Browsing by Author "Cosack, Nicolai"

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Open Access
Detection of wind evolution and lidar trajectory optimization for lidar-assisted wind turbine control
(2015) Schlipf, David; Haizmann, Florian; Cosack, Nicolai; Siebers, Tom; Cheng, Po Wen
In this work a collective pitch feedforward controller for floating wind turbines is presented. The feedforward controller provides a pitch rate update to a conventional feedback controller based on a wind speed preview. The controller is designed similar to the one for onshore turbines, which has proven its capability to improve wind turbine control performance in field tests. In a first design step, perfect wind preview and a calm sea is assumed. Under these assumptions the feedforward controller is able to compensate almost perfectly the effect of changing wind speed to the rotor speed of a full nonlinear model over the entire full load region. In a second step, a nacelle-based lidar is simulated scanning the same wind field which is used also for the aero-hydro-servo-elastic simulation. With model-based wind field reconstruction methods, the rotor effective wind speed is estimated from the raw lidar data and is used in the feedforward controller after filtering out the uncorrelated frequencies. Simulation results show that even with a more realistic wind preview, the feedforward controller is able to significantly reduce rotor speed and power variations. Furthermore, structural loads on the tower, rotor shaft, and blades are decreased. A comparison to a theoretical investigation shows that the reduction in rotor speed regulation is close to the optimum.
Open Access
Fatigue load monitoring with standard wind turbine signals
(2010) Cosack, Nicolai; Kühn, Martin (Prof. Dr. Dipl.-Ing.)
The loading of wind turbines is in general not monitored and measurements are only performed for the validation of prototypes. A shift towards increased use of load monitoring devices for the forthcoming generation of large multi-megawatt turbines is obvious, as relative costs for measurement devices go down and the benefit from feeding load signals into the turbine control increases. However, it is hard to imagine that this will be the case for turbines with rated power in the range of 1 to 3 MW which are installed in large numbers today. As a consequence, very little or, in most cases, nothing is known about the load history for more than a few of the many existing turbines. Besides information about energy yield, operating hours and maintenance or repair activities, a turbine's performance and especially its dynamic behaviour is only known from prototype testing. This does not necessarily reflect the real conditions in the series at a particular site properly. Nevertheless, some standard signals (like rotor speed, electrical power or pitch angle) which are commonly only used for turbine control, are available for these turbines. The objective of this work is to systematically investigate, whether the estimation of wind turbine fatigue loads based on these signals is feasible. The research comprises the identification of the main drivers for wind turbine fatigue loads and tackles the question, whether these conditions are also reflected in standard wind turbine signals. Furthermore, possible techniques for the set-up of transfer functions to link standard signals and loads are investigated. Because of the many conditions which can potentially influence fatigue loads and the non-linear characteristics of wind turbines, a neural network based approach seems to be the most promising option, at least for loads encountered during normal power production. Besides discussing the general outline of a load estimation system, different prediction schemes are developed from simulations of a typical 1.5 MW wind turbine. Loads and standard signals are generated using the industrial design tool Flex5. This allows to perform first test of methods for the prediction of equivalent load ranges and equivalent magnitudes from statistical parameters of standard signals. In addition, it is investigated if frequency domain methods can serve as the basis for the estimation of load cycle distributions. Because of the non-linear characteristics of wind turbines and the non-stationary operational conditions it turns out that these methods can not be applied easily. Therefore, an empirical method which employs fitting of combined probability density distributions to measured loads is developed. Test and refinement of the methods is performed with measured data. In a first step the developed schemes are validated on the basis of data recorded from measurements at the state-of-the-art 5 MW wind turbine Multibrid M5000. The transferability of the approach to another turbine type and the feasibility to derive accurate predictions from a reduced number of input data is emphasised by additional tests at one 2.5 MW Nordex N80 turbine. First results indicating the applicability of already established transfer functions for load predictions at turbines in a series are derived by utilising measurements from a second N80 turbine. Finally, recommendations for further research activities towards the employment of a load estimation system on an industrial scale are given.