Damage Identification in a Laboratory Offshore Wind Turbine Demonstrator

2013 ◽  
Vol 569-570 ◽  
pp. 555-562
Author(s):  
Ana Gómez González ◽  
Ekhi Zugasti ◽  
Javier Anduaga
Keyword(s):  
Damage Detection ◽  
Wind Turbine ◽  
Damage Identification ◽  
Detection Method ◽  
Lattice Structure ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Detection Algorithms ◽  
Single Piece ◽  
Different Parts

This paper presents a method to detect and identify damage in a laboratory offshore wind turbine support structure. The structure consists of three different parts: the jacket, the tower and the nacelle. The jacket is a lattice structure joined with several bolts. The tower consists of three different sections joined by bolts. The nacelle is composed of a single piece. The different parts are also joined with bolts. The damage in the structure is simulated by loosening some of the bolts in the jacket. Two damage detection algorithms, namely AutoRegressive methods and NullSpace methods, have been tested in a primitive variation of this structure without the jacket, obtaining good results. In this paper we present the application of the last damage detection method to the new structure with the jacket and an extension to identification of the damage.

scholarly journals Feature Selection - Extraction Methods Based on PCA and Mutual Information to Improve Damage Detection Problem in Offshore Wind Turbines

2013 ◽  
Vol 569-570 ◽  
pp. 620-627 ◽  
Author(s):  
Ekhi Zugasti ◽  
Luis Eduardo Mujica ◽  
Javier Anduaga ◽  
Fernando Martínez
Keyword(s):  
Feature Selection ◽  
Damage Detection ◽  
Input Data ◽  
Detection Method ◽  
Extraction Methods ◽  
Offshore Wind ◽  
Detection Problem ◽  
Offshore Wind Turbines ◽  
Detection Algorithms ◽  
Tower Model

Damage Detection problem in Structural Health Monitoring (SHM) is widely studied by many researchers, therefore lots of damage detection algorithms can be found in the literature. Feature Selection / Extraction methods are essential in the accuracy of these algorithms, they provide the suitable data to be used. The main goal of this work is to improve the input data to be the most representative for the damage detection problem. This is done using different Feature Selection / Extraction methods (PCA, UmRMR, and a combination of both). After taking the representative features, the results are tested using a damage detection method; the NullSpace in this case. The data has been collected from a Laboratory Offshore tower model. The different results are compared (different preprocessing vs Raw data) and these show how the correct preselection of the data can improve damage detection.

Data fusion-based damage identification for a monopile offshore wind turbine structure using wireless smart sensors

2020 ◽  
Vol 195 ◽  
pp. 106728 ◽  
Author(s):  
Seunghoo Jeong ◽  
Eun-Jin Kim ◽  
Do Hyoung Shin ◽  
Jong-Woong Park ◽  
Sung-Han Sim
Keyword(s):  
Data Fusion ◽  
Wind Turbine ◽  
Damage Identification ◽  
Offshore Wind ◽  
Smart Sensors ◽  
Offshore Wind Turbine ◽  
Wireless Smart Sensors

Damage detection in grouted connections using electromechanical impedance spectroscopy

2018 ◽  
Vol 233 (3) ◽  
pp. 947-950 ◽  
Author(s):  
Jochen Moll
Keyword(s):  
Impedance Spectroscopy ◽  
Damage Detection ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Oil And Gas ◽  
Offshore Wind ◽  
Wind Loading ◽  
Offshore Wind Turbine ◽  
Offshore Wind Turbines ◽  
Electromechanical Impedance

Grouted connections are structural joints formed by a cementitious grout cast between two concentric circular tubes. They are widely used in the offshore construction of oil and gas platforms, and for offshore wind turbines (monopiles and jackets). However, their application in offshore wind turbine installations can be critical due to the high bending moments coming from wind loading. Recently, it was found that grouted connections show limited performance in offshore wind turbine installations leading to settlements between the steel tubes and steel/grout debonding. Hence, structural health monitoring techniques for grouted connections are needed that ensure a safe and reliable operation of offshore wind turbines. This short communication describes the successful application of electromechanical impedance spectroscopy for damage detection in grouted connections.

Experimental Study for SPAR Type Floating Offshore Wind Turbine With Blade-Pitch Control

2013 ◽  
Author(s):  
Toshiki Chujo ◽  
Yoshimasa Minami ◽  
Tadashi Nimura ◽  
Shigesuke Ishida
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Offshore Wind ◽  
Scale Model ◽  
Offshore Wind Turbine ◽  
Experimental Proof ◽  
Pitch Control ◽  
Model Experiments ◽  
North Eastern ◽  
North Eastern Part

The experimental proof of the floating wind turbine has been started off Goto Islands in Japan. Furthermore, the project of floating wind farm is afoot off Fukushima Prof. in north eastern part of Japan. It is essential for realization of the floating wind farm to comprehend its safety, electric generating property and motion in waves and wind. The scale model experiments are effective to catch the characteristic of floating wind turbines. Authors have mainly carried out scale model experiments with wind turbine models on SPAR buoy type floaters. The wind turbine models have blade-pitch control mechanism and authors focused attention on the effect of blade-pitch control on both the motion of floater and fluctuation of rotor speed. In this paper, the results of scale model experiments are discussed from the aspect of motion of floater and the effect of blade-pitch control.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

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