Forecasting Wind Turbine Failures and Associated Costs

Electricity demand is rapidly increasing with growth of population, development of technologies and electrically intensive industries. Also, emerging climate change concerns compel governments to seek environmentally friendly ways to produce electricity such as wind energy systems. In 2018, the wind energy reached 600 GW total capacity globally. However, this corresponds to only about 6% of global electricity demand and there is a need to increase wind energy penetration in electricity grids. One way to enhance the competitiveness of wind energy is to improve its reliability and availability and reduce associated maintenance costs. This study utilizes a database entitled “Wind Monitor and Evaluation Program (WMEP)” to investigate, model and improve wind turbine reliability and availability. The WMEP database consists of maintenance data of 575 wind turbines in Germany during 1989-2008. It is unique as it includes details of turbine model and size, affected subsystem and component, cause of failure, date and time of maintenance, location, and energy production from the wind turbines. Additional parameters such as climatic regions, geography number of previous failures and mean annual wind speed are added to the database in this study. In this research, two metrics are considered and developed such as time-to-failure or failure rate and time-to-repair or downtime for reliability and availability, respectively. This study investigated failure causes, effects and criticalities of wind turbine subsystems and components, assessed the risk factors impacting wind turbine reliability, modeled the reliability of wind turbines based on assessed risk factors, and predicted the cost of wind turbine failures under various operational and environmental conditions. A well-established reliability assessment technique - Failure Modes, Effects and Criticality Analysis is applied on the WMEP maintenance data from 109 wind turbines and three different climatic regions to understand the impacts of climate and wind turbine design type on wind turbine reliability and availability. First, climatic region impacts on identical wind turbine failures are investigated, then impacts of wind turbine design type are examined for the same climatic region. Furthermore, we compared the results of this investigation with results from previous FMECA studies which neglected impacts of climatic region and turbine design type in section 5.4. Two-step cluster and survival analyses are used to determine risk factors that affect wind turbine reliability. Six operational and environmental factors are considered for this approach, namely capacity factor (CF), wind turbine design type, number of previous failures (NOPF), geographical location, climatic region and mean annual wind speed (MAWS). Data are classified as frequent (time-to-failure80 days) failures and we identified 615 operations listing all these factor and energy production from 21 wind turbines in the WMEP data base. These factors are examined for their impact on wind turbine reliability and results are compared. In addition, wind turbine reliability is modeled by machine learning methods, namely logistic regression (LR) and artificial neural network (ANN), using the considered 615 operations. The objective of this investigation is to model and predict probability of frequently-failing wind turbines based on wind turbines’ known operational and environmental conditions. The models are evaluated and cross validated with 10-fold cross validation and prediction performances and compared with other algorithms such as k-nearest neighbor and support vector machines. Also, prediction performances of LR and ANN are discussed along with their easiness to interpret and share with others. Lastly, using data from 753 operations, a decision support tool for predicting cost of wind turbine failures is developed. The tool development includes machine learning application for estimating probability of failures in 60 days of operation and time-to-repair probabilities for divisions of 0-8hrs, 8-16hrs, 16-24hrs and more than 1 day based on operational and environmental conditions of wind turbines. Prediction for cost of wind turbine failures for 60 days of operation is calculated using assumed costs from time-to-repair divisions. The decision support tool can be updated by the user’s discretion on the cost of failures. This study provides a better understanding of wind turbine failures by investigating associated risk factors, modeling wind turbine reliability and predicting the future cost of failures by applying state-of-the art reliability and data analysis techniques. Wind energy developers and operators can be guided by this study in improving the reliability of wind turbines. Also, wind energy investors, operators and maintenance service managers can predict the cost of wind turbine failures with the decision support tool provided in this study

Preparation of Silicon-Antimony based Anode Materials for Lithium-Ion Batteries

I n this study, SixSb immiscible composite blend as anode materials have been synthesized using micron-sized silicone and antimony particles in different compositions through chemical reduction-mechanical alloying method CR-MA . The obtained microstructures have been investigated by X-ray diffraction XRD and Scanning Electron Microscopy SEM with Energy Dispersive X-Ray analysis EDX . Spectroscopic characterizations of the composite materials showed that a traditional intermetallic compound could not be achieved. However a novel immiscible composite blend system have been developed. One of the newly prepared composite materials, Si0.65Sb, exhibits an initial capacity of 790 mAh g-1 and a good cyclic stability compared to the pure silicone. The battery performance results of the micron-sized Si0.65Sb blend system have been compared with the commercially used graphite and the nano-sized Si/Sb alloy systems. The cycling stability of the micron-sized Si0.65Sb blend system showed an improvement compared to nano-sized Si/Sb alloy systems. Moreover its specific capacity is slightly higher than the commercial graphite anode material. These results portray the importance of micron sized Si/Sb system in large-scale applications due to its low cost

