energy production.

There are three categories of data analysis that form a natural workflow on building insight and providing enhanced decision-making support: descriptive analysis, post-event diagnostics and prognostics.

– Descriptive analysis. This category of analysis identifies the main features of a data set through established statistical calculations and visual representations. Examples of descriptive analysis include calculations, such as mean and variance; and visualization techniques, such as a scatter plots, histograms and box plots.

– Post-event diagnostics. This analysis seeks to identify the cause and effect of system change. In this context, system change is generally an abrupt change that will exceed a predefined threshold.

– Prognostics. This method of analysis seeks to predict system change. The state of the system is monitored for a subtle change relative to a reference behavior, where the change is deemed to be trending toward some undesirable condition.


The main features of the data gathered from system operation are exposed through standard statistical computations and visualization methods. The primary goals of descriptive statistics are to inform and form the core of subsequent analysis. The larger the data set is, the more informative and accurate the descriptive analysis becomes. Therefore, the longer a wind farm operates, the more data it will provide.




Vestas, winner of the Deloitte’s Big Data Award in June of 2015, uses smart data to optimize maintenance of wind farms all over the world, receiving ten-minute data from 27,000 wind turbines. They are able to foresee when turbine components are malfunctioning and need maintenance before they actually get damage or broken.



The use of this type of sophisticated data is one
of the key competitive factor in the business world right now.





Alba Traver Gual


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