pinecone/core-2020-05-10-deduplication huggingface dataset:
Optimization-based load reduction during rapid shutdown of multi-megawatt wind turbine generators

This paper describes an optimization-based approach to reducing extreme structural loads during rapid or emergency shutdown of multi-megawatt wind turbine generators. The load reduction problem is cast into an optimal control formulation, and a simple, low-order model is developed in order for this optimization problem to be tractable in reasonable time using state-of-the-art numerical methods. To handle the variations in wind speed and turbulence inherent to wind turbine operation as well as the presence of model mismatch, a real-time optimization strategy based on fast sensitivity updates is also considered, whose online computational burden is limited to the repeated solution of quadratic programs that are designed offline. The low-order model and both the open-loop and closed-loop optimal control strategies are validated against a high-fidelity model in the simulation environment Bladed™ for an industrial 3 MW wind turbine. Under favorable shutdown scenarios, i.e. when the wind turbine is operating properly and the actuators and sensors are not faulty, large reductions of the first compressive peak and subsequent compressive/tensile peaks of the tower load pattern are obtained at various above-rated wind speeds compared with normal pitch control shutdown. Extension to more challenging shutdown scenarios are also discussed. Copyright © 2013 John Wiley & Sons, Ltd. Copyright © 2013 John Wiley & Sons, Ltd
Improving wind turbine drivetrain bearing reliability through pre-misalignment.

Improving the reliability of wind turbines (WT) is an essential component in the bid to minimize the cost of energy, especially for offshore wind because of the difficulties associated with access for maintenance. Numerous studies have shown that WT gearbox and generator failure rates are unacceptably high, particularly given the long downtime incurred per failure. There is evidence that bearing failures of the gearbox high-speed stage (HSS) and generator account for a significant proportion of these failures. However, the root causes of these failure data are not known, and there is therefore a need for fundamental computational studies to support the valuable ‘top down’ reliability analyses. In this paper, a real (proprietary) 2 MW geared WT was modelled to compute the gearbox–generator misalignment and predict the impact of this misalignment upon the gearbox HSS and generator bearings. At rated torque, misalignment between the gearbox and generator of 8500 µm was seen. For the 2 MW WT analysed, the computational data show that the L10 fatigue lives of the gearbox HSS bearings were not significantly affected by this misalignment but that the L10 fatigue lives of the generator bearings, particularly the drive-end bearing, could be significantly reduced. It is proposed to apply a nominal offset to the generator to reduce the misalignment under operation, thereby reducing the loading on the gearbox HSS and generator bearings. The value of performing integrated system analyses has been demonstrated, and a robust methodology has been outlined
Copula based model for wind turbine power curve outlier rejection

Power curve measurements provide a conventional and effective means of assessing the performance of a wind turbine, both commercially and technically. Increasingly high wind penetration in power systems and offshore accessibility issues make it even more important to monitor the condition and performance of wind turbines based on timely and accurate wind speed and power measurements. Power curve data from Supervisory Control and Data Acquisition (SCADA) system records, however, often contains significant measurement deviations, which are commonly produced as a consequence of wind turbine operational transitions rather than stemming from physical degradation of the plant. To use this raw data for wind turbine condition monitoring purposes is thus likely to lead to high false alarm rates which would make the actual fault detection unreliable and potentially add unnecessarily to the costs of maintenance. To this end, this paper proposes a probabilistic outlier exclusion method developed around a Copula based joint probability model. This approach has the capability of capturing the complex multivariate nonlinear relation between parameters based on their univariate marginal distributions through the use of Copula; data points that deviate significantly from the consolidated power curve can then be removed depending on this derived joint probability distribution. After data filtering in this manner, it is shown how the resulting power curves are better defined and less subject to uncertainty, whilst broadly retaining the same dominant statistical characteristics. These improved power curves make subsequent condition monitoring more effective in the reliable detection of faults
Wind speed variability across the UK between 1957 and 2011

