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The increased penetration of renewable energy sources (RESs) as an effective mechanism to reduce carbon emissions leads to an increased weather dependency for power and energy systems. This has created dynamic operation and degradation phenomena, which affect the lifetime estimation of the assets operated with RESs. For the reliable and efficient operation of RES it is crucial to monitor the health of its constituent components and feature selection is a crucial step for building robust and accurate health monitoring approaches. In this context, this paper presents a probabilistic feature selection approach, which probabilistically weights and selects features through a heuristic and iterative process for an improved asset lifetime estimation. Power transformers are key power grid assets and they are used to demonstrate the validity and impact of the proposed approach. The approach is tested on two different photovoltaic power plants operated in Spain and Australia. Results consistently show that the proposed feature-selection approach reduces the prediction error and consistently selects relevant features. The approach has been applied to transformer lifetime estimation, but it can be generally applied to assist in the lifetime estimation of other components operated in RESs. Part of the studies presented here as well as source codes are all open-source under the GitHub repository https://github.com/iramirezg/FeatureSelection. • Probabilistic feature selection approach for improved asset lifetime estimation. • Integration of environmental features for improved renewable-operated asset lifetime. • Systematic and robust feature weighting methodology. • Improved transformer lifetime estimation including sensor and environmental data. • Validated on two real photovoltaic power plant case studies. [ABSTRACT FROM AUTHOR]

This paper presents the results of the influence of frequency on the permanent deformation and degradation behavior of ballast during cyclic loading. The behavior of ballast under numerous cycles was investigated through a series of large-scale cyclic triaxial tests. The tests were conducted at frequencies ranging from 10–40 Hz, which is equivalent to a train traveling from 73 km/h to 291 km/h over standard gauge tracks in Australia. The results showed that permanent deformation and degradation of ballast increased with the frequency of loading and number of cycles. Much of breakage occurs during the initial cycle; however, there exists a frequency zone of 20 Hz<=f<=30 Hz where cyclic densification takes place without much additional breakage. An empirical relationship among axial strain, frequency and number of cycles has been proposed based on the experimental data. In addition, discrete-element method (DEM) simulations were carried out using PFC2D on an assembly of irregular shaped particles. A novel approach was used to model a two-dimensional (2D) projection of real ballast particles. Clusters of bonded circular particles were used to model a 2D projection of angular ballast particles. Degradation of the bonds within a cluster was considered to represent particle breakage. The results of DEM simulations captured the ballast behavior under cyclic loading in accordance with the experimental observations. Moreover, the evolution of micromechanical parameters such as a distribution of the contact force and bond force developed during cyclic loading was presented to explain the mechanism of particle breakage. It has been revealed that particle breakage is mainly due to the tensile stress developed during cyclic loading and is located mainly in the direction of the movement of ballast particles. [ABSTRACT FROM AUTHOR]

Abstract: There is a growing requirement for techniques to assess land susceptibility to wind erosion, i.e. land erodibility, over large geographic areas (>104 km2). This requirement stems from a lack of wind erosion research between the field (101 km2) and regional scales, and a need to evaluate the performance of spatially explicit wind erosion models across these scales. This paper addresses this issue by presenting a methodology for monitoring land erodibility at the landscape scale (103 km2). First, we define criteria suitable for evaluating land erodibility based on empirical relationships between soil texture, vegetation cover, geomorphology, and wind erosion. The criteria were used to visually assess land erodibility over long distances (103 km) using vehicle-based transects run through the rangelands of western Queensland, Australia. Application of the data for testing the performance of a spatially explicit land erodibility model (AUSLEM) is then demonstrated by comparing the visual assessments of land erodibility with the model output. The model performed best in the west of the study area in the open rangelands. In regions with higher woody shrub and tree cover the model performance decreases. This highlights the need for research to better parameterise controls on erodibility in semi-arid landscapes consisting of forested and rangeland mosaics. [Copyright &y& Elsevier]