This is the pre-peer reviewed version of the following article:Vidal, Y.; Bonet, J.; Huerta, A. Stabilized updated Lagrangian corrected SPH for explicit dynamic problems. "International journal for numerical methods in engineering", Març 2007, vol. 69, núm. 13, p. 2687-2710, which has been published in final form at http://www3.interscience.wiley.com/journal/112777203/abstractSmooth Particle Hydrodynamics with a total Lagrangian formulation are, in general, more robust than nite elements for large distortion problems. Nevertheless, updating the reference con¯guration\ud may still be necessary in some problems involving extremely large distortions. However, as discussed here a standard updated formulation suffers the presence of zero energy modes that are activated\ud and may spoil completely the solution. It is important to note that, unlike an Eulerian formulation,the updated Lagrangian does not present tension instability but only zero energy modes. Here an stabilization technique is incorporated to the updated formulation to obtain an improved method\ud without mechanisms and capable to solve problems with extremely large distortions.Peer ReviewedPostprint (author’s final draft

This paper addresses the problem of online fault detection of an advanced wind turbine benchmark under actuators (pitch and torque) and sensors (pitch angle measurement) faults of different type: fixed value, gain factor, offset and changed dynamics. The fault detection scheme starts by computing the baseline principal component analysis (PCA) model from the healthy or undamaged wind turbine. Subsequently, when the structure is inspected or supervised, new measurements are obtained are projected into the baseline PCA model. When both sets of data-the baseline and the data from the current wind turbine-are compared, a statistical hypothesis testing is used to make a decision on whether or not the wind turbine presents some damage, fault or misbehavior. The effectiveness of the proposed fault-detection scheme is illustrated by numerical simulations on a well-known large offshore wind turbine in the presence of wind turbulence and realistic fault scenarios. The obtained results demonstrate that the proposed strategy provides and early fault identification, thereby giving the operators sufficient time to make more informed decisions regarding the maintenance of their machines.

Incompressible modelling in finite elements has been a major concern since its early developments and has been extensively studied. However, incompressibility in meshfree methods is still an open topic. Thus, instabilities or locking can preclude the use of mesh-free approximations in such problems. Here, a novel mesh-free formulation is proposed for incompressible flow. It is based on defining a pseudo-divergence-free interpolation space. That is, the finite dimensional interpolation space approaches a divergence-free space when the discretization is refined. Note that such an interpolation does not include any overhead in the computations. The numerical evaluations are performed using the inf-sup numerical test and two well-known benchmark examples for Stokes flow.

This article states a condition monitoring strategy for wind turbines using a statistical data-driven modeling approach by means of supervisory control and data acquisition (SCADA) data. Initially, a baseline data-based model is obtained from the healthy wind turbine by means of multiway principal component analysis (MPCA). Then, when the wind turbine is monitorized, new data is acquired and projected into the baseline MPCA model space. The acquired SCADA data are treated as a random process given the random nature of the turbulent wind. The objective is to decide if the multivariate distribution that is obtained from the wind turbine to be analyzed (healthy or not) is related to the baseline one. To achieve this goal, a test for the equality of population means is performed. Finally, the results of the test can determine that the hypothesis is rejected (and the wind turbine is faulty) or that there is no evidence to suggest that the two means are different, so the wind turbine can be considered as healthy. The methodology is evaluated on a wind turbine fault detection benchmark that uses a 5 MW high-fidelity wind turbine model and a set of eight realistic fault scenarios. It is noteworthy that the results, for the presented methodology, show that for a wide range of significance, α ∈ [1%, 13%], the percentage of correct decisions is kept at 100%; thus it is a promising tool for real-time wind turbine condition monitoring.

