Modal Analysis of CX-100 Rotor Blade and Micon 65/13 Wind Turbine

White, Jonathan; Adams, Douglas E.; Rumsey, Mark A

doi:10.1007/978-1-4419-9716-6_2

Cited by 35 publications

(22 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…General reviews can be found in [9] and [10]. Several researchers have tried in recent years to apply damage detection technologies, and these studies were mainly in a laboratory environment [11]- [18]. Briefly, both passive and active sensing technologies have been applied in the context of WTs [13].…”

Section: Introductionmentioning

confidence: 99%

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

Papatheou

Dervilis

Maguire

et al. 2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

The use of offshore wind farms has been growing in recent years. Europe is presenting a geometrically growing interest in exploring and investing in such offshore power plants as the continent's water sites offer impressive wind conditions. Moreover, as human activities tend to complicate the construction of land wind farms, offshore locations, which can be found more easily near densely populated areas, can be seen as an attractive choice. However, the cost of an offshore wind farm is relatively high, and therefore, their reliability is crucial if they ever need to be fully integrated into the energy arena. This paper presents an analysis of supervisory control and data acquisition (SCADA) extracts from the Lillgrund offshore wind farm for the purposes of monitoring. An advanced and robust machine-learning approach is applied, in order to produce individual and population-based power curves and then predict measurements of the power produced from each wind turbine (WT) from the measurements of the other WTs in the farm. Control charts with robust thresholds calculated from extreme value statistics are successfully applied for the monitoring of the turbines.Index Terms-Machine learning, offshore wind farm, pattern recognition, supervisory control and data acquisition (SCADA), wind turbine (WT) monitoring.This work is licensed under a Creative Commons Attribution 3.0 License. For more information, see http://creativecommons.org/licenses/by/3.0/

show abstract

Section: Introductionmentioning

confidence: 99%

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

Papatheou

Dervilis

Maguire

et al. 2015

IEEE Trans. Ind. Electron.

View full text Add to dashboard Cite

show abstract

“…The first two scenarios were chosen to verify that the issues that arose from a previous code verification study [13] were resolved with the new ANSYS Shell281 elements. The verification of the modal analysis was also demonstrated because a modal analysis is one of the most typical dynamic characterizations ( [18], [19], and [20]). …”

Section: Code Verification With Ansysmentioning

confidence: 99%

Model Form Error of Alternate Modeling Strategies: Shell Type Wind Turbine Blades

Buren

Atamturktur

2011

Rotating Machinery, Structural Health Monitoring, Shock and Vibration, Volume 5

View full text Add to dashboard Cite

In the U.S. there is a need for the Wind Energy sector to reduce maintenance costs and increase the continuous availability of wind turbines to compete with other forms of energy. In this respect, reliable modeling and simulation techniques play an indispensible role in allowing a better understanding of wind turbine blade vibrations. The purpose of this paper is to quantify the degrading effects of simplifications to the material cross section of a finite element (FE) model of a Suzlon S88-2.1 MW wind turbine blade. First, a code verification study with ANSYS Shell281 elements is performed to verify the element type selection. Next, a three-dimensional model of the blade is developed using NuMAD, pre-processing software and analyzed in ANSYS, FE modeling software. A solution verification study is performed to identify the mesh size that will yield converged solutions with manageable demands on computational resources. Five constitutive models are developed by incrementally simplifying the cross section of the blade. The most sophisticated model is idealized as the baseline to create synthetic experimental data, utilizing orthotropic materials and a composite cross section, in comparison to the least involved model which has an isotropic, smeared cross section. This manuscript explores the extent to which errors are increased as the model is simplified in order to reduce the number of input calibration parameters and computation time. Considering the baseline, the model form errors associated with the four models of lesser sophistication are estimated. The estimates of model form error conveniently quantify the effects of simplifications to the entire domain of a FE model wind turbine blade. Model form error will help to identify the tradeoffs between varying levels of sophistication in the physics, and will thus aid in future attempts to develop dependable FE models of shell type wind turbine blades.

show abstract

“…This particular blade has been widely studied [35,36,37]. A simplified finite element model was generated in ANSYS using shell elements.…”

Section: Wind Turbine Blade Modelmentioning

confidence: 99%

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

Venkatesh

Cao

Vaidya

et al. 2016

Meas. Sci. Technol.

View full text Add to dashboard Cite

Abstract. In this work, we develop a data-driven method for the diagnosis of damage in mesoscale mechanical structures using an array of distributed sensor networks. The proposed approach relies on comparing intrinsic geometries of data sets corresponding to the undamage and damage states of the system. We use spectral diffusion map approach for identifying the intrinsic geometry of the data set. In particular, time series data from distributed sensors is used for the construction of diffusion maps. The low dimensional embedding of the data set corresponding to different damage levels is obtained using singular value decomposition of the diffusion map. We construct appropriate metrics in the diffusion space to compare the different data sets corresponding to different damage cases. The developed algorithm is applied for damage diagnosis of wind turbine blades. Towards this goal, we developed a detailed finite element-based model of CX-100 blade in ANSYS using shell elements. Typical damage, such as crack or delamination, will lead to a loss of stiffness, is modeled by altering the stiffness of the laminate layer. One of the main challenges in the development of health monitoring algorithms is the ability to use sensor data with relatively small signal-to-noise ratio. Our developed diffusion map-based algorithm is shown to be robust to the presence of sensor noise. The proposed diffusion map-based algorithm is advantageous by enabling the comparison of data from numerous sensors of similar or different types of data through data fusion, hereby making it attractive to exploit the distributed nature of sensor arrays. This distributed nature is further exploited for the purpose of damage localization. We perform extensive numerical simulations to demonstrate that the proposed method can successfully determine the extent of damage on the wind turbine blade and also localize the damage. We also present preliminary results for the application of the developed algorithm on the experimental data. These preliminary results obtained using experimental data are promising and is a topic of our ongoing investigation.

show abstract

Modal Analysis of CX-100 Rotor Blade and Micon 65/13 Wind Turbine

Cited by 35 publications

References 6 publications

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

A Performance Monitoring Approach for the Novel Lillgrund Offshore Wind Farm

Model Form Error of Alternate Modeling Strategies: Shell Type Wind Turbine Blades

Damage detection on mesosurfaces using distributed sensor network and spectral diffusion maps

Contact Info

Product

Resources

About