Fault Diagnosis of a Wind Turbine Simulated Model via Neural Networks

The fault diagnosis of wind turbine systems has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of wind turbines, and it proposes viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves a data-driven approach, as it represents an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data-driven proposed solution relies on neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the nonlinear autoregressive with exogenous input topology, as it can represent a dynamic evolution of the system along time. The developed fault diagnosis scheme is tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed solutions against the typical parameter uncertainties and disturbances

Fault Diagnosis of a Wind Turbine Simulated Model via Neural Networks

The fault diagnosis of wind turbine systems has been proven to be a challenging task and motivates the research activities carried out through this work. Therefore, this paper deals with the fault diagnosis of wind turbines, and it proposes viable solutions to the problem of earlier fault detection and isolation. The design of the fault indicator involves a data-driven approach, as it represents an effective tool for coping with a poor analytical knowledge of the system dynamics, together with noise and disturbances. In particular, the data-driven proposed solution relies on neural networks that are used to describe the strongly nonlinear relationships between measurement and faults. The chosen network architecture belongs to the nonlinear autoregressive with exogenous input topology, as it can represent a dynamic evolution of the system along time. The developed fault diagnosis scheme is tested by means of a high-fidelity benchmark model, that simulates the normal and the faulty behaviour of a wind turbine. The achieved performances are compared with those of other control strategies, coming from the related literature. Moreover, a Monte Carlo analysis validates the robustness of the proposed solutions against the typical parameter uncertainties and disturbances

Active Fault Tolerant Control of a Wind Farm System

In order to enhance the 'sustainability’ of offshore wind farms, thus skipping unplanned maintenance operations and costs, that can be important for offshore systems, the earlier management of faults represents the key point. Therefore, this work studies the development of an adaptive sustainable control scheme with application to a wind farm benchmark consisting of nine wind turbine systems. They are described via their nonlinear models, as well as the wind and wake effects among the wind turbines of the wind park. The fault tolerant control strategy uses the recursive estimation of the faults provided by nonlinear estimators designed via a nonlinear differential algebraic tool. This aspect of the study, together with the more straightforward solution based on a data-driven scheme, is the key issue when on-line applications are proposed for a viable implementation of the proposed solutions

Hardware-In-The-Loop Assessment of a Fault Tolerant Fuzzy Control Scheme for an Offshore Wind Farm Simulator

To enhance both the safety and the efficiency of offshore wind park systems, faults must be accommodated in their earlier occurrence, in order to avoid costly unplanned maintenance. Therefore, this paper aims at implementing a fault tolerant control strategy by means of a data-driven approach relying on fuzzy logic. In particular, fuzzy modelling is considered here as it enables to approximate unknown nonlinear relations, while managing uncertain measurements and disturbance. On the other hand, the model of the fuzzy controller is directly estimated from the input-output signals acquired from the wind farm system, with fault tolerant capabilities. In general, the use of purely nonlinear relations and analytic methods would require more complex design tools. The design is therefore enhanced by the use of fuzzy model prototypes obtained via a data-driven approach, thus representing the key point if real- time solutions have to implement the proposed fault tolerant control strategy. Finally, a high- fidelity simulator relying on a hardware-in-the-loop tool is exploited to verify and validate the reliability and robustness characteristics of the developed methodology also for on-line and more realistic implementations

Parameter identification for piecewise-affine fuzzy models in noisy environment

AbstractIn this paper the problem of identifying a fuzzy model from noisy data is addressed. The piecewise-affine fuzzy model structure is used as non-linear prototype for a multi–input, single–output unknown system. The consequents of the fuzzy model are identified from noisy data which are collected from experiments on the real system. The identification procedure is formulated within the Frisch scheme, well established for linear systems, which is extended so that it applies to piecewise-affine, constrained models

Flavor decomposition of the sea quark helicity distributions in the nucleon from semi-inclusive deep-inelastic scattering

Double-spin asymmetries of semi-inclusive cross sections for the production of identified pions and kaons have been measured in deep-inelastic scattering of polarized positrons on a polarized deuterium target. Five helicity distributions including those for three sea quark flavors were extracted from these data together with re-analyzed previous data for identified pions from a hydrogen target. These distributions are consistent with zero for all three sea flavors. A recently predicted flavor asymmetry in the polarization of the light quark sea appears to be disfavored by the data.Comment: 5 pages, 3 figure

Nuclear Polarization of Molecular Hydrogen Recombined on a Non-metallic Surface

The nuclear polarization of $\mathrm{H}_2$ molecules formed by recombination of nuclear polarized H atoms on the surface of a storage cell initially coated with a silicon-based polymer has been measured by using the longitudinal double-spin asymmetry in deep-inelastic positron-proton scattering. The molecules are found to have a substantial nuclear polarization, which is evidence that initially polarized atoms retain their nuclear polarization when absorbed on this type of surfac

First Measurement of the Tensor Structure Function b1b_1 of the Deuteron

The \Hermes experiment has investigated the tensor spin structure of the deuteron using the 27.6 GeV/c positron beam of \Hera. The use of a tensor polarized deuteron gas target with only a negligible residual vector polarization enabled the first measurement of the tensor asymmetry \At and the tensor structure function \bd for average values of the Bj{\o}rken variable $0.01<0.45$ and of the squared four-momentum transfer $0.5 {\rm GeV^2} <5 {\rm GeV^2}$. The quantities \At and \bd are found to be non-zero. The rise of \bd for decreasing values of $x$ can be interpreted to originate from the same mechanism that leads to nuclear shadowing in unpolarized scattering

A gas analyzer for the internal polarized target of the HERMES experiment

A gas analyzer has been developed for the internal polarized target of the HERMES experiment at DESY in order to determine the relative amount of atomic and molecular hydrogen or deuterium in a gas sample. The precise quantitative knowledge of this ratio is crucial because the nucleons in atoms and molecules contribute differently to the average nuclear polarization of the target gas. A new calibration technique used to derive the relative sensitivity to atoms and molecules is presented. As an example, it is shown how the gas analyzer is used within the HERMES environment to divide the molecules in the gas sample into an unpolarized and a potentially polarized fraction

The Q^2-Dependence of Nuclear Transparency for Exclusive ρ0\rho^0 Production

Exclusive coherent and incoherent electroproduction of the $\rho^0$ meson from $^1$H and $^{14}$N targets has been studied at the HERMES experiment as a function of coherence length ($l_c$), corresponding to the lifetime of hadronic fluctuations of the virtual photon, and squared four-momentum of the virtual photon ($-Q^2$). The ratio of $^{14}$N to $^1$H cross sections per nucleon, known as nuclear transparency, was found to increase (decrease) with increasing coherence length for coherent (incoherent) $\rho^0$ electroproduction. For fixed coherence length, a rise of nuclear transparency with $Q^2$ is observed for both coherent and incoherent $\rho^0$ production, which is in agreement with theoretical calculations of color transparency.Comment: 5 pages, 4 figure