Part 2: Joint multifractal analysis of available wind power and rain intensity from an operational wind farm

Abstract. Wind power production plays an important role in achieving UN’s (United nations) Sustainable development goal (SDG) 7 – affordable and clean energy for all; and in the increasing global transition towards renewable and carbon neutral energy, understanding the uncertainties associated with wind and turbulence is extremely important. Characterization of wind is not straightforward due to its intrinsic intermittency: activity of the field becomes increasingly concentrated at smaller and smaller supports as the scale decreases. When it comes to power production by wind turbines, another complexity arises from the influence of rainfall, which only a limited number of studies have addressed so far suggesting short term as well as long-term effects. To understand this, the project RWTurb (https://hmco.enpc.fr/portfolio-archive/rw-turb/; supported by the French National Research Agency, ANR-19-CE05-0022) employs multiple 3D sonic anemometers (manufactured by Thies), mini meteorological stations (manufactured by Thies), and disdrometers (Parsivel², manufactured by OTT) on a meteorological mast in the wind farm of Pays d’Othe (110 km south-east of Paris, France; operated by Boralex). With this simultaneously measured data, it is possible to study wind power and associated atmospheric fields under various rain conditions.

Variations of wind velocity, power available at the wind farm, power produced by wind turbines and air density are examined here during rain and dry conditions using the framework of Universal Multifractals (UM). UM is a widely used, physically based, scale invariant framework for characterizing and simulating geophysical fields over wide range of scales which accounts for the intermittency in the field. Since rated power acts like an upper threshold in statistical analysis of empirical wind power, efforts were made to use the theoretical available power as a proxy to see the difference. From an event based analysis, differences in UM parameters were observed between rain and dry conditions for the fields illustrating the influence of rain. This is further explored using joint multifractal analysis and an increase in correlation exponent was observed between various fields with an increase in rain rate. Here we also examine the possibility of differences in power production according to type of rain (convective or stratiform) as well as various regimes of wind velocity. While examining time steps according to wind velocity, power curves showed different regions of departure from state curve according to the rain rate.