Use of advanced analytics for health estimation and failure prediction in wind turbines

Tesi en modalitat de tesi per compendiThe energy sector has undergone drastic changes and critical revolutions in the last few decades. Renewable energy sources have grown significantly, now representing a sizeable share of the energy production mix. Wind energy has seen increasing rate of adoptions, being one of the more convenient and sustainable mean of producing energy. Research and innovation have helped greatly in driving down production and operation costs of wind energy, yet important challenges still remain open. This thesis addresses predictive maintenance and monitoring of wind turbines, aiming to present predictive frameworks designed with the necessities of the industry in mind. More concretely: interpretability, scalability, modularity and reliability of the predictions are the objectives —together with limited data requirements— of this project. Of all the available data at the disposal of wind turbine operators, SCADA is the principal source of information utilized in this research, due to its wide availability and low cost. Ensemble models played an important role in the development of the presented predictive frameworks thanks to their modular nature which allows to combine very diverse algorithms and data types. Important insights gained from these experiments are the beneficial effect of combining multiple and diverse sources of data —for example SCADA and alarms logs—, the easiness of combining different algorithms and indicators, and the noticeable gain in predicting performance that it can provide. Finally, given the central role that SCADA data plays in this thesis, but also in the wind energy industry, a detailed analysis of the limitations and shortcomings of SCADA data is presented. In particular, the ef- fect of data aggregation —a common practice in the wind industry— is determined developing a methodological framework that has been used to study high–frequency SCADA data. This lead to the conclusion that typical aggregation periods, i.e. 5–10 minutes that are the standard in wind energy industry are not able to capture and maintain the information content of fast–changing signals, such as wind and electrical measurements.El sector energètic ha experimentat importants canvis i revolucions en les últimes dècades. Les fonts d’energia renovables han crescut significativament, i ara representen una part important en el conjunt de generació. L’energia eòlica ha augmentat significativament, convertint-se en una de les millors alternatives per produir energia verda. La recerca i la innovació ha ajudat a reduir considerablement els costos de producció i operació de l’energia eòlica, però encara hi ha oberts reptes importants. Aquesta tesi aborda el manteniment predictiu i el seguiment d’aerogeneradors, amb l’objectiu de presentar solucions d’algoritmes de predicció dissenyats tenint en compte les necessitats de la indústria. Més concretament conceptes com, la interpretabilitat, escalabilitat, modularitat i fiabilitat de les prediccions ho són els objectius, juntament amb els requisits limitats per les de dades disponibles d’aquest projecte. De totes les dades disponibles a disposició dels operadors d’aerogeneradors, les dades del sistema SCADA són la principal font d’informació utilitzada en aquest projecte, per la seva àmplia disponibilitat i baix cost. En el present treball, els models de conjunt tenen un paper important en el desenvolupament dels marcs predictius presentats gràcies al seu caràcter modular que permet l’ús d’algoritmes i tipus de dades molt diversos. Resultats importants obtinguts d’aquests experiments són l’efecte beneficiós de combinar múltiples i diverses fonts de dades, per exemple, SCADA i dades d’alarmes, la facilitat de combinar diferents algorismes i indicadors i el notable guany en predir el rendiment que es pot oferir. Finalment, donat el paper central que SCADA l’anàlisi de dades juga en aquesta tesi, però també en la indústria de l’energia eòlica, una anàlisi detallada de la es presenten les limitacions i les mancances de les dades SCADA. En particular es va estudiar l’efecte de l’agregació de dades -una pràctica habitual en la indústria eòlica-. Dins d’aquest treball es proposa un marc metodològic que s’ha utilitzat per estudiar dades SCADA d’alta freqüència. Això va portar a la conclusió que els períodes d’agregació típics, de 5 a 10 minuts que són l’estàndard a la indústria de l’energia eòlica, no són capaços de capturar i mantenir el contingut d’informació de senyals que canvien ràpidament, com ara mesures eòliques i elèctriquesPostprint (published version

Optimized solutions for smart microgrids

The share of distributed energy generation is growing at a rapid pace. The dropping cost of photovoltaic panels and Governments’ incentives are making more and more convenient the installation of photovoltaic panels for privates all around the world. In this thesis, data from 18 houses in the Netherlands is collected and analyzed to verify the effect of a large concentration of photovoltaic energy generation on the distribution grid. The study reveals that during Spring and Summer problems for the grid may arise due to the large amount of current injected into the grid. Distributed storage, through the installation of batteries, and load shifting are simulated to test their effectiveness in the reduction of the over-injection problem. The results of the physical model are then studied from the economic perspective to verify which option is the most profitable. Finally, different machine learning algorithms are implemented to predict the load consumption and photovoltaic energy generation one-day ahead

Improving the Performance of Cryogenic Calorimeters with Nonlinear Multivariate Noise Cancellation Algorithms

