Inversion using a new low-dimensional representation of complex binary geological media based on a deep neural network

Efficient and high-fidelity prior sampling and inversion for complex geological media is still a largely unsolved challenge. Here, we use a deep neural network of the variational autoencoder type to construct a parametric low-dimensional base model parameterization of complex binary geological media. For inversion purposes, it has the attractive feature that random draws from an uncorrelated standard normal distribution yield model realizations with spatial characteristics that are in agreement with the training set. In comparison with the most commonly used parametric representations in probabilistic inversion, we find that our dimensionality reduction (DR) approach outperforms principle component analysis (PCA), optimization-PCA (OPCA) and discrete cosine transform (DCT) DR techniques for unconditional geostatistical simulation of a channelized prior model. For the considered examples, important compression ratios (200 - 500) are achieved. Given that the construction of our parameterization requires a training set of several tens of thousands of prior model realizations, our DR approach is more suited for probabilistic (or deterministic) inversion than for unconditional (or point-conditioned) geostatistical simulation. Probabilistic inversions of 2D steady-state and 3D transient hydraulic tomography data are used to demonstrate the DR-based inversion. For the 2D case study, the performance is superior compared to current state-of-the-art multiple-point statistics inversion by sequential geostatistical resampling (SGR). Inversion results for the 3D application are also encouraging

Multilevel Converters: An Enabling Technology for High-Power Applications

| Multilevel converters are considered today as the state-of-the-art power-conversion systems for high-power and power-quality demanding applications. This paper presents a tutorial on this technology, covering the operating principle and the different power circuit topologies, modulation methods, technical issues and industry applications. Special attention is given to established technology already found in industry with more in-depth and self-contained information, while recent advances and state-of-the-art contributions are addressed with useful references. This paper serves as an introduction to the subject for the not-familiarized reader, as well as an update or reference for academics and practicing engineers working in the field of industrial and power electronics.Ministerio de Ciencia y Tecnología DPI2001-3089Ministerio de Eduación y Ciencia d TEC2006-0386

Cost and losses associated with offshore wind farm collection networks which centralise the turbine power electronic converters

Costs and losses have been calculated for several different network topologies, which centralise the turbine power electronic converters, in order to improve access for maintenance. These are divided into star topologies, where each turbine is connected individually to its own converter on a platform housing many converters, and cluster topologies, where multiple turbines are connected through a single large converter. Both AC and DC topologies were considered, along with standard string topologies for comparison. Star and cluster topologies were both found to have higher costs and losses than the string topology. In the case of the star topology, this is due to the longer cable length and higher component count. In the case of the cluster topology, this is due to the reduced energy capture from controlling turbine speeds in clusters rather than individually. DC topologies were generally found to have a lower cost and loss than AC, but the fact that the converters are not commercially available makes this advantage less certain