The role of smart sensor networks for voltage monitoring in smart grids

The large-scale deployment of the Smart Grid paradigm will support the evolution of conventional electrical power systems toward active, flexible and self-healing web energy networks composed of distributed and cooperative energy resources. In a Smart Grid platform, distributed voltage monitoring is one of the main issues to address. In this field, the application of traditional hierarchical monitoring paradigms has some disadvantages that could hinder their application in Smart Grids where the constant growth of grid complexity and the need for massive pervasion of Distribution Generation Systems (DGS) require more scalable, more flexible control and regulation paradigms. To try to overcome these challenges, this paper proposes the concept of a decentralized non-hierarchal voltage monitoring architecture based on intelligent and cooperative smart entities. These devices employ traditional sensors to acquire local bus variables and mutually coupled oscillators to assess the main variables describing the global grid state

Memory and information processing in neuromorphic systems

A striking difference between brain-inspired neuromorphic processors and current von Neumann processors architectures is the way in which memory and processing is organized. As Information and Communication Technologies continue to address the need for increased computational power through the increase of cores within a digital processor, neuromorphic engineers and scientists can complement this need by building processor architectures where memory is distributed with the processing. In this paper we present a survey of brain-inspired processor architectures that support models of cortical networks and deep neural networks. These architectures range from serial clocked implementations of multi-neuron systems to massively parallel asynchronous ones and from purely digital systems to mixed analog/digital systems which implement more biological-like models of neurons and synapses together with a suite of adaptation and learning mechanisms analogous to the ones found in biological nervous systems. We describe the advantages of the different approaches being pursued and present the challenges that need to be addressed for building artificial neural processing systems that can display the richness of behaviors seen in biological systems.Comment: Submitted to Proceedings of IEEE, review of recently proposed neuromorphic computing platforms and system

Hydrodynamics of Biomimetic Marine Propulsion and Trends in Computational Simulations

[Abstract] The aim of the present paper is to provide the state of the works in the field of hydrodynamics and computational simulations to analyze biomimetic marine propulsors. Over the last years, many researchers postulated that some fish movements are more efficient and maneuverable than traditional rotary propellers, and the most relevant marine propulsors which mimic fishes are shown in the present work. Taking into account the complexity and cost of some experimental setups, numerical models offer an efficient, cheap, and fast alternative tool to analyze biomimetic marine propulsors. Besides, numerical models provide information that cannot be obtained using experimental techniques. Since the literature about trends in computational simulations is still scarce, this paper also recalls the hydrodynamics of the swimming modes occurring in fish and summarizes the more relevant lines of investigation of computational models

Biologically Inspired Energy Manager Design For The Greatt Residential Microgrid

A biologically inspired method, involving the design of an energy manager, for coordinating the operation of a hybrid renewable residential micro-grid is presented. Flexible optimization procedures that minimize the cost of renewable distribution generators based upon the climate and location of the load profile have been developed and modeled in simulation. A novel design of a dual channel converter system and its control system forms the distributed energy storage (DES) system that features the capability of balancing the power flow in the micro-grid (even in the grid-off mode). The proposed energy management system utilizes a back propagation neural network in order to predict the state of charge (SOC) of the DES, yielding the reference value of control variables, which allows the micro-grid to respond to the desired operation conditions rapidly fast with acceptable controller error. Preliminary results indicate that the DES system allows for the implementation of energy management strategies in a technically viable manner

Proceedings of Abstracts Engineering and Computer Science Research Conference 2019

© 2019 The Author(s). This is an open-access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. For further details please see https://creativecommons.org/licenses/by/4.0/. Note: Keynote: Fluorescence visualisation to evaluate effectiveness of personal protective equipment for infection control is © 2019 Crown copyright and so is licensed under the Open Government Licence v3.0. Under this licence users are permitted to copy, publish, distribute and transmit the Information; adapt the Information; exploit the Information commercially and non-commercially for example, by combining it with other Information, or by including it in your own product or application. Where you do any of the above you must acknowledge the source of the Information in your product or application by including or linking to any attribution statement specified by the Information Provider(s) and, where possible, provide a link to this licence: http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/This book is the record of abstracts submitted and accepted for presentation at the Inaugural Engineering and Computer Science Research Conference held 17th April 2019 at the University of Hertfordshire, Hatfield, UK. This conference is a local event aiming at bringing together the research students, staff and eminent external guests to celebrate Engineering and Computer Science Research at the University of Hertfordshire. The ECS Research Conference aims to showcase the broad landscape of research taking place in the School of Engineering and Computer Science. The 2019 conference was articulated around three topical cross-disciplinary themes: Make and Preserve the Future; Connect the People and Cities; and Protect and Care

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Artificial piezoelectric grass for energy harvesting from turbulence-induced vibration

The primary objective of this research is to develop a deploy-and-forget energy harvesting device for use in low-velocity, highly turbulent fluid flow environments i.e. streams or ventilation systems. The work presented here focuses on a novel, lightweight, highly robust, energy harvester design referred to as piezoelectric grass . This biologically inspired design consists of an array of cantilevers, each constructed with piezoelectric material. When exposed to proper turbulent flow conditions, these cantilevers experience vigorous vibrations. Preliminary results have shown that a small array of piezoelectric grass was able to produce up to 1.0 mW per cantilever in high-intensity turbulent flow having a mean velocity of 11.5 m s −1 . According to the literature, this is among the highest output achieved using similar harvesting methods. A distributed parameter model for energy harvesting from turbulence-induced vibration will be introduced and experimentally validated. This model is generalized for the case of a single cantilever in turbulent cross-flow. Two high-sensitivity pressure probes were needed to perform spectral measurements within various turbulent flows. The design and performance of these probes along with calibration and measurement techniques will be discussed.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/98616/1/0964-1726_21_10_105024.pd