Frequency-Domain Identification of Radiation Forces for Floating Wind Turbines by Moment-Matching

The dynamics of a floating structure can be expressed in terms of Cummins’ equation, which is an integro-differential equation of the convolution class. In particular, this convolution operator accounts for radiation forces acting on the structure. Considering that the mere existence of this operator is highly inconvenient due to its excessive computational cost, it is commonly replaced by an approximating parametric model. Recently, the Finite Order Approximation by Moment-Matching (FOAMM) toolbox has been developed within the wave energy literature, allowing for an efficient parameterisation of this radiation force convolution term, in terms of a state-space representation. Unlike other parameterisation strategies, FOAMM is based on an interpolation approach, where the user can select a set of interpolation frequencies where the steady-state response of the obtained parametric representation exactly matches the behaviour of the target system. This paper illustrates the application of FOAMM to a UMaine semi-submersible-like floating structure.

An Overview of Variational Autoencoders for Source Separation, Finance, and Bio-Signal Applications

Autoencoders are a self-supervised learning system where, during training, the output is an approximation of the input. Typically, autoencoders have three parts: Encoder (which produces a compressed latent space representation of the input data), the Latent Space (which retains the knowledge in the input data with reduced dimensionality but preserves maximum information) and the Decoder (which reconstructs the input data from the compressed latent space). Autoencoders have found wide applications in dimensionality reduction, object detection, image classification, and image denoising applications. Variational Autoencoders (VAEs) can be regarded as enhanced Autoencoders where a Bayesian approach is used to learn the probability distribution of the input data. VAEs have found wide applications in generating data for speech, images, and text. In this paper, we present a general comprehensive overview of variational autoencoders. We discuss problems with the VAEs and present several variants of the VAEs that attempt to provide solutions to the problems. We present applications of variational autoencoders for finance (a new and emerging field of application), speech/audio source separation, and biosignal applications. Experimental results are presented for an example of speech source separation to illustrate the powerful application of variants of VAE: VAE, β-VAE, and ITL-AE. We conclude the paper with a summary, and we identify possible areas of research in improving performance of VAEs in particular and deep generative models in general, of which VAEs and generative adversarial networks (GANs) are examples.

System Modeling and Reliability Assessment of Microgrids: A Review

The world today is plagued with problems of increased transmission and distribution (T&D) losses leading to poor reliability due to power outages and an increase in the expenditure on electrical infrastructure. To address these concerns, technology has evolved to enable the integration of renewable energy sources (RESs) like solar, wind, diesel and biomass energy into small scale self-governing power system zones which are known as micro-grids (MGs). A de-centralised approach for modern power grid systems has led to an increased focus on distributed energy resources and demand response. MGs act as complete power system units albeit on a small scale. However, this does not prevent them from large operational sophistication allowing their independent functioning in both grid-connected and stand-alone modes. MGs provide greater reliability as compared to the entire system owing to the large amount of information secured from the bulk system. They comprise numerous sources like solar, wind, diesel along with storage devices and converters. Several modeling schemes have been devised to reduce the handling burden of large scale systems. This paper gives a detailed review of MGs and their architecture, state space representation of wind energy conversion systems & solar photovoltaic (PV) systems, operating modes and power management in a MG and its impact on a distribution network.

A practical guide for generating unsupervised, spectrogram-based latent space representations of animal vocalizations

The manual detection, analysis, and classification of animal vocalizations in acoustic recordings is laborious and requires expert knowledge. Hence, there is a need for objective, generalizable methods that detect underlying patterns in these data, categorize sounds into distinct groups, and quantify similarities between them. Among all computational methods that have been proposed to accomplish this, neighborhood-based dimensionality reduction of spectrograms to produce a latent-space representation of calls stands out for its conceptual simplicity and effectiveness. Using a dataset of manually annotated meerkat (Suricata suricatta) vocalizations, we demonstrate how this method can be used to obtain meaningful latent space representations that reflect the established taxonomy of call types. We analyze strengths and weaknesses of the proposed approach, give recommendations for its usage and show application examples, such as the classification of ambiguous calls and the detection of mislabeled calls. All analyses are accompanied by example code to help researchers realize the potential of this method for the study of animal vocalizations.

Evaluation and space representation of the maximum surface runoff in watersheds

In Cuba, regardless of the advances made in hydrological and hydraulic investigations related to floods, there are limitations with the detailed knowledge of the true maximum surface runoff or maximum flow that characterizes these phenomena in the season of intense rains, for which it is necessary to carry out complex hydrological study that, with the help of professional software and statistical techniques, help to determine and model spatially with certain reliability, the maximum water surface drained in watersheds. The general objective was pursued: To determine and represent spatially with the use of Geographic Information Systems (GIS) and hydrological methods, the runoff or maximum flow produced by the intense rains in a watershed, selecting to exemplify the watershed from the Magdalena River to the south east of the Santiago de Cuba municipality. As a result was obtained in the first instance, that it is feasible to apply this procedure to know in a preliminary way what maximum flow is available at any point of a main river or tributary. This approximation constitutes a significant advance for subsequent work in other watersheds of Cuba or internationally.

On Parametrizations of State Feedbacks and Static Output Feedbacks and Their Applications

In this chapter, we provide an explicit free parametrization of all the stabilizing static state feedbacks for continuous-time Linear-Time-Invariant (LTI) systems, which are given in their state-space representation. The parametrization of the set of all the stabilizing static output feedbacks is next derived by imposing a linear constraint on the stabilizing static state feedbacks of a related system. The parametrizations are utilized for optimal control problems and for pole-placement and exact pole-assignment problems.

Closed-Loop Control of a New High Step-Up Multi-Input Multi-Output DC-DC Converter

A new multi-input multi-output dc-dc converter with high step-up capability for wide power ranges is proposed in this paper. The converter's number of inputs and outputs is arbitrary and independent of each other. The proposed topology combines the benefits of DC-DC boost and switched-capacitor converters. The number of input, output, and voltage multiplier stages is arbitrary and depends on the design conditions. First, the various operating modes of the proposed converter are discussed. The closed-loop control system also must be designed using state space representation and small-signal modelling. Finally, the operation of the proposed converter is derived from the simulation results. Keywords: High power converter, Low voltage stress, Multi-Input Multi-Output (MIMO) converter, Non-isolated high step-up dc-dc converter, closed loop control.