Field monitoring of the ground vibrations adjacent to an onshore wind turbine foundation

2020 ◽  
Author(s):  
Pengpeng He ◽  
Jesús González-Hurtado ◽  
Tim Newson ◽  
Hanping Hong ◽  
Melanie Postman ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Near Field ◽  
Wind Farms ◽  
Field Monitoring ◽  
Vibration Monitoring ◽  
Onshore Wind ◽  
Wind Structure ◽  
Soil Foundation ◽  
Non Gaussian ◽  
Fluctuating Wind

Investigations of the soil-foundation interaction behavior of wind turbine foundations and transfer of energy from the wind to the ground have not been reported in Canada. Indeed, very few vibration monitoring studies have been conducted globally around wind farms. It has been found that turbines predominantly produce vibrations related to structural resonances and blade passing frequencies. Energy is found to be modified with distance and is dominated by surface waves. This paper describes a study of the effect of the wind-structure interaction on the behaviour of a turbine foundation and the generation of ground-based vibrations around a working commercial wind turbine in Ontario. The field monitoring system and meteorological instrumentation are described in this paper and the responses of the structure and the surrounding ground due to the fluctuating wind-field are discussed. The spectral analysis shows that the higher frequency vibrations attenuate more rapidly than the lower frequency vibrations. The tilted elliptical particle motions are found to be non-Gaussian because of the non-Gaussian wind conditions. The response attenuation with distance indicates that both geometric and material attenuation may dominate the vibration attenuation in the near field and only geometric attenuation occurs in the far field.

Overview of Wind Turbine Field Failure Databases: A Discussion of the Requirements for an Analysis

2018 ◽  
Author(s):  
Roozbeh Bakhshi ◽  
Peter Sandborn
Keyword(s):  
Statistical Analysis ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farms ◽  
Onshore Wind ◽  
Failure Times ◽  
Reliability Parameters ◽  
Failure Data ◽  
Offshore Installations ◽  
Field Failure

With renewable energy and wind energy in particular becoming mainstream means of energy production, the reliability aspect of wind turbines and their sub-assemblies has become a topic of interest for owners and manufacturers of wind turbines. Operation and Maintenance (O&M) costs account for more than 25% of total costs of onshore wind projects and these costs are even higher for offshore installations. Effective management of O&M costs depends on accurate failure prediction for turbine sub-assemblies. There are numerous models that predict failure times and O&M costs of wind farms. All these models have inputs in the form of reliability parameters. These parameters are usually generated by researchers using field failure data. There are several databases that report the failure data of operating wind turbines and researches use these failure data to generate the reliability parameters through various methods of statistical analysis. However, in order to perform the statistical analysis or use the results of the analysis, one must understand the underlying assumptions of the database along with information about the wind turbine population in the database such as their power rating, age, etc. In this work, we analyze the relevant assumptions and discuss what information is required from a database in order to improve the statistical analysis on wind turbines’ failure data.

Analysis of vibration monitoring data of an onshore wind turbine under different operational conditions

2020 ◽  
Vol 205 ◽  
pp. 110071 ◽  
Author(s):  
Yan Zhao ◽  
Jianing Pan ◽  
Zhuye Huang ◽  
Yachao Miao ◽  
Jianqun Jiang ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Vibration Monitoring ◽  
Monitoring Data ◽  
Onshore Wind ◽  
Operational Conditions

Neural Network-Based Method for Structural Damage and Scour Estimation Using Modal Parameters and Dynamic Responses

2020 ◽  
Author(s):  
Shuqing Wang ◽  
Yufeng Jiang
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Structural Damage ◽  
Wind Farms ◽  
Dynamic Responses ◽  
Beam Model ◽  
Noisy Environment ◽  
Modal Parameter ◽  
Onshore Wind ◽  
Acceleration Signals

