Uncovering wind turbine properties through two-dimensional stochastic modeling of wind dynamics
arXiv:1210.7161 · doi:10.1103/PhysRevE.88.042146
Abstract
Using a method for stochastic data analysis, borrowed from statistical physics, we analyze synthetic data from a Markov chain model that reproduces measurements of wind speed and power production in a wind park in Portugal. We first show that our analysis retrieves indeed the power performance curve, which yields the relationship between wind speed and power production and we discuss how this procedure can be extended for extracting unknown functional relationships between pairs of physical variables in general. Second, we show how specific features, such as the rated speed of the wind turbine or the descriptive wind speed statistics, can be related with the equations describing the evolution of power production and wind speed at single wind turbines.
arXiv comment8 figures
References in corpus (7)
- Comment on "Indispensable Finite Time Correlations for Fokker-Planck Equations from Time Series Data"
- Wind Energy and the Turbulent Nature of the Atmospheric Boundary Layer
- Estimation of drift and diffusion functions from time series data: A maximum likelihood framework
- Extracting strong measurement noise from stochastic series: applications to empirical data
- Principal axes for stochastic dynamics
- Geometric and projection effects in Kramers-Moyal analysis
- Searching for optimal variables in real multivariate stochastic data
Cited by in corpus (5)
- Air quality prediction using optimal neural networks with stochastic variables
- Principal wind turbines for a conditional portfolio approach to wind farms
- Reconstructing the intermittent dynamics of the torque in wind turbines
- Fatigue Loads Estimation Through a Simple Stochastic Model
- A cyclic time-dependent Markov process to model daily patterns in wind turbine power production