State-of-the-art physics experiments require high-resolution, low-noise, and low-threshold detectors to achieve competitive scientific results. However, experimental environments invariably introduce sources of noise, such as electrical interference or microphonics. The sources of this environmental noise can often be monitored by adding specially designed "auxiliary devices" (e.g. microphones, accelerometers, seismometers, magnetometers, and antennae). A model can then be constructed to predict the detector noise based on the auxiliary device information, which can then be subtracted from the true detector signal. Here, we present a multivariate noise cancellation algorithm which can be used in a variety of settings to improve the performance of detectors using multiple auxiliary devices. To validate this approach, we apply it to simulated data to remove noise due to electromagnetic interference and microphonic vibrations. We then employ the algorithm to a cryogenic light detector in the laboratory and show an improvement in the detector performance. Finally, we motivate the use of nonlinear terms to better model vibrational contributions to the noise in thermal detectors. We show a further improvement in the performance of a particular channel of the CUORE detector when using the nonlinear algorithm in combination with optimal filtering techniques.Comment: 21 pages, 15 figures, 7 table

Can early-onset acquired demyelinating syndrome (ADS) hide pediatric Behcet's disease? A case report

Behcet's disease (BD) is a rare vasculitis characterized by multisystemic inflammation. Central nervous system (CNS) involvement is rare and heterogeneous, particularly in the pediatric population. A diagnosis of neuro-Behcet could be highly challenging, especially if neurological manifestations precede other systemic features; however, its timely definition is crucial to prevent long-term sequelae. In this study, we describe the case of a girl who, at 13 months of age, presented with a first episode of encephalopathy compatible with acute disseminated encephalomyelitis, followed, after 6 months, by a neurological relapse characterized by ophthalmoparesis and gait ataxia, in association with new inflammatory lesions in the brain and spinal cord, suggesting a neuromyelitis optica spectrum disorder. The neurological manifestations were successfully treated with high-dose steroids and intravenous immunoglobulins. In the following months, the patient developed a multisystemic involvement suggestive of Behcet's disease, characterized by polyarthritis and uveitis, associated with HLA-B51 positivity. The challenge presented by this unique case required a multidisciplinary approach involving pediatric neurologists, neuro-radiologists, and pediatric rheumatologists, with all of these specialists creating awareness about early-onset acquired demyelinating syndromes (ADSs). Given the rarity of this presentation, we performed a review of the literature focusing on neurological manifestations in BD and differential diagnosis of patients with early-onset ADS

The Genopolis Microarray Database

<p>Abstract</p> <p>Background</p> <p>Gene expression databases are key resources for microarray data management and analysis and the importance of a proper annotation of their content is well understood.</p> <p>Public repositories as well as microarray database systems that can be implemented by single laboratories exist. However, there is not yet a tool that can easily support a collaborative environment where different users with different rights of access to data can interact to define a common highly coherent content. The scope of the Genopolis database is to provide a resource that allows different groups performing microarray experiments related to a common subject to create a common coherent knowledge base and to analyse it. The Genopolis database has been implemented as a dedicated system for the scientific community studying dendritic and macrophage cells functions and host-parasite interactions.</p> <p>Results</p> <p>The Genopolis Database system allows the community to build an object based MIAME compliant annotation of their experiments and to store images, raw and processed data from the Affymetrix GeneChip^®platform. It supports dynamical definition of controlled vocabularies and provides automated and supervised steps to control the coherence of data and annotations. It allows a precise control of the visibility of the database content to different sub groups in the community and facilitates exports of its content to public repositories. It provides an interactive users interface for data analysis: this allows users to visualize data matrices based on functional lists and sample characterization, and to navigate to other data matrices defined by similarity of expression values as well as functional characterizations of genes involved. A collaborative environment is also provided for the definition and sharing of functional annotation by users.</p> <p>Conclusion</p> <p>The Genopolis Database supports a community in building a common coherent knowledge base and analyse it. This fills a gap between a local database and a public repository, where the development of a common coherent annotation is important. In its current implementation, it provides a uniform coherently annotated dataset on dendritic cells and macrophage differentiation.</p

Occurrence and Effects on Weaning From Mechanical Ventilation of Intensive Care Unit Acquired and Diaphragm Weakness: A Pilot Study

PurposeLimb intensive care unit (ICU)-acquired weakness (ICUAW) and ICU acquired diaphragm weakness (DW) occur frequently in mechanically ventilated (MV) patients; their coexistence in cooperative and uncooperative patients is unknown. This study was designed to (1) describe the co-occurrence of the two conditions (2) evaluate the impact of ICUAW and DW on the ventilator-free days (VFDs) at 28 days and weaning success, and (3) assess the correlation between maximal inspiratory pressure (MIP) and thickening fraction (TFdi) in patients with DW.MethodsThis prospective pilot study was conducted in a single-center on 73 critically ill MV patients. Muscle weakness was defined as a Medical Research Council score &lt; 48 in cooperative patients or a bilateral mean simplified peroneal nerve test &lt; 5.26 mV in uncooperative patients. Diaphragm dysfunction was defined as MIP &lt; 30 cm H2O or as a TFdi &lt; 29%. Weaning success was defined according to weaning according to a new definition (WIND).ResultsFifty-seven patients (78%) had ICUAW and 59 (81%) had DW. The coexistence of the two conditions occurred in 48 patients (65%), without association (χ2 = 1.06, p = 0.304). In the adjusted analysis, ICUAW was independently related to VFDs at 28-days (estimate difference 6 days, p = 0.016), and WIND (OR of 3.62 for having WIND different than short weaning), whereas DW was not. The linear mixed model showed a significant but weak correlation between MIP and TFdi (p &lt; 0.001).ConclusionThis pilot study is the first to explore the coexistence of ICUAW and DW in both cooperative and uncooperative patients; a lack of association was found between DW and ICUAW when considering both cooperative and uncooperative patients. We found a strong correlation between ICUAW but not DW with the VFDs at 28 days and weaning success. A future larger study is warranted in order to confirm our results, and should also investigate the use of transdiaphragmatic twitch pressure measurement during bilateral anterior magnetic phrenic nerve stimulation for the diagnosis of DW

