Correlated power time series of individual wind turbines: A data driven model approach

TL;DR

This paper analyzes offshore wind farm power data over a year, characterizes its autocorrelation, and develops a stochastic model capturing the observed persistent and bimodal behaviors.

Contribution

It introduces a data-driven stochastic model that reproduces key empirical features of wind turbine power time series, including autocorrelation and phase transitions.

Findings

Power time series exhibit persistent autocorrelation with cross-over behavior.

The proposed model captures bimodal distribution and autocorrelation structure.

Empirical data shows transitions between two dominant power generation phases.

Abstract

Wind farms can be regarded as complex systems that are, on the one hand, coupled to the nonlinear, stochastic characteristics of weather and, on the other hand, strongly influenced by supervisory control mechanisms. One crucial problem in this context today is the predictability of wind energy as an intermittent renewable resource with additional non-stationary nature. In this context, we analyze the power time series measured in an offshore wind farm for a total period of one year with a time resolution of 10 min. Applying detrended fluctuation analysis, we characterize the autocorrelation of power time series and find a Hurst exponent in the persistent regime with cross-over behavior. To enrich the modeling perspective of complex large wind energy systems, we develop a stochastic reduced-form model ofpower time series. The observed transitions between two dominating power generation phases are reflected by a bistable deterministic component, while correlated stochastic fluctuations account for the identified persistence. The model succeeds to qualitatively reproduce several empirical characteristics such as the autocorrelation function and the bimodal probability density function.