109 research outputs found
Perspectives on SCADA Data Analysis Methods for Multivariate Wind Turbine Power Curve Modeling
Wind turbines are rotating machines which are subjected to non-stationary conditions and their power depends non-trivially on ambient conditions and working parameters. Therefore, monitoring the performance of wind turbines is a complicated task because it is critical to construct normal behavior models for the theoretical power which should be extracted. The power curve is the relation between the wind speed and the power and it is widely used to monitor wind turbine performance. Nowadays, it is commonly accepted that a reliable model for the power curve should be customized on the wind turbine and on the site of interest: this has boosted the use of SCADA for data-driven approaches to wind turbine power curve and has therefore stimulated the use of artificial intelligence and applied statistics methods. In this regard, a promising line of research regards multivariate approaches to the wind turbine power curve: these are based on incorporating additional environmental information or working parameters as input variables for the data-driven model, whose output is the produced power. The rationale for a multivariate approach to wind turbine power curve is the potential decrease of the error metrics of the regression: this allows monitoring the performance of the target wind turbine more precisely. On these grounds, in this manuscript, the state-of-the-art is discussed as regards multivariate SCADA data analysis methods for wind turbine power curve modeling and some promising research perspectives are indicated
Finite-size corrections to the rotating string and the winding state
We compute higher order finite size corrections to the energies of the
circular rotating string on AdS_5 x S^5, of its orbifolded generalization on
AdS_5 x S^5/Z_M and of the winding state which is obtained as the limit of the
orbifolded circular string solution when J -> infinity and J/M^2 is kept fixed.
We solve, at the first order in lambda'=lambda/J^2, where lambda is the 't
Hooft coupling, the Bethe equations that describe the anomalous dimensions of
the corresponding gauge dual operators in an expansion in m/K, where m is the
winding number and K is the "magnon number", and to all orders in the angular
momentum J. The solution for the circular rotating string and for the winding
state can be matched to the energy computed from an effective quantum
Landau-Lifshitz model beyond the first order correction in 1/J. For the leading
1/J corrections to the circular rotating string in m^2 and m^4 and for the
subleading 1/J^2 corrections to the m^2 term, we find agreement. For the
winding state we match the energy completely up to, and including, the order
1/J^2 finite-size corrections. The solution of the Bethe equations
corresponding to the spinning closed string is also provided in an expansion in
m/K and to all orders in J.Comment: v2: 33 pages, misprints corrected, references added, version accepted
for publication in JHE
Acoustically efficient concrete: acoustic absorption coefficient of porous concrete with different aggregate size
Porous absorbers are the most widely used type of acoustically absorptive materials. The interest on their outdoor applications has put further attention on the use of porous concrete in the building industry. This work investigates the acoustic properties of porous concrete. The assessment of the sound absorbing performances has been conducted in the small-scale reverberation room of Politecnico di Torino (Italy), in agreement with the indication in the ISO 354:2003 Standard. For each concrete type, three panel thicknesses, i.e. 20 mm, 40 mm, 60 mm were tested. Moreover, different mounting methods were tested, considering the presence of an airgap between the panel and the backing, and considering the introduction of rockwool in the airgap itself. The result show weighted absorption coefficients (aw) in the range 0.30-0.75 depending on the thickness and mounting conditions. These encouraging values make these materials useful for practical applications in architecture and civil engineering
Wind Energy Forecast in Complex Sites with a Hybrid Neural Network and CFD based Method
Abstract The wind is an intermittent renewable energy source and the energy production forecast is a fundamental activity for many reasons (grid regulation, maintenance, etc.). In this work a hybrid method (based on weather forecast data, neural networks and computational fluid dynamics) and a pure neural network approach are compared in a complex terrain site. The post processing of real production data has been discovered to be a key activity. Treatment and filtering of data spreading out from the supervisory control and data acquisition system are fundamental both for training and testing methods reliability
Long Term Wind Turbine Performance Analysis Through SCADA Data: A Case Study
Performance monitoring of horizontal-axis wind turbines is a complex task because they operate under nonstationary conditions. Furthermore, in real-world applications, there can be data quality issues because the free stream wind speed is reconstructed through a nacelle transfer function from cup anemometers measurements collected behind the rotor span. Given these matters of fact, one of the objectives of the present work is applying an innovative method for correcting the nacelle wind speed measurements, which is based on the manufacturer power curve and statistical considerations. Three operating wind turbines, having 2 MW of rated power and owned by the ENGIE Italia company, are contemplated as test cases. Operation data spanning ten years (2011–2020) are studied: actually, this work aims as well at contributing to the methods for estimating the performance decline with age of wind turbines, basing on long term SCADA data analysis. The raw and corrected wind speed measurements are fed as input to a Support Vector Regression for the power curve: by selecting appropriately the training and validation data sets, it is possible to estimate the average yearly rate of performance decline. Using the corrected wind speed, the estimate obtained in this study is compatible with the most recent findings in the literature, which indicate a -0.17% decline per year
Finite-size corrections in the SU(2) x SU(2) sector of type IIA string theory on AdS_4 x CP^3
We consider finite-size corrections in the SU(2) x SU(2) sector of type IIA
string theory on AdS_4 x CP^3, which is the string dual of the recently
constructed N=6 superconformal Chern-Simons theory of Aharony, Bergman,
Jafferis and Maldacena (ABJM theory). The string states we consider are in the
R x S^2 x S^2 subspace of AdS_4 x CP^3 with an angular momentum J on CP^3 being
large. We compute the finite-size corrections using two different methods, one
is to consider curvature corrections to the Penrose limit giving an expansion
in 1/J, the other by considering a low energy expansion in lambda'=lambda/J^2
of the string theory sigma-model, lambda being the 't Hooft coupling of the
dual ABJM theory. For both methods there are interesting issues to deal with.
In the near-pp-wave method there is a 1/\sqrt{J} interaction term for which we
use zeta-function regularization in order to compute the 1/J correction to the
energy. For the low energy sigma-model expansion we have to take into account a
non-trivial coupling to a non-dynamical transverse direction. We find agreement
between the two methods. At order lambda' and lambda'^2, for small lambda', our
results are analogous to the ones for the SU(2) sector in type IIB string
theory on AdS_5 x S^5. Instead at order lambda'^3 there are interactions
between the two two-spheres. We compare our results with the recently proposed
all-loop Bethe ansatz of Gromov and Vieira and find agreement.Comment: 21 pages. v2: typos fixed, refs. added. v3: misprints corrected,
refs. adde
How Wind Turbines Alignment to Wind Direction Affects Efficiency? A Case Study through SCADA Data Mining
SCADA control systems are the keystone for reliable performance optimization of wind farms. Processing into knowledge the amount of information they spread is a challenging task, involving engineering, physics, statistics and computer science skills. The present work deals with the effects on the efficiency of turbine inability of optimal aligning to the wind direction, due to meandering wind caused by wakes. The approach is tested on a judiciously chosen cluster of turbines of a wind farm sited in southern Italy. By a post-processing method based on discretization of nacelle position measurements, a set of dominant patterns of the cluster is identified. The patterns associated to best performances are individuated and it is shown that they correspond to non-trivial alignment to wind direction
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