This is the pre-peer reviewed version of the article, which has been published in final form at: http://onlinelibrary.wiley.com/doi/10.1002/we.1679/abstract. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.Using surface data from 57 UK meteorological stations, a national (BADC-57) and regional wind index for the UK has been calculated for the period 1983 to 2011. For a subset of seven stations, an additional national index (BADC-7) has been calculated for the period 1957 to 2011. The indices show an annual variability of 4% over their respective periods corresponding to a variation in typical wind turbine capacity factor of 7%. These indices are compared with indices calculated from other sources, namely: an index generated using a gridded dataset of observed values interpolated across the UK; an index calculated from an area bounding the UK using the ERA-40 reanalysis dataset; indices calculated from bilinear interpolation of the ERA-40\ud
reanalysis dataset to the 57 and seven stations; and another independent UK wind index. The indices show variation in trends with all showing some level of decline with the exception of that generated using the ERA-40 reanalysis dataset averaged over the UK which shows a significant increase. The various indices show varying degrees of agreement with correlation coefficients, after trends are removed, ranging between 0.611-0.979. The effect of changes in site exposure, instrument bias and measuring height were considered for the BADC-7 and BADC-57 indices. The change in instrument measurement height appears to have a significant biasing effect and it is likely that this along with changes in exposure at urban sites have caused the decline in annual wind speeds observed for some of the indices. There does not appear to be evidence for significant changes in large area (mesoscale) surface roughness. The correlation between annual mean wind speeds at the seven surface station sites used to calculate the BADC-7 index is seen to be quite weak indicating very localised variations in inter-annual variability. When regional differences in the index are investigated, it is seen that wind speeds show a very slight decline across the UK in all regions except the south-east, which shows a slight increase. The greatest decrease is seen in the north-west. These changes are in the same direction as the tentative predictions given by climate models for future changes in wind speed across the UK, though the uncertainty is large given the large degree of inter-annual variation
Energy storage sizing for wind power: impact of the autocorrelation of day-ahead forecast errors

International audienceAvailability of day-ahead production forecast is an important step towards better dispatchability of wind power production. However, the stochastic nature of forecast errors prevents a wind farm operator from holding a firm production commitment. In order to mitigate the deviation from the commitment, an energy storage system connected to the wind farm is considered. One statistical characteristic of day-ahead forecast errors has a major impact on storage performance: errors are significantly correlated along several hours. We thus use a data-fitted autoregressive model that captures this correlation to quantify the impact of correlation on storage sizing. With a Monte Carlo approach, we study the behavior and the performance of an energy storage system (ESS) using the autoregressive model as an input. The ability of the storage system to meet a production commitment is statistically assessed for a range of capacities, using a mean absolute deviation criterion. By parametrically varying the correlation level, we show that disregarding correlation can lead to an underes- timation of a storage capacity by an order of magnitude. Finally, we compare the results obtained from the model and from field data to validate the model
Quantifying the contribution of wind farms to distribution network reliability.

The reliability of power supply to distribution network customers can be increased by embedded generation, including wind farms. The value of this increase in reliability needs to be evaluated, and national standards such as the Great Britain security of supply standard P2/6 seek to do so. This paper appraises the capacity credit evaluation methodology in P2/6 and outlines an alternative methodology to integrate generation with load more effectively, taking into account the topology, loading and reliability of the surrounding network. It concludes that under certain circumstances, the presence of embedded wind generation can allow the deferral of costly network reinforcement projects but that the time for which reinforcement can reasonably be deferred is a function not only of the generators themselves but also of the surrounding network
Langevin power curve analysis for numerical WEC models with new insights
  on high frequency power performance

Based on the Langevin equation it has been proposed to obtain power curves
for wind turbines from high frequency data of wind speed measurements u(t) and
power output P (t). The two parts of the Langevin approach, power curve and
drift field, give a comprehensive description of the conversion dynamic over
the whole operating range of the wind turbine. The method deals with high
frequent data instead of 10 min means. It is therefore possible to gain a
reliable power curve already from a small amount of data per wind speed.
Furthermore, the method is able to visualize multiple fixed points, which is
e.g. characteristic for the transition from partial to full load or in case the
conversion process deviates from the standard procedures. In order to gain a
deeper knowledge it is essential that the method works not only for measured
data but also for numerical wind turbine models and synthetic wind fields.
Here, we characterize the dynamics of a detailed numerical wind turbine model
and calculate the Langevin power curve for different data samplings. We show,
how to get reliable results from synthetic data and verify the applicability of
the method for field measurements with ultra-sonic, cup and Lidar measurements.
The independence of the fixed points on site specific turbulence effects is
also confirmed with the numerical model. Furthermore, we demonstrate the
potential of the Langevin approach to detect failures in the conversion process
and thus show the potential of the Langevin approach for a condition monitoring
system.Comment: in Wind Energy (2014
Aerodynamic design optimization of wind turbine rotors under geometric uncertainty