Machine learning and domain adaptation to monitor yoghurt fermentation using ultrasonic measurements

In manufacturing environments, real-time monitoring of yoghurt fermentation is required to maintain an optimal production schedule, ensure product quality, and prevent the growth of pathogenic bacteria. Ultrasonic sensors combined with machine learning models offer the potential for non-invasive process monitoring. However, methods are required to ensure the models are robust to changing ultrasonic measurement distributions as a result of changing process conditions. As it is unknown when these changes in distribution will occur, domain adaptation methods are needed that can be applied to newly acquired data in real-time. In this work, yoghurt fermentation processes are monitored using non-invasive ultrasonic sensors. Furthermore, a transmission based method is compared to an industrially-relevant non-transmission method which does not require the sound wave to travel through the fermenting yoghurt. Three machine learning algorithms were investigated including fully-connected neural networks, fully-connected neural networks with long short-term memory layers, and convolutional neural networks with long short-term memory layers. Three real-time domain adaptation strategies were also evaluated, namely; feature alignment, prediction alignment, and feature removal. The most accurate method (mean squared error of 0.008 to predict pH during fermentation) was non-transmission based and used convolutional neural networks with long short-term memory layers, and a combination of all three domain adaption methods

Some fixed point theorems for generalized contractive mappings in complete metric spaces

We introduce new concepts of generalized contractive and generalized alpha-Suzuki type contractive mappings. Then, we obtain sufficient conditions for the existence of a fixed point of these classes of mappings on complete metric spaces and b-complete b-metric spaces. Our results extend the theorems of Ciric, Chatterjea, Kannan and Reich

Full-endoscopic removal of third ventricular colloid cysts: technique, results, and limitations

IntroductionColloid cysts (CCs) are rare benign lesions that usually arise from the roof of the third ventricle. They may present with obstructive hydrocephalus and cause sudden death. Treatment options include ventriculoperitoneal shunting, cyst aspiration, and cyst resection microscopically or endoscopically. This study aims to report and discuss the full-endoscopic technique for removing colloid cysts.Materials and methodsA 25°-angled neuroendoscope with an internal working channel diameter of 3.1 mm and a length of 122 mm is used. The authors described the technique of resecting a colloid cyst by a full-endoscopic procedure and evaluated the surgical, clinical, and radiological results.ResultsTwenty-one consecutive patients underwent an operation with a transfrontal full-endoscopic approach. The swiveling technique (grasping the cyst wall and rotational movements) was used for CC resection. Of these patients, 11 were female, and ten were male (mean age, 41 years). The most frequent initial symptom was a headache. The mean cyst diameter was 13.9 mm. Thirteen patients had hydrocephalus at admission, and one needed shunting after cyst resection. Seventeen patients (81%) underwent total resection; 3 (14%), subtotal resection; and 1 (5%), partial resection. There was no mortality; one patient had permanent hemiplegia, and one had meningitis. The mean follow-up period was 14 months.ConclusionEven though microscopic resection of cysts has been widely used as a gold standard, successful endoscopic removal has been described recently with lower complication rates. Applying angled endoscopy with different techniques is essential for total resection. Our study is the first case series to show the outcomes of the swiveling technique with low recurrence and complication rates

Measurement of the top quark forward-backward production asymmetry and the anomalous chromoelectric and chromomagnetic moments in pp collisions at √s = 13 TeV

Abstract The parton-level top quark (t) forward-backward asymmetry and the anomalous chromoelectric (d̂ t) and chromomagnetic (μ̂ t) moments have been measured using LHC pp collisions at a center-of-mass energy of 13 TeV, collected in the CMS detector in a data sample corresponding to an integrated luminosity of 35.9 fb−1. The linearized variable AFB(1) is used to approximate the asymmetry. Candidate t t ¯ events decaying to a muon or electron and jets in final states with low and high Lorentz boosts are selected and reconstructed using a fit of the kinematic distributions of the decay products to those expected for t t ¯ final states. The values found for the parameters are AFB(1)=0.048−0.087+0.095(stat)−0.029+0.020(syst),μ̂t=−0.024−0.009+0.013(stat)−0.011+0.016(syst), and a limit is placed on the magnitude of | d̂ t| < 0.03 at 95% confidence level. [Figure not available: see fulltext.

Measurement of t(t)over-bar normalised multi-differential cross sections in pp collisions at root s=13 TeV, and simultaneous determination of the strong coupling strength, top quark pole mass, and parton distribution functions

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