COVID-19-associated Guillain-Barré syndrome in the early pandemic experience in Lombardia (Italy)

Objective To estimate the incidence and describe clinical characteristics and outcome of GBS in COVID-19 patients (COVID19-GBS) in one of the most hit regions during the frst pandemic wave, Lombardia. Methods Adult patients admitted to 20 Neurological Units between 1/3–30/4/2020 with COVID19-GBS were included as part of a multi-center study organized by the Italian society of Hospital Neuroscience (SNO). Results Thirty-eight COVID19-GBS patients had a mean age of 60.7 years and male frequency of 86.8%. CSF albuminocytological dissociation was detected in 71.4%, and PCR for SARS-CoV-2 was negative in 19 tested patients. Based on neurophysiology, 81.8% of patients had a diagnosis of AIDP, 12.1% of AMSAN, and 6.1% of AMAN. The course was favorable in 76.3% of patients, stable in 10.5%, while 13.2% worsened, of which 3 died. The estimated occurrence rate in Lombardia ranges from 0.5 to 0.05 GBS cases per 1000 COVID-19 infections depending on whether you consider positive cases or estimated seropositive cases. When we compared GBS cases with the pre-pandemic period, we found a reduction of cases from 165 to 135 cases in the 2-month study period in Lombardia. Conclusions We detected an increased incidence of GBS in COVID-19 patients which can refect a higher risk of GBS in COVID-19 patients and a reduction of GBS events during the pandemic period possibly due to a lower spread of more common respiratory infectious diseases determined by an increased use of preventive measures

Jet energy measurement with the ATLAS detector in proton-proton collisions at root s=7 TeV

The jet energy scale and its systematic uncertainty are determined for jets measured with the ATLAS detector at the LHC in proton-proton collision data at a centre-of-mass energy of √s = 7TeV corresponding to an integrated luminosity of 38 pb-1. Jets are reconstructed with the anti-kt algorithm with distance parameters R=0. 4 or R=0. 6. Jet energy and angle corrections are determined from Monte Carlo simulations to calibrate jets with transverse momenta pT≥20 GeV and pseudorapidities {pipe}η{pipe}<4. 5. The jet energy systematic uncertainty is estimated using the single isolated hadron response measured in situ and in test-beams, exploiting the transverse momentum balance between central and forward jets in events with dijet topologies and studying systematic variations in Monte Carlo simulations. The jet energy uncertainty is less than 2. 5 % in the central calorimeter region ({pipe}η{pipe}<0. 8) for jets with 60≤pT<800 GeV, and is maximally 14 % for pT<30 GeV in the most forward region 3. 2≤{pipe}η{pipe}<4. 5. The jet energy is validated for jet transverse momenta up to 1 TeV to the level of a few percent using several in situ techniques by comparing a well-known reference such as the recoiling photon pT, the sum of the transverse momenta of tracks associated to the jet, or a system of low-pT jets recoiling against a high-pT jet. More sophisticated jet calibration schemes are presented based on calorimeter cell energy density weighting or hadronic properties of jets, aiming for an improved jet energy resolution and a reduced flavour dependence of the jet response. The systematic uncertainty of the jet energy determined from a combination of in situ techniques is consistent with the one derived from single hadron response measurements over a wide kinematic range. The nominal corrections and uncertainties are derived for isolated jets in an inclusive sample of high-pT jets. Special cases such as event topologies with close-by jets, or selections of samples with an enhanced content of jets originating from light quarks, heavy quarks or gluons are also discussed and the corresponding uncertainties are determined. © 2013 CERN for the benefit of the ATLAS collaboration

Search for displaced vertices arising from decays of new heavy particles in 7 TeV pp collisions at ATLAS

We present the results of a search for new, heavy particles that decay at a significant distance from their production point into a final state containing charged hadrons in association with a high-momentum muon. The search is conducted in a pp-collision data sample with a center-of-mass energy of 7 TeV and an integrated luminosity of 33 pb^-1 collected in 2010 by the ATLAS detector operating at the Large Hadron Collider. Production of such particles is expected in various scenarios of physics beyond the standard model. We observe no signal and place limits on the production cross-section of supersymmetric particles in an R-parity-violating scenario as a function of the neutralino lifetime. Limits are presented for different squark and neutralino masses, enabling extension of the limits to a variety of other models.Comment: 8 pages plus author list (20 pages total), 8 figures, 1 table, final version to appear in Physics Letters