Presented is a robust optimization strategy for the aerodynamic design of horizontal axis wind turbine rotors including the variability of the annual energy production due to the uncertainty of the blade geometry caused by manufacturing and assembly errors. The energy production of a rotor designed with the proposed robust optimization approach features lower sensitivity to stochastic geometry errors with respect to that of a rotor designed with the conventional deterministic optimization approach that ignores these errors. The geometry uncertainty is represented by normal distributions of the blade pitch angle, and the twist angle and chord of the airfoils. The aerodynamic module is a blade-element momentum theory code. Both Monte Carlo sampling and the univariate reduced quadrature technique, a novel deterministic uncertainty analysis method, are used for uncertainty propagation. The performance of the two approaches is assessed in terms of accuracy and computational speed. A two-stage multi-objective evolutionbased optimization strategy is used. Results highlight that, for the considered turbine type, the sensitivity of the annual energy production to rotor geometry errors can be reduced by reducing the rotational speed and increasing the blade loading. The primary objective of the paper is to highlight how to incorporate an efficient and accurate uncertainty propagation strategy in wind turbine design. The formulation of the considered design problem does not include all the engineering constraints adopted in real turbine design, but the proposed probabilistic design strategy is fairly independent of the problem definition and can be easily extended to turbine design systems of any complexity
An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access

This paper demonstrates that wave height forecasters chosen on statistical quality metrics result in sub-optimal decision support for offshore wind farm maintenance. Offshore access is constrained by wave height, but the majority of approaches to evaluating the effectiveness of a wave height forecaster utilize overall accuracy or error rates. This paper introduces a new metric more appropriate to the wind industry, which considers the economic impact of an incorrect forecast above or below critical wave height boundaries. The paper describes a process for constructing a value criteria where the implications between forecasting error and economic consequences are explicated in terms of opportunity costs and realized maintenance costs. A comparison between nine forecasting techniques for modeling and predicting wave heights based on historical data, including an ensemble aggregator, is described demonstrating that the performance ranking of forecasters is sensitive to the evaluation criteria. The results highlight the importance of appropriate metrics for wave height prediction specific to the wind industry, and the limitations of current models that minimize a metric that does not support decision making. With improved ability to forecast weather windows, maintenance scheduling is subject to less uncertainty, hence reducing costs related to vessel dispatch, and lost energy due to downtime
Effects of turbine spacing on the power output of extended wind-farms

We present results from large eddy simulations (LES) of extended wind-farms
for several turbine configurations with a range of different spanwise and
streamwise spacing combinations. The results show that for wind-farms arranged
in a staggered configuration with spanwise spacings in the range
$\approx[3.5,8]$D, where $D$ is the turbine diameter, the power output in the
fully developed regime depends primarily on the geometric mean of the spanwise
and streamwise turbine spacings. In contrast, for the aligned configuration the
power output in the fully developed regime strongly depends on the streamwise
turbine spacing and shows weak dependence on the spanwise spacing. Of interest
to the rate of wake recovery, we find that the power output is well correlated
with the vertical kinetic energy flux, which is a measure of how much kinetic
energy is transferred into the wind-turbine region by the mean flow. A
comparison between the aligned and staggered configurations reveals that the
vertical kinetic energy flux is more localized along turbine rows for aligned
wind-farms than for staggered ones. This additional mixing leads to a
relatively fast wake recovery for aligned wind-farms.Comment: 12 pages, 8 figure
Intelligent integrated maintenance for wind power generation

A novel architecture and system for the provision of Reliability Centred Maintenance (RCM) for offshore wind power generation is presented. The architecture was developed by conducting a bottom-up analysis of the data required to support RCM within this specific industry, combined with a top-down analysis of the required maintenance functionality. The architecture and system consists of three integrated modules for Intelligent Condition Monitoring, Reliability and Maintenance Modelling, and Maintenance Scheduling that provide a scalable solution for performing dynamic, efficient and cost effective preventative maintenance management within this extremely demanding renewable energy generation sector. The system demonstrates for the first time, the integration of state-of-the-art advanced mathematical techniques: Random Forests, Dynamic Bayesian Networks, and Memetic Algorithms in the development of an intelligent autonomous solution. The results from the application of the intelligent integrated system illustrated the automated detection of faults within a wind farm consisting of over 100 turbines, the modelling and updating of the turbines’ survivability and creation of a hierarchy of maintenance actions, and the optimising of the maintenance schedule with a view to maximising the availability and revenue generation of the turbines
Mapping hail meteorological observations for prediction of erosion in wind turbines

Wind turbines are subject to a wide range of environmental conditions during a lifespan that can conceivably extend beyond 20 years. Hailstone impact is thought to be a key factor in the leading edge erosion and damage of wind turbine blades. Along with the size and density of the hailstone, the aggregated impact velocity components are crucial variables that characterise the kinetic energy associated with singular impact. These components include: the terminal velocity of the hailstone, the mean wind speed and the rotational speed of the turbine. Theorised values for the impact velocity may not truly reflect the conditions experienced by wind turbine blades. Using UK meteorological data, a greater representation of hail characteristics, occurrence probabilities and realistic impact component velocities is proposed, which will assist in the development of a realistic damage model for hailstone impact
Dependence of optimal wind turbine spacing on wind farm length

