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In the context of optimized Operation & Maintenance of wind energy infrastructure, it is important to develop decision support tools, able to guide operators and engineers in the management of these assets. This task is particularly challenging given the multiplicity of uncertainties involved, from the point of view of the aggregated data, the available knowledge with respect to the wind turbine structures, and sub-components, as well as the constantly varying operational and environmental loads. We here propose to propagate wind turbine telemetry through a decision tree learning algorithm to detect faults, errors, damage, patterns, anomalies and abnormal operation. The use of decision trees is motivated by the fact that they tend to be easier to implement and interpret than other quantitative data-driven methods. Furthermore, the telemetry consists of data from condition and structural health monitoring systems, which lends itself nicely in the field of Big Data as large amounts are continuously sampled at high rate from thousands of wind turbines. In this paper, we review several decision tree algorithms formerly proposed by the machine learning community (i.e. ID3, C4.5, C5.0, J48, SPRINT, FACT, FIRM, SLIQ, CHAID, QUEST, CRUISE, PUBLIC,BOAT, RAINFOREST, MARS, RIPPER and CART), we then train an ensemble Bagged decision tree classifier on a large condition monitoring dataset from an offshore wind farm comprising 48 wind turbines, and use it to automatically extract paths linking excessive vibrations faults to their possible root causes. We finally give an outlook of an architecture to implement decision tree learning in the context of cloud computing for big data, namely involving a cloud based Apache Hadoop software for very large data storage and handling, and Apache Spark for efficiently running machine-learning algorithms.

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This study introduces a computational framework for efficient and accurate seismic fragility analysis based on a combination of artificial ground motion modeling, polynomial-chaos-based global sensitivity analysis, and hierarchical kriging surrogate modeling. The framework follows the philosophy of the Performance-Based Earthquake Engineering PEER approach, where the fragility analysis is decoupled from hazard analysis. This study addresses three criticalities that are present in the current practice. Namely, reduced size of hazardconsistent size-specific ensembles of seismic records, validation of structural simulators against large-scale experiments, high computational cost for accurate fragility estimates. The effectiveness of the proposed framework is demonstrated for the Rio Torto Bridge, recently tested using hybrid simulation within the RETRO project.

Harvesting ultra-low frequency random vibration, such as human motion or turbine tower oscillations, has always been a challenge, but could enable many potential self-powered sensing applications. In this paper, a methodology to effectively harness this type of energy is proposed using rotary-translational motion and bi-stability. A sphere rolling magnet is designed to oscillate in a tube with two tethering magnets underneath the rolling path, providing two stable positions for the oscillating magnet. The generated magnetic restoring forces are of periodic form with regard to the sphere magnet location, providing unique nonlinear dynamics and allowing the harvester to operate effectively at ultra-low frequencies (< 1 Hz). Two sets of coils are mounted above the rolling path, and the change of magnetic flux within the coils accomplishes the energy conversion. A theoretical model, including the magnetic forces, the electromagnetic conversion and the occurring bi-stability, is established to understand the electromechanical dynamics and guide the harvester design. End linear springs are designed to maintain the periodic double-well oscillation when the excitation magnitude is high. Parametric studies considering different design factors and operation conditions are conducted to analyze the nonlinear electromechanical dynamics. The harvester illustrates its capabilities in effectively harnessing ultra-low frequency motions over a wide range of low excitation magnitudes.

In this work we are addressing the problem of statistical modeling of the joint distribution of data collected from wind turbines interacting due to collective effect of their placement in a wind-farm, the wind characteristics (speed/orientation) and the turbine control. Operating wind turbines extract energy from the wind and at the same time produce wakes on the down-wind turbines in a park, causing reduced power production and increased vibrations, potentially contributing in a detrimental manner to fatigue life. This work presents a Variational Auto-Encoder (VAE) Neural Network architecture capable of mapping the high dimensional correlated stochastic variables over the wind-farm, such as power production and wind speed, to a parametric probability distribution of much lower dimensionality. We demonstrate how a trained VAE can be used in order to quantify levels of statistical deviation on condition monitoring data. Moreover, we demonstrate how the VAE can be used for pretraining an inference model, capable of predicting the power production of the farm together with bounds on the uncertainty of the predictions. Examples employing simulated wind-farm Supervisory Control And Data Acquisition (SCADA) data are presented. The simulated farm data are acquired from a Dynamic Wake Meandering (DWM) simulation of a small wind farm comprised of nine 5MW turbines in close spacing using OpenFAST. The contribution of this work lies in the introduction of state-of-the-art machine learning techniques in the general context of condition monitoring and uncertainty quantification. We show how the high dimensional joint probability distribution of condition monitoring parameters can be analyzed by exploiting the underlying lower dimensional structure of the data imposed by the physics of the problem. The process of making use of the trained joint distribution for the purposes of inference under uncertainty and condition monitoring is clearly exposed.

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