Abstract
The Taylor power law (or temporal fluctuation scaling), is a scaling relationship of the form σ ~ (P)λ where !! is the standard deviation and hPi the mean value of a sample of a time series has been observed for power output data sampled at 5 min and 1 s and from five wind farms and a single wind turbine, located at different places. Furthermore, an analogy with the turbulence field is performed, consequently allowing the establishment of a scaling relationship between the turbulent production IP and the mean value (P).
Highlights
Wind energy is a complex process in constant growing
Its complexity results from interactions between weather dynamics, atmospheric turbulence and wind turbines located at different positions in wind farms
To verify the existence of Taylor power law for our datasets, the mean value hP iτ and the standard deviation στ are computed for time scales τ ranging between approximately 4 h and days for data sampled at 5 min, and τ ranging between approximately 16 min and 1 day for data sampled at 1 s
Summary
Its complexity results from interactions between weather dynamics, atmospheric turbulence and wind turbines located at different positions in wind farms. This energy resource exhibits high fluctuations at all temporal and spatial scales. Such complex processes are ubiquitous in many research fields. The exponent value λ can fluctuate between two universal classes λ = 1/2 and λ = 1 [6,12] This scaling relationship has been highlighted for the power output delivered by a wind farm [20]. As an extension of this early study, fluctuation scaling is investigated for the power output delivered by five wind farms and a single turbine
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