Multiaxial fatigue assessment of jacket-supported offshore wind turbines considering multiple random correlated loads

2021 ◽  
Vol 169 ◽  
pp. 1252-1264
Author(s):  
Chaoshuai Han ◽  
Kun Liu ◽  
Yongliang Ma ◽  
Peijiang Qin ◽  
Tao Zou
Keyword(s):  
Wind Turbines ◽  
Multiaxial Fatigue ◽  
Offshore Wind ◽  
Fatigue Assessment ◽  
Offshore Wind Turbines

Effective virtual sensing scheme for fatigue assessment of monopile offshore wind turbines

2016 ◽  
pp. 305-310 ◽  
Author(s):  
A. Iliopoulos ◽  
D. Van Hemelrijck ◽  
N. Noppe ◽  
W. Weijtjens ◽  
C. Devriendt
Keyword(s):  
Wind Turbines ◽  
Offshore Wind ◽  
Fatigue Assessment ◽  
Offshore Wind Turbines ◽  
Virtual Sensing

Continuous fatigue assessment of offshore wind turbines using a stress prediction technique

2014 ◽  
Author(s):  
Alexandros N. Iliopoulos ◽  
Christof Devriendt ◽  
Sokratis N. Iliopoulos ◽  
Danny Van Hemelrijck
Keyword(s):  
Wind Turbines ◽  
Offshore Wind ◽  
Fatigue Assessment ◽  
Offshore Wind Turbines ◽  
Prediction Technique ◽  
Stress Prediction

Fatigue assessment of offshore wind turbines on monopile foundations using multi-band modal expansion

Wind Energy ◽  
2017 ◽  
Vol 20 (8) ◽  
pp. 1463-1479 ◽  
Author(s):  
Alexandros Iliopoulos ◽  
Wout Weijtjens ◽  
Danny Van Hemelrijck ◽  
Christof Devriendt
Keyword(s):  
Wind Turbines ◽  
Offshore Wind ◽  
Fatigue Assessment ◽  
Modal Expansion ◽  
Offshore Wind Turbines ◽  
Multi Band

scholarly journals Data-driven farm-wide fatigue estimation on jacket foundation OWTs for multiple SHM setups

2021 ◽  
Author(s):  
Francisco d N Santos ◽  
Nymfa Noppe ◽  
Wout Weijtjens ◽  
Christof Devriendt
Keyword(s):  
Feature Selection ◽  
Wind Turbines ◽  
Offshore Wind ◽  
Data Driven ◽  
Strain Gauges ◽  
Accurate Information ◽  
Accelerometer Data ◽  
Fatigue Assessment ◽  
Offshore Wind Turbines ◽  
Thrust Load

Abstract. The sustained development over the past decades of the offshore wind industry has seen older wind farms beginning to reach their design lifetime. This has led to a greater interest in wind turbine fatigue, the remaining useful lifetime and lifetime extensions. In an attempt to quantify the progression of fatigue life for offshore wind turbines, also referred to as a fatigue assessment, structural health monitoring (SHM) appears as a valuable contribution. Accurate information from a SHM system, can enable informed decisions regarding lifetime extensions. Unfortunately direct measurement of fatigue loads typically revolves around the use of strain gauges and the installation of strain gauges on all turbines of a given farm is generally not considered economically feasible. However, when we consider that great amounts of data, such as Supervisory Control And Data Acquisition (SCADA) and accelerometer data (of cheaper installation than strain gauges), is already being captured, this data might be used to circumvent the lack of direct measurements. It is then highly relevant to know what is the minimal sensor instrumentation required for a proper fatigue assessment. In order to determine this minimal instrumentation, a data-driven methodology is developed for real-world jacket-foundation Offshore Wind Turbines (OWT). Firstly, high-frequent 1s SCADA data is used to train an Artificial Neural Network (ANN) that seeks to estimate the quasi-static thrust load, and able to accurately estimate the thrust load with a Mean Absolute Error (MAE) below 2 %. The thrust load is then, along with 1s SCADA and acceleration data, processed into 10-minute metrics and undergoes a comparative analysis of feature selection algorithms with the goal of performing the most efficient dimensionality reduction possible. The features selected by each method are compared and related to the sensors. The variables chosen by the best-performing feature selection algorithm then serve as the input for a second ANN which estimates the tower fore-aft (FA) bending moment Damage Equivalent Loads (DEL), a valuable metric closely related to fatigue. This approach can then be understood as a two-tier model: the first tier concerns itself with engineering and processing 10 minute features, which will serve as an input for the second tier. It is this two-tier methodology that is used to assess the performance of 8 realistic instrumentation setups (ranging from 10 minute SCADA to 1s SCADA, thrust load and dedicated tower SHM accelerometers). Amongst other findings, it was seen that accelerations are essential for the model's generalization. The best performing instrumentation setup is looked in greater depth, with validation results of the tower FA DEL ANN model show an accuracy of around 1 % (MAE) for the training turbine and below 3 % for other turbines, with a slight underprediction of fatigue rates. Finally, the ANN DEL estimation model – based on a intermediate instrumentation setup (1s SCADA, thrust load, low quality accelerations) – is employed in a farm-wide setting, and the probable causes for outlier behaviour investigated.

Pitch Angle Control of Floating Offshore Wind Turbines by H∞ Control

2014 ◽  
Vol 134 (8) ◽  
pp. 1096-1103 ◽  
Author(s):  
Sho Tsujimoto ◽  
Ségolène Dessort ◽  
Naoyuki Hara ◽  
Keiji Konishi
Keyword(s):  
Wind Turbines ◽  
Pitch Angle ◽  
Offshore Wind ◽  
Offshore Wind Turbines ◽  
Floating Offshore Wind Turbines

scholarly journals Maintenance Planning of Offshore Wind Turbine Using Condition Monitoring Information

2009 ◽  
Author(s):  
Jose´ G. Rangel-Rami´rez ◽  
John D. So̸rensen
Keyword(s):  
Condition Monitoring ◽  
Wind Turbines ◽  
Wind Farm ◽  
Offshore Structures ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Maintenance Planning ◽  
Offshore Wind Turbines ◽  
Inspection And Maintenance ◽  
Monitoring Information

Deterioration processes such as fatigue and corrosion are typically affecting offshore structures. To “control” this deterioration, inspection and maintenance activities are developed. Probabilistic methodologies represent an important tool to identify the suitable strategy to inspect and control the deterioration in structures such as offshore wind turbines (OWT). Besides these methods, the integration of condition monitoring information (CMI) can optimize the mitigation activities as an updating tool. In this paper, a framework for risk-based inspection and maintenance planning (RBI) is applied for OWT incorporating CMI, addressing this analysis to fatigue prone details in welded steel joints at jacket or tripod steel support structures for offshore wind turbines. The increase of turbulence in wind farms is taken into account by using a code-based turbulence model. Further, additional modes t integrate CMI in the RBI approach for optimal planning of inspection and maintenance. As part of the results, the life cycle reliabilities and inspection times are calculated, showing that earlier inspections are needed at in-wind farm sites. This is expected due to the wake turbulence increasing the wind load. With the integration of CMI by means Bayesian inference, a slightly change of first inspection times are coming up, influenced by the reduction of the uncertainty and harsher or milder external agents.

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