Langevin power curve analysis for numerical WEC models with new insights on high frequency power performance

TL;DR

This paper applies Langevin equation-based analysis to high-frequency wind turbine data, enabling detailed power curve estimation, failure detection, and insights into conversion dynamics across measured and synthetic data.

Contribution

It extends the Langevin power curve method to numerical models and synthetic data, demonstrating its robustness and potential for condition monitoring and failure detection.

Findings

Reliable power curves can be derived from synthetic data.

The method visualizes multiple fixed points indicating transition states.

It confirms independence of fixed points from site-specific turbulence effects.

Abstract

Based on the Langevin equation it has been proposed to obtain power curves for wind turbines from high frequency data of wind speed measurements u(t) and power output P (t). The two parts of the Langevin approach, power curve and drift field, give a comprehensive description of the conversion dynamic over the whole operating range of the wind turbine. The method deals with high frequent data instead of 10 min means. It is therefore possible to gain a reliable power curve already from a small amount of data per wind speed. Furthermore, the method is able to visualize multiple fixed points, which is e.g. characteristic for the transition from partial to full load or in case the conversion process deviates from the standard procedures. In order to gain a deeper knowledge it is essential that the method works not only for measured data but also for numerical wind turbine models and synthetic wind fields. Here, we characterize the dynamics of a detailed numerical wind turbine model and calculate the Langevin power curve for different data samplings. We show, how to get reliable results from synthetic data and verify the applicability of the method for field measurements with ultra-sonic, cup and Lidar measurements. The independence of the fixed points on site specific turbulence effects is also confirmed with the numerical model. Furthermore, we demonstrate the potential of the Langevin approach to detect failures in the conversion process and thus show the potential of the Langevin approach for a condition monitoring system.