122,742 research outputs found
Short and long-term wind turbine power output prediction
In the wind energy industry, it is of great importance to develop models that
accurately forecast the power output of a wind turbine, as such predictions are
used for wind farm location assessment or power pricing and bidding,
monitoring, and preventive maintenance. As a first step, and following the
guidelines of the existing literature, we use the supervisory control and data
acquisition (SCADA) data to model the wind turbine power curve (WTPC). We
explore various parametric and non-parametric approaches for the modeling of
the WTPC, such as parametric logistic functions, and non-parametric piecewise
linear, polynomial, or cubic spline interpolation functions. We demonstrate
that all aforementioned classes of models are rich enough (with respect to
their relative complexity) to accurately model the WTPC, as their mean squared
error (MSE) is close to the MSE lower bound calculated from the historical
data. We further enhance the accuracy of our proposed model, by incorporating
additional environmental factors that affect the power output, such as the
ambient temperature, and the wind direction. However, all aforementioned
models, when it comes to forecasting, seem to have an intrinsic limitation, due
to their inability to capture the inherent auto-correlation of the data. To
avoid this conundrum, we show that adding a properly scaled ARMA modeling layer
increases short-term prediction performance, while keeping the long-term
prediction capability of the model
Reliability assessment of cutting tool life based on surrogate approximation methods
A novel reliability estimation approach to the cutting tools based on advanced approximation methods is proposed. Methods such as the stochastic response surface and surrogate modeling are tested, starting from a few sample points obtained through fundamental experiments and extending them to models able to estimate the tool wear as a function of the key process parameters. Subsequently, different reliability analysis methods are employed such as Monte Carlo simulations and first- and second-order reliability methods. In the present study, these reliability analysis methods are assessed for estimating the reliability of cutting tools. The results show that the proposed method is an efficient method for assessing the reliability of the cutting tool based on the minimum number of experimental results. Experimental verification for the case of high-speed turning confirms the findings of the present study for cutting tools under flank wear
Wave Extremes in the North East Atlantic from Ensemble Forecasts
A method for estimating return values from ensembles of forecasts at advanced
lead times is presented. Return values of significant wave height in the
North-East Atlantic, the Norwegian Sea and the North Sea are computed from
archived +240-h forecasts of the ECMWF ensemble prediction system (EPS) from
1999 to 2009.
We make three assumptions: First, each forecast is representative of a
six-hour interval and collectively the data set is then comparable to a time
period of 226 years. Second, the model climate matches the observed
distribution, which we confirm by comparing with buoy data. Third, the ensemble
members are sufficiently uncorrelated to be considered independent realizations
of the model climate. We find anomaly correlations of 0.20, but peak events
(>P97) are entirely uncorrelated. By comparing return values from individual
members with return values of subsamples of the data set we also find that the
estimates follow the same distribution and appear unaffected by correlations in
the ensemble. The annual mean and variance over the 11-year archived period
exhibit no significant departures from stationarity compared with a recent
reforecast, i.e., there is no spurious trend due to model upgrades.
EPS yields significantly higher return values than ERA-40 and ERA-Interim and
is in good agreement with the high-resolution hindcast NORA10, except in the
lee of unresolved islands where EPS overestimates and in enclosed seas where it
is biased low. Confidence intervals are half the width of those found for
ERA-Interim due to the magnitude of the data set.Comment: 27 pp, 10 figures, J Climate (in press
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