7,495 research outputs found
Model-based Aeroservoelastic Design and Load Alleviation of Large Wind Turbine Blades
This paper presents an aeroservoelastic modeling approach for dynamic load alleviation
in large wind turbines with trailing-edge aerodynamic surfaces. The tower, potentially on a
moving base, and the rotating blades are modeled using geometrically non-linear composite
beams, which are linearized around reference conditions with arbitrarily-large structural
displacements. Time-domain aerodynamics are given by a linearized 3-D unsteady vortexlattice
method and the resulting dynamic aeroelastic model is written in a state-space
formulation suitable for model reductions and control synthesis. A linear model of a single
blade is used to design a Linear-Quadratic-Gaussian regulator on its root-bending moments,
which is finally shown to provide load reductions of about 20% in closed-loop on the full
wind turbine non-linear aeroelastic model
Bayesian spline method for assessing extreme loads on wind turbines
This study presents a Bayesian parametric model for the purpose of estimating
the extreme load on a wind turbine. The extreme load is the highest stress
level imposed on a turbine structure that the turbine would experience during
its service lifetime. A wind turbine should be designed to resist such a high
load to avoid catastrophic structural failures. To assess the extreme load,
turbine structural responses are evaluated by conducting field measurement
campaigns or performing aeroelastic simulation studies. In general, data
obtained in either case are not sufficient to represent various loading
responses under all possible weather conditions. An appropriate extrapolation
is necessary to characterize the structural loads in a turbine's service life.
This study devises a Bayesian spline method for this extrapolation purpose,
using load data collected in a period much shorter than a turbine's service
life. The spline method is applied to three sets of turbine's load response
data to estimate the corresponding extreme loads at the roots of the turbine
blades. Compared to the current industry practice, the spline method appears to
provide better extreme load assessment.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS670 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Microtab dynamic modelling for wind turbine blade load rejection
A dynamic model characterising the effect of microtab deployment on the aerodynamics of its base aerofoil is presented. The developed model predicts the transient aerodynamic coefficients consistent with the experimental and computational data reported in the literature. The proposed model is then used to carry out investigation on the effectiveness of microtabs in load alleviation and lifespan increase of wind turbine blades. Simulating a bangâbang controller, different load rejection scenarios are examined and their effect on blade lifespan is investigated. Results indicate that the range of frequencies targeted for rejection can significantly impact the blade fatigue life.
Case studies are carried out to compare the predicted load alleviation amount and the blade lifespan using the developed model with those obtained by other researchers using the steady state model. It is shown that the assumption of an instantaneous aerodynamic response as used in the steady state model can lead to inaccurate results
Hysteresis-based design of dynamic reference trajectories to avoid saturation in controlled wind turbines
The main objective of this paper is to design a dynamic reference trajectory based on hysteresis to avoid saturation in controlled wind turbines. Basically, the torque controller and pitch controller set-points are hysteretically manipulated to avoid saturation and drive the system with smooth dynamic changes. Simulation results obtained from a 5MW wind turbine benchmark model show that our proposed strategy has a clear added value with respect to the baseline controller (a well-known and accepted industrial wind turbine controller). Moreover, the proposed strategy has been tested in healthy conditions but also in the presence of a realistic fault where the baseline controller caused saturation to nally conduct to instability.Peer ReviewedPostprint (author's final draft
Real-time predictive maintenance for wind turbines using Big Data frameworks
This work presents the evolution of a solution for predictive maintenance to
a Big Data environment. The proposed adaptation aims for predicting failures on
wind turbines using a data-driven solution deployed in the cloud and which is
composed by three main modules. (i) A predictive model generator which
generates predictive models for each monitored wind turbine by means of Random
Forest algorithm. (ii) A monitoring agent that makes predictions every 10
minutes about failures in wind turbines during the next hour. Finally, (iii) a
dashboard where given predictions can be visualized. To implement the solution
Apache Spark, Apache Kafka, Apache Mesos and HDFS have been used. Therefore, we
have improved the previous work in terms of data process speed, scalability and
automation. In addition, we have provided fault-tolerant functionality with a
centralized access point from where the status of all the wind turbines of a
company localized all over the world can be monitored, reducing O&M costs
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