Abstract Wind energy is the most promising clean, renewable energies to the power industry in the world. More and more wind turbine structures equipped with the larger capacity, taller towers, and longer blades were installed at the offshore/onshore wind farms. But these structures face many harsh environmental conditions, and structural damage and foundation scour are continuously accumulated. It could alter the modal parameter and dynamic response and further reduce the safety of structures. It is a significant challenge on how to accurately estimate the structural states if there is structural damage or foundation scour. For addressing these limitations, a One Dimensional Convolutional Neural Network (1D CNN) method is developed to estimate the structural state. After the Fast Fourier Transform of the acceleration signals, these frequency responses are used as the input to train the 1D CNN, while these states are estimated as the output. A simplified spring-beam model is introduced to simulate the pile-soil interaction, and the effects of the damage and scour on natural frequencies are investigated and compared. The effectiveness and robustness of the proposed 1D CNN method have been numerically investigated by several scenarios associated with the wind turbine structure. Results demonstrate that the 1D CNN method can accurately estimate the structural states, even under a noisy environment. Further, the 1D CNN method can identify the location of damage and scour depth with very high accuracy. This approach may be useful in the on-site structural health monitoring in the wind turbine structure.

Flow Simulation Around a Rim-Driven Wind Turbine and in Its Wake

2013 ◽  
Author(s):  
Bryan E. Kaiser ◽  
Svetlana V. Poroseva ◽  
Michael A. Snider ◽  
Rob O. Hovsapian ◽  
Erick Johnson
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Wind Farm ◽  
Flow Simulation ◽  
Wind Farms ◽  
Sensitivity Study ◽  
Onshore Wind ◽  
Flow Simulations ◽  
Turbine Design ◽  
The Impact

A relatively high free stream wind velocity is required for conventional horizontal axis wind turbines (HAWTs) to generate power. This requirement significantly limits the area of land for viable onshore wind farm locations. To expand a potential for wind power generation and an area suitable for onshore wind farms, new wind turbine designs capable of wind energy harvesting at low wind speeds are currently in development. The aerodynamic characteristics of such wind turbines are notably different from industrial standards. The optimal wind farm layout for such turbines is also unknown. Accurate and reliable simulations of a flow around and behind a new wind turbine design should be conducted prior constructing a wind farm to determine optimal spacing of turbines on the farm. However, computations are expensive even for a flow around a single turbine. The goal of the current study is to determine a set of simulation parameters that allows one to conduct accurate and reliable simulations at a reasonable cost of computations. For this purpose, a sensitivity study on how the parameters variation influences the results of simulations is conducted. Specifically, the impact of a grid refinement, grid stretching, grid cell shape, and a choice of a turbulent model on the results of simulation of a flow around a mid-sized Rim Driven Wind Turbine (U.S. Patent 7399162) and in its near wake is analyzed. This wind turbine design was developed by Keuka Energy LLC. Since industry relies on commercial software for conducting flow simulations, STAR-CCM+ software [1] was used in our study. A choice of a turbulence model was made based on the results from our previous sensitivity study of flow simulations over a rotating disk [2].

scholarly journals Optimized Placement of Onshore Wind Farms Considering Topography

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2944 ◽  
Author(s):  
Xiawei Wu ◽  
Weihao Hu ◽  
Qi Huang ◽  
Cong Chen ◽  
Zhe Chen ◽  
...  
Keyword(s):  
Wind Turbine ◽  
Wind Turbines ◽  
Production Cost ◽  
Wind Farms ◽  
Power Production ◽  
Optimization Method ◽  
Wake Effect ◽  
Onshore Wind ◽  
Wake Interaction ◽  
Height Change

As the scale of onshore wind farms are increasing, the influence of wake behavior on power production becomes increasingly significant. Wind turbines sittings in onshore wind farms should take terrain into consideration including height change and slope curvature. However, optimized wind turbine (WT) placement for onshore wind farms considering both topographic amplitude and wake interaction is realistic. In this paper, an approach for optimized placement of onshore wind farms considering the topography as well as the wake effect is proposed. Based on minimizing the levelized production cost (LPC), the placement of WTs was optimized considering topography and the effect of this on WTs interactions. The results indicated that the proposed method was effective for finding the optimized layout for uneven onshore wind farms. The optimization method is applicable for optimized placement of onshore wind farms and can be extended to different topographic conditions.

scholarly journals Local turbulence parameterization improves the Jensen wake model and its implementation for power optimization of an operating wind farm

2018 ◽  
Author(s):  
Thomas Duc ◽  
Olivier Coupiac ◽  
Nicolas Girard ◽  
Gregor Giebel ◽  
Tuhfe Göçmen
Keyword(s):  
Wind Turbine ◽  
Wind Farm ◽  
Decay Constant ◽  
Wind Farms ◽  
Power Production ◽  
Original Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Onshore Wind ◽  
Wake Model

