A Wake Detector for Wind Farm Control

The paper describes an observer capable of detecting the impingement on a wind turbine rotor of the wake of an upstream machine. The observer estimates the local wind speed and turbulence intensity on the left and right parts of the rotor disk. The estimation is performed based on blade loads measured by strain gages or optical fibers, sensors which are becoming standard equipment on many modern machines. A lower wind speed and higher turbulence intensity on one part of the rotor, possibly in conjunction with other information, can then be used to infer the presence of a wake impinging on the disk. The wake state information is useful for wind plant control strategies, as for example wake deflection by active yawing. In addition, the local wind speed estimates may be used for a rough evaluation of the vertical wind shear

A MIMO periodic ARX identification algorithm for the Floquet stability analysis of wind turbines

The paper presents a new stability analysis approach applicable to wind turbines. At first, a reduced order periodic model is identified from response time histories, and then stability is assessed using Floquet theory. The innovation of the proposed approach is in the ability of the algorithm to simultaneously consider multiple response time histories, for example in the form of measurements recorded both on the rotor and in the stand still system. As each different measurement carries a different informational content on the system, the simultaneous use of all available signals improves the quality and robustness of the analysis

High-resolution periodic mode shapes identification for wind turbines

The stability analysis of in-operation wind turbines is a very important topic, that has received considerable attention in the last years. Many identification algorithms have been developed to estimate frequencies and damping ratios, but very few papers have been dedicated to the mode shapes. The knowledge of high-resolution mode shapes could be exploited for several applications including model validation, accurate description of the vibratory content of a machine and spatially-accurate damage detection. In this work, we will present a procedure to compute the high-resolution periodic mode shapes of a wind turbine, and apply it to a high-fidelity wind turbine model. The results show that this methodology is able to identify the first low-damped modes of the system with good accuracy

Simultaneous observation of wind shears and misalignments from rotor loads

A wind turbine is used in this paper as a sensor to measure the wind conditions at the rotor disk. In fact, as any anisotropy in the wind will lead to a specific signature in the machine response, by inverting a response model one may infer its generating cause, i.e. the wind. Control laws that exploit this knowledge can be used to enhance the performance of a wind turbine or a wind power plant. This idea is used in the present paper to formulate a linear implicit model that relates wind states and rotor loads. Simulations are run in both uniform and turbulent winds, using a high-fidelity aeroservoleastic wind turbine model. Results demonstrate the ability of the proposed observer in detecting the horizontal and vertical wind misalignments, as well as the vertical and horizontal shears

Wake center position tracking using downstream wind turbine hub loads

Having an improved awareness of the flow within a wind farm is useful for power harvesting maximization, load minimization and design of wind farm layout. Local flow information at each wind turbine location can be obtained by using the response of the wind turbines, which are consequently used as distributed sensors. This paper proposes the use of hub loads to track the position of wakes within a wind farm. Simulation experiments conducted within a high-fidelity aeroservoelastic environment demonstrate the performance of the new method