The future of the wind energy industry passes through the use of larger and more flexible wind turbines in remote locations, which are increasingly o↵shore to benefit stronger and more uniform wind conditions. The cost of operation and maintenance of o↵shore wind turbines is approximately 15-35% of the total cost. Of this, 80% goes towards unplanned maintenance issues due to di↵erent faults in the wind turbine components. Thus, an auspicious way to contribute to the increasing demands and challenges is by applying low-cost advanced fault detection schemes. This work proposes a new method for detection and classification of wind turbine actuators and sensors faults in variablespeed wind turbines. For this purpose, time domain signals acquired from the operating wind turbine are represented as two-dimensional matrices to obtain grayscale digital images. Then, the image pattern recognition is processed getting texture features under a multichannel representation. In this work, four types of texture characteristics are used: statistical, wavelet, granulometric and Gabor features. Next, the most significant ones are selected using the conditional mutual criterion. Finally, the faults are detected and distinguished between them (classified) using an automatic classification tool. In particular, a 10-fold cross-validation is used to obtain a more generalized model and evaluates the classification performance. Coupled non-linear aero-hydro-servo-elastic simulations of a 5MW o↵shore type wind turbine are carried out in several fault scenarios. The results show a promising methodology able to detect and classify the most common wind turbine faults.

This paper develops a fault diagnosis (FD) and fault-tolerant control (FTC) of pitch actuators in wind turbines. This is accomplished by combining a disturbance compensator with a controller, both of which are formulated in the discrete time domain. The disturbance compensator has a dual purpose: to estimate the actuator fault (which is used by the FD algorithm) and to design the discrete time controller to obtain an FTC. That is, the pitch actuator faults are estimated, and then, the pitch control laws are appropriately modified to achieve an FTC with a comparable behavior to the fault-free case. The performance of the FD and FTC schemes is tested in simulations with the aero-elastic code FAST.

Due to the increasing installation of wind turbines in remote locations, both onshore and offshore, advanced fault detection and classification strategies have become crucial to accomplish the required levels of reliability and availability. In this work, without using specific tailored devices for condition monitoring but only increasing the sampling frequency in the already available (in all commercial wind turbines) sensors of the Supervisory Control and Data Acquisition (SCADA) system, a data-driven multi-fault detection and classification strategy is developed. An advanced wind turbine benchmark is used. The wind turbine we consider is subject to different types of faults on actuators and sensors. The main challenges of the wind turbine fault detection lie in their non-linearity, unknown disturbances, and significant measurement noise at each sensor. First, the SCADA measurements are pre-processed by group scaling and feature transformation (from the original high-dimensional feature space to a new space with reduced dimensionality) based on multiway principal component analysis through sample-wise unfolding. Then, 10-fold cross-validation support vector machines-based classification is applied. In this work, support vector machines were used as a first choice for fault detection as they have proven their robustness for some particular faults, but at the same time have never accomplished the detection and classification of all the proposed faults considered in this work. To this end, the choice of the features as well as the selection of data are of primary importance. Simulation results showed that all studied faults were detected and classified with an overall accuracy of 98.2%. Finally, it is noteworthy that the prediction speed allows this strategy to be deployed for online (real-time) condition monitoring in wind turbines.

As stated by the European Academy of Wind Energy (EAWE), the wind industry has identified main bearing failures as a critical issue in terms of increasing wind turbine reliability and availability. This is owing to major repairs with high replacement costs and long downtime periods associated with main bearing failures. Thus, the main bearing fault prognosis has become an economically relevant topic and is a technical challenge. In this work, a data-based methodology for fault prognosis is presented. The main contributions of this work are as follows: (i) Prognosis is achieved by using only supervisory control and data acquisition (SCADA) data, which is already available in all industrial-sized wind turbines; thus, no extra sensors that are designed for a specific purpose need to be installed. (ii) The proposed method only requires healthy data to be collected; thus, it can be applied to any wind farm even when no faulty data has been recorded. (iii) The proposed algorithm works under different and varying operating and environmental conditions. (iv) The validity and performance of the established methodology is demonstrated on a real underproduction wind farm consisting of 12 wind turbines. The obtained results show that advanced prognostic systems based solely on SCADA data can predict failures several months prior to their occurrence and allow wind turbine operators to plan their operations.