Abstract. In this paper, a new calculation procedure to improve the accuracy of the Jensen wake model for operating wind farms is proposed. In this procedure the wake decay constant is updated locally at each wind turbine based on the turbulence intensity measurement provided by the nacelle anemometer. This procedure was tested against experimental data at onshore wind farm La Sole du Moulin Vieux (SMV) in France and the offshore wind farm Horns Rev-I in Denmark. Results indicate that the wake deficit at each wind turbine is described more accurately than when using the original model, reducing the error from 15–20 % to approximately 5 %. Furthermore, this new model properly calibrated for the SMV wind farm is then used for coordinated control purposes. Assuming an axial induction control strategy, and following a model predictive approach, new power settings leading to an increased overall power production of the farm are derived. Power gains found are in the order of 2.5 % for a two wind turbine case with close spacing and 1 to 1.5 % for a row of five wind turbines with a larger spacing. Finally, the uncertainty of the updated Jensen model is quantified considering the model inputs. When checked against the predicted power gain, the uncertainty of the model estimations is seen to be excessive, reaching approximately 4 %, which indicates the difficulty of field observations for such a gain. Nevertheless, the optimized settings are to be implemented during a field test campaign at SMV wind farm in scope of the national project SMARTEOLE.

scholarly journals Wind turbine load dynamics in the context of turbulence intermittency

2019 ◽  
Vol 4 (4) ◽  
pp. 581-594 ◽  
Author(s):  
Carl Michael Schwarz ◽  
Sebastian Ehrich ◽  
Joachim Peinke
Keyword(s):  
Wind Turbine ◽  
Design Guidelines ◽  
Elastic Model ◽  
Spatial Correlations ◽  
Wind Fields ◽  
Wind Structure ◽  
Turbulence Intermittency ◽  
Velocity Increments ◽  
Fatigue Loads ◽  
Non Gaussian

Abstract. The importance of a high-order statistical feature of wind, which is neglected in common wind models, is investigated: non-Gaussian distributed wind velocity increments related to the intermittency of turbulence and their impact on wind turbine dynamics and fatigue loads are the focus. Gaussian and non-Gaussian synthetic wind fields obtained from a continuous-time random walk model are compared and fed to a common aero-servo-elastic model of a wind turbine employing blade element momentum (BEM) aerodynamics. It is discussed why and how the effect of the non-Gaussian increment statistics has to be isolated. This is achieved by assuring that both types feature equivalent probability density functions, spectral properties and coherence, which makes them indistinguishable based on wind characterizations of common design guidelines. Due to limitations in the wind field genesis, idealized spatial correlations are considered. Three examples with idealized; differently sized wind structures are presented. A comparison between the resulting wind turbine loads is made. For the largest wind structure sizes, differences in the fatigue loads between intermittent and Gaussian are observed. These are potentially relevant in a wind turbine certification context. Subsequently, the dependency of this intermittency effect on the field's spatial variation is discussed. Towards very small structured fields, the effect vanishes.

Monitor Method of Offshore Wind Turbine Based on Memory-Like and Neural Network Expert System

2014 ◽  
Vol 635-637 ◽  
pp. 687-693
Author(s):  
Ling Xia Su ◽  
Xia Xia Ma
Keyword(s):  
Fault Diagnosis ◽  
Wind Turbine ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Offshore Wind Farms ◽  
Onshore Wind ◽  
Diagnosis Method ◽  
Simulation Results ◽  
Improve Reliability

The number of offshore wind farms increases gradually because of the high capability of power generation. However, the costs of manufacturing, logistics, installation and maintenance of offshore wind turbine are higher than those of onshore wind turbine. Thus the introduction of fault diagnosis is considered as a suitable way to improve reliability of wind turbine and reduce costs of repairs and casualties. In this paper, 3 major failures of direct-driven wind turbine according to urgency and system responses are discussed. A "memory-like" model pretreatment method and a fault diagnosis method for the failures are investigated. The simulation results show that total amount of fault data to be processed and stored is reduced, and difficulties of knowledge gaining and fault reasoning are also decreased.

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