Uncovering wind turbine properties through two-dimensional stochastic modeling of wind dynamics

TL;DR

This paper applies stochastic data analysis from statistical physics to synthetic wind data, successfully extracting turbine performance curves and key features, offering a new approach to understanding wind turbine dynamics.

Contribution

It introduces a stochastic modeling method to analyze wind dynamics, enabling extraction of performance curves and physical relationships from wind data.

Findings

Successfully retrieves the power performance curve from synthetic data.

Links turbine features like rated speed to stochastic model equations.

Demonstrates potential for extracting unknown physical relationships.

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.