Efficient power monitoring with dynamic power curve

Stochastic methods provide a broad range of analysis for an environment characterized by an incoming turbulence. In collaboration with ForWind at the university of Oldenburg, Fraunhofer IWES promotes these methods, e.g. CTRW wind field model, continuous time random walk model as well as the dynamic power curve for power monitoring. A main method is the analyses of noise profiles. Different sources of noise and deterministic dynamics can be separated in a signal. This process can be applied on all kinds of data, which are influenced by deterministic and random parts.

The dynamic power curve is a quick and cost effective method to monitor the power output of wind turbines and whole wind farms. The software is based on stochastic examination method, which enables the user to determine a turbine´s power curve in only a few days by using hub anemometer and power output data. The generated data provide an overview of the wind turbine functionality for manufacturers, operators, service companies and public utilities. Deviating behavior of same type turbines is reliably detected, so that improvements of turbines and wind farms result in a reduction of yield losses.

The continuous time random walk (CTRW) model

The base of a realistic load calculation for wind turbines is a good model of the incoming wind field. However, wind field generators based on Gaussian distributions have problems to reproduce the correct distribution of changes in wind speed compared to real wind fields. In contradiction to the model, a realistic wind field comprises a large amount of incidences and its peak wind speeds outstrip the calculated Gaussian prediction by far. The “continuous time random walk” (CTRW) model is able to generate correlated wind fields with realistic conditions. In collaboration with ForWind at the university of Oldenburg, Fraunhofer IWES offers a wind field generator based on the CTRW method.



Development and application of a grid generation tool for aerodynamic simulations of wind turbines
Rahimi, H.; Daniele, E.; Stoevesandt, B.; Peinke, J.
(Journal article)

Wind engineering 40 (2016), Nr.2, S.148-172

Link Fraunhofer Publica


Combined structural optimization and aeroelastic analysis of a Vertical Axis Wind Turbine (Conference contribution)
Roscher, B.; Ferreira, C.S.; Bernhammer, L.O.; Madsen, H.A.; Griffith, D.T.; Stoevesandt, B.

American Institute of Aeronautics and Astronautics -AIAA-, Washington/D.C.:
33rd Wind Energy Symposium 2015. Vol.1 : Kissimmee, Florida, USA, 5 - 9 January 2015; held at the AIAA SciTech Forum 2015
Red Hook, NY: Curran, 2015

Link Fraunhofer Publica

The impact of wake models on wind farm layout optimization (Conference contribution, journal article)
Schmidt, Jonas; Stoevesandt, Bernhard

Journal of physics. Conference series 625 (2015), Art.012040, 10 S.

Link Fraunhofer Publica

Numerical investigation on tower effects for downwind turbines (Abstr.)
Stoevesandt, B.; Habib, F.; Mehra, B.; Rahimi, H.; Peinke, J.

UL International GmbH, Wilhelmshaven:
DEWEK 2015. Book of abstracts : 12th German Wind Energy Conference, 19/20 May 2015, Bremen, Germany
Bremen, 2015

Link Fraunhofer Publica

Roof region dependent wind potential assessment with different RANS turbulence models (Journal article)
Toja-Silva, F.; Peralta, C.; Lopez-Garcia, O.; Navarro, J.; Cruz, I.

Journal of wind engineering and industrial aerodynamics 142 (2015), S.258-271

Link Fraunhofer Publica

Wind farm layout optimization in complex terrain with CFD wakes (Conference contribution)
Schmidt, J.; Stoevesandt, B.

Paper presented at EWEA 2015, Europe's Premier Wind Energy Event, 17 - 20 November 2015, Paris, France

Link Fraunhofer Publica

Wind power energy in Southern Brazil: Evaluation using a mesoscale meteorological model (Journal article & KConference contribution)
Krusche, Nisia; Peralta, Carlos; Chang, Chi-Yao; Stoevesandt, Bernhard

Energy Procedia 76 (2015), S.164-168

Link Fraunhofer Publica