Open AccessPower performance of wind energy converters characterized as stochastic process: applications of the Langevin power curve
Author(s) -
Wächter Matthias,
Milan Patrick,
Mücke Tanja,
Peinke Joachim
Publication year - 2011
Publication title -
wind energy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.743
H-Index - 92
eISSN - 1099-1824
pISSN - 1095-4244
DOI - 10.1002/we.453
Subject(s) - power (physics) , converters , wind power , stochastic process , process (computing) , energy (signal processing) , statistical physics , control theory (sociology) , computer science , physics , engineering , mathematics , electrical engineering , statistics , artificial intelligence , control (management) , quantum mechanics , operating system
The power performance of a wind energy converter (WEC) commonly refers to the relation between the input source and the electrical output, i.e. the input wind speed u and the electrical power output P . The International Electrotechnical Commission defined a so‐called power curve P ( u ) that quantifies this relation. Recently, a novel approach was introduced based on the short‐time dynamical response of the WEC to high‐frequency wind fluctuations. The dynamical behavior of the WEC is quantified by a drift field and the corresponding Langevin power curve (LPC). We present three applications of our method to wind energy based on the LPC. The first application consists of testing the power performance of WECs using LIDAR wind measurements. We then extend this test to the monitoring of the WEC performance over time. Finally, we apply the LPC to a simulation model for a WEC as a tool to characterize its performance. These applications illustrate the flexibility of the LPC as a relevant tool for performance testing and monitoring. Copyright © 2011 John Wiley & Sons, Ltd.