Okyay Altay

Vibration Mitigation of Wind Turbine Towers with Tuned Mass Dampers

Because of its minor environmental impact, electricity generation using wind power is getting remarkable. The further growth of the wind industry depends on technological solutions to the challenges in production and construction of the turbines. Wind turbine tower vibrations, which limit power generation efficiency and cause fatigue problems with high maintenance costs, count as one of the main structural difficulties in the wind energy sector. To mitigate tower vibrations auxiliary measures are necessary. The effectiveness of tuned mass damper is verified by means of a numeric study on a 5 MW onshore reference wind turbine. Hereby, also seismic-induced vibrations and soil–structure interaction are considered. Acquired results show that tuned mass damper can effectively reduce resonant tower vibrations and improve the fatigue life of wind turbines. This chapter is also concerned with tuned liquid column damper and a semiactive application of it. Due to its geometric versatility and low prime costs, tuned liquid column dampers are a good alternative to other damping measures, in particular for slender structures like wind turbines.

Experimental identification of an elasto-mechanical multi-degree-of-freedom-system using stochastic signals

The determination of dynamic parameters are the central points of the system identification of civil engineering structures under dynamic loading. This paper first gives a brief summary of the recent developments of the system identification methods in civil engineering and describes mathematical models, which enable the identification of the necessary parameters using only stochastic input signals. Relevant methods for this identification use Frequency Domain Decomposition (FDD), Autoregressive Moving Average Models (ARMA) and the Autoregressive Models with eXogenous input (ARX). In a first step an elasto-mechanical mdof-system is numerically modeled using FEM and afterwards tested numerically by above mentioned identification methods using stochastic signals. During the second campaign, dynamic measurements are conducted experimentally on a real 7-story RC-building with ambient signal input using sensors. The results are successfully for the relevant system identification methods.

Experimental Incremental System Identification Method Using Separate Time Windows on Basis of Ambient Signals

Experimental system identification methods, such as Frequency Domain Decomposition (FDD), Autoregressive Moving Average Model (ARMA), Autoregressive Models with eXogenous input (ARX), Kalman Filter and Stochastic Subspace Identification (SSI), are commonly used in civil engineering to determine dynamic parameters of existing structures. Basis for these methods are in-situ measurements, which can be very time-consuming and cost-intensive depending on the complexity of the structure. This paper investigates the possibility to reduce the in-situ measurement effort by introducing a new method, which bases on incremental measurements by using only a single sensor in separate time windows. The proposed incremental identification method (IIM) requires stationary ergodic response signals of the structure induced by ambient vibrations with white noise density. Therefore, after each incremental measurement a quality-check of the response signal should be conducted to verify the applicability of the theory. This approach ensures the comparability of the input signals with each other and thus the reproducibility of the identified dynamic behavior. For this purpose, a signal evaluation criterion is defined. For low-quality data, which cannot satisfy this criterion, special signal processing methods have to be applied. With the signals, which already accomplish the evaluation criterion, the identification of the system parameter can then be carried out by using one of the above mentioned system identification methods, such as FDD. The IIM is applied so far both on numerical and experimental examples. In this paper the validation of the IIM is reached by identifying the parameters of the IASC-ASCE Structural Health Monitoring Benchmark Problem for different ambient simulated input signals.