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AbstractWe devise a methodology to predict failures in wind turbine drive‐train components and quantify its utility. The methodology consists of two main steps. The first step is the set up of a predictive model for shutdown events, which is able to raise an alarm in advance of the fault‐induced shutdown. The model is trained on data for shutdown events retrieved from the alarm log of an offshore wind farm. Here, it is assumed that the timely prediction of low‐severity events, typically caused by abnormal component operation, allows for an intervention that can prevent premature component failures. The prediction models are based on statistical classification using only supervisory control and data acquisition (SCADA) data. In the second step, the shutdown prediction model is combined with a cost model to provide an estimate of the benefits associated with implementing the predictive maintenance system. This is achieved by computing the maximum net utility attainable as a function of the model performance and efficiency of intervention carried out by the user. Results show that the system can be expected to be cost‐effective under specific conditions. A discussion about potential improvements of the approach is provided, along with suggestions for further research in this area.

The presented Solid Fuel Entrained Flow Gasification Database contains complete datasets on individual solid fuels considering the most important physical properties and conversion behavior analysis necessary for the simulation of pressurized high temperature entrained flow gasification. This includes their composition (proximate- and ultimate analysis, XRF of ash at 200, 450, 815 and 1500 °C) as well as physical properties such as original mineral phase composition (XRD), density (true and tap) and particle size distribution. The database also contains data on fuel behavior such as heating values, swelling factors, fragmentation index, slag viscosity, ash melting behavior and ash mineral phase evolution during heat-up and cool-down. Moreover, the database provides model parameters describing their pyrolysis behavior, gasification kinetics including product gas inhibition, thermal deactivation and surface area development. Chars produced and gasified in pressurized high temperature entrained flow reactors like the PiTER (located at the Chair of Energy Systems of the Technical University of Munich) and the KIVAN (operated by the Institute of Energy Process Engineering and Chemical Engineering of the TU Bergakademie Freiberg) were analyzed in thermogravimetric and structural analyzer. Since these reactors are designed for temperatures up to 1800 °C (PiTER) and pressures up to 100 bar (KIVAN), the resulting model parameters are relevant for the simulation of industrial scale applications. In order to validate the applied models for entrained flow gasification kinetics, the database refers to several publications describing the models and experimental setups as well as providing additional experimental data points.

This dataset contains a compilation of noble gas, hydrochemistry, stable water isotopes and radiocarbon from dissolved organic carbon data sampled from geothermal power plants and research wells screened in the deep Upper Jurassic Aquifer in the South German Molasse Basin in Bavaria. This dataset was used to calculate noble gas infiltration temperatures and reconstruct the paleoclimate in Southern Germany during the Late Pleistocene. More details on the methods used to calculate noble gas infiltration temperatures and radiocarbon dating can be found in the associated publication.

We provide a dataset from a household survey in Mpanda region in Western Tanzania (N = 137) that was conducted in 2011. Household heads (or replacements) were interviewed. The topics addressed covered a broad range of socio-economic data and including, among others, household information (number of household members, age, sex, religion etc.), agricultural production (e.g. crops produced and livestock owned) including number and size of plots, income generation, energy access and owned assets.

AbstractState of the art technologies like air-to-water heat pumps have become popular because of their high efficiencies and their supply by electricity rather than by gas or oil. As a direct result of this supply substitution and due to high load- shifting potential, electricity demand of heat pumps have to be taken into account into energy management strategies of the overall building. In this paper a new controller is discussed, which has predictive and adaptive properties and is able to optimize the running-times of non-modulating heat pumps.

Although several studies suggest that tree species in mixed stands resist drought events better than in pure stands, little is known about the impact on growth and the tree water deficit (TWD) in different tree heights at heavy drought. With dendrometer data at the upper and lower stem and coarse roots, we calculated the TWD and growth (ZGmax) (referring to the stem/root basal area) to show (1) the relationship of TWD in different tree heights (50% tree height (H50), breast height (BH), and roots) and the corresponding leaf water potential and (2) how mixture and drought influence the partitioning of growth and tree water. The analyses were made in a mature temperate forest of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica (L.)). Half of the plots were placed under conditions of extreme drought through automatic closing roof systems within the stand. We found a tight relationship of leaf water potentials and TWD at all tree compartments. Through this proven correlation at all tree heights we were also able to study the differences of TWD in all tree compartments next to the growth allocation. Whereas at the beginning of the growing period, trees prioritized growth of the upper stem, during the course of the year the growth of lower stem became a greater priority. Growth allocation of mixed spruces showed a tendency of a higher growth of the roots compared to the BH. However, spruces in interspecific neighborhoods exhibited a lesser TWD in the roots as spruces in intraspecific neighborhood. Beeches in intraspecific neighborhoods showed a higher TWD in BH compared to H50 as beeches in interspecific neighborhoods. Mixture seems to enhance the water supply of spruce trees, which should increase the stability of this species in a time of climatic warming.

The use CFD-DEM for the simulation of dense gas–solid flows comes with limitations based on the numerical cell size depending on the diameter of the particles. With the introduction of a multi-grid approach, i.e., the calculation of the fluid phase and the solid phase are done with two separate meshes, a finer and a coarser one, this limitation can be overcome. In this work, a three-level grid approach is proposed, in which an additional numerical grid is used to transform parameters used for the solid-fluid momentum exchange from the Lagrangian to the Eulerian grid. The resolution of this newly introduced grid lies in between the resolution of the two other meshes. A conventional single- and dual-grid, as well as the newly introduced three-level grid approach were validated with experimental data in literature and afterwards compared in terms of accuracy and simulation speed. A multi-grid approach, dual- or three-level, shows a higher accuracy than the single-grid method. An additional third grid achieves in almost all cases slightly better results than the dual-grid method while the increase in simulation time is negligible. A coarsening of the solid grid has a higher effect on accuracy than the refinement of the fluid grid. Furthermore, the coarse grain method was implemented to increase simulation speed. Still, the three-level grid approach requires a deeper understanding in the resolution of the numerical grid as there are more degrees of freedom for the cell sizes, but an adequate choice of the numerical grid can drastically improve the simulation accuracy and speed in combination with a coarse grain method.

The promotion of electric vehicles (EVs) is an important measure for dealing with climate change and reducing carbon emissions, which are widely agreed goals worldwide. Being an important operating mode for electric vehicle charging stations in the future, the integrated photovoltaic and energy storage charging station (PES-CS) is receiving a fair amount of attention and discussion. However, how to optimally configure photovoltaic and energy storage capacity to achieve the best economy is essential and a huge challenge to overcome. In this paper, based on the historical data-driven search algorithm, the photovoltaic and energy storage capacity allocation method for PES-CS is proposed, which determines the capacity ratio of photovoltaic and energy storage by analyzing the actual operation data, which is performed while considering the target of maximizing economic benefits. In order to achieve the proposed capacity allocation, the method is as follows: First, the economic benefit model of the charging stations is established, taking the net present value and investment payback period as evaluation indicators; then, by analyzing the operation data of the existing charging station with the target of maximizing economic benefits, the initial configuration capacity is obtained; finally, the capacity configuration is verified through a comprehensive case analysis for the actual operation data. The results show that the capacity configuration obtained through the data analysis features an optimized economic efficiency and photovoltaic utilization. The proposed method can provide a theoretical and practical basis for newly planned or improved large-scale charging stations.