Recent large eddy simulations have led to improved parameterizations of the
effective roughness height of wind farms. This effective roughness height can
be used to predict the wind velocity at hub-height as function of the geometric
mean of the spanwise and streamwise turbine spacings and the turbine loading
factors. Meyers and Meneveau (Wind Energy 15, 305-317 (2012)) used these
parameterizations to make predictions for the optimal wind turbine spacing in
infinitely large wind farms. They found that for a realistic cost ratio between
the turbines and the used land surface the optimal turbine spacing may be
considerably larger than used in conventional wind farms. Here we extend this
analysis by taking the length of the wind farm, i.e.\ the number of rows in the
downstream direction, into account and show that the optimal turbine spacing
strongly depends on the wind farm length. For small to moderately sized wind
farms the model predictions are consistent with spacings found in operational
wind farms. For much larger wind farms the extended optimal spacing found for
infinite wind farms is confirmed.Comment: 13 pages, 9 figure
Failure rate, repair time and unscheduled O&M cost analysis of offshore wind turbines

Determining and understanding offshore wind turbine failure rates and resource requirement for repair are vital for modelling and reducing O&M costs and in turn reducing the cost of energy. While few offshore failure rates have been published in the past even less details on resource requirement for repair exist in the public domain. Based on ~350 offshore wind turbines throughout Europe this paper provides failure rates for the overall wind turbine and its sub-assemblies. It also provides failure rates by year of operation, cost category and failure modes for the components/sub-assemblies that are the highest contributor to the overall failure rate. Repair times, average repair costs and average number of technicians required for repair are also detailed in this paper. An onshore to offshore failure rate comparison is carried out for generators and converters based on this analysis and an analysis carried out in a past publication. The results of this paper will contribute to offshore wind O&M cost and resource modelling and aid in better decision making for O&M planners and managers
Adjustment of wind farm power output through flexible turbine operation using wind farm control

When the installed capacity of wind power becomes high, the power generated by wind farms can no longer simply be that dictated by the wind speed.With sufficiently high penetration, it will be necessary for wind farms to provide assistance with supply-demand matching. The work presented here introduces a wind farm controller that regulates the power generated by the wind farm to match the grid requirements by causing the power generated by each turbine to be adjusted. Further benefits include fast response to reach the wind farm power demanded, flexibility, little fluctuation in the wind farm power output and provision of synthetic inertia
Generalized coupled wake boundary layer model: applications and
  comparisons with field and LES data for two wind-farms

We describe a generalization of the Coupled Wake Boundary Layer (CWBL) model
for wind-farms that can be used to evaluate the performance of wind-farms under
arbitrary wind inflow directions whereas the original CWBL model (Stevens et
al., J. Renewable and Sustainable Energy 7, 023115 (2015)) focused on aligned
or staggered wind-farms. The generalized CWBL approach combines an analytical
Jensen wake model with a "top-down" boundary layer model coupled through an
iterative determination of the wake expansion coefficient and an effective wake
coverage area for which the velocity at hub-height obtained using both models
converges in the "deep-array" portion (fully developed region) of the
wind-farm. The approach accounts for the effect of the wind direction by
enforcing the coupling for each wind direction. Here we present detailed
comparisons of model predictions with LES results and field measurements for
the Horns Rev and Nysted wind-farms operating over a wide range of wind inflow
directions. Our results demonstrate that two-way coupling between the Jensen
wake model and a "top-down" model enables the generalized CWBL model to predict
the "deep-array" performance of a wind-farm better than the Jensen wake model
alone. The results also show that the new generalization allows us to study a
much larger class of wind-farms than the original CWBL model, which increases
the utility of the approach for wind-farm designers.Comment: 17 pages, 11 figure
The application of passive air jet vortex-generators to stall suppression on wind turbine blades

An experimental study was performed to assess the feasibility of passive air jet vortex-generators to the performance enhancement of a domestic scale wind turbine. It has been demonstrated that these simple devices, properly designed and implemented, can provide worthwhile performance benefits for domestic wind turbines of the type investigated in this study. In particular, this study shows that they can increase the maximum output power coefficient, reduce the cut-in wind speed and improve power output at lower wind speeds while reducing the sensitivity to wind speed unsteadiness. A theoretical performance analysis of a 500 kW stall-regulated wind turbine, based on blade element momentum theory, indicates that passive air jet vortex-generators would be capable of recovering some of the power loss because of blade stall, thereby allowing attainment of rated power output at slightly lower average wind speed