Optimal Phase Swapping in Low Voltage Distribution Networks Based on Smart Meter Data and Optimization Heuristics

In this paper a modified version of the Harmony Search algorithm is proposed as a novel tool for phase swapping in Low Voltage Distribution Networks where the objective is to determine to which phase each load should be connected in order to reduce the unbalance when all phases are added into the neutral conductor. Unbalanced loads deteriorate power quality and increase costs of investment and operation. A correct assignment is a direct, effective alternative to prevent voltage peaks and network outages. The main contribution of this paper is the proposal of an optimization model for allocating phases consumers according to their individual consumption in the network of low-voltage distribution considering mono and bi-phase connections using real hourly load patterns, which implies that the computational complexity of the defined combinatorial optimization problem is heavily increased. For this purpose a novel metric function is defined in the proposed scheme. The performance of the HS algorithm has been compared with classical Genetic Algorithm. Presented results show that HS outperforms GA not only on terms of quality but on the convergence rate, reducing the computational complexity of the proposed scheme while provide mono and bi phase connections.This paper includes partial results of the UPGRID project. This project has re- ceived funding from the European Unions Horizon 2020 research and innovation programme under grant agreement No 646.531), for further information check the website: http://upgrid.eu. As well as by the Basque Government through the ELKARTEK programme (BID3A and BID3ABI projects)

Transformations of Participatory Budgeting in Spain: from the implementation of the Porto Alegre model to the instrumentalization of the new experiences

Se presenta una investigación sobre la evolución del Presupuesto Participativo en España, desde su aparición a principios del siglo XXI hasta la actualidad. El Presupuesto Participativo es una metodología de gestión pública y democracia participativa en la que la ciudadanía, en un espacio de diálogo y deliberación, decide sobre una parte del presupuesto municipal. El estudio parte de los modelos propuestos por Ganuza y Francés (2012) y se centra en las experiencias de las “Grandes Ciudades”. Los resultados indican el surgimiento de un nuevo modelo, al que hemos denominado “instrumentalista”, con menos espacios deliberativos y en el que las tecnologías son el mecanismo participativo predominante.We present a study about the evolution of Participatory Budgeting in Spain, from its appearance at the beginning of the 21st century to the present day. Participatory Budgeting is a methodology of public management and participatory democracy in which citizens, in a space of dialogue and deliberation, decide on a part of the municipal budget. The study starts from the models proposed by Ganuza and Francés (2012) and focuses on the experiences of the “Big Cities”. The results indicate the emergence of a new model, which we have called “instrumentalist”, with fewer deliberative spaces and where technologies are the predominant participatory mechanism

Machine learning based adaptive soft sensor for flash point inference in a refinery realtime process

In industrial control processes, certain characteristics are sometimes difficult to measure by a physical sensor due to technical and/or economic limitations. This fact is especially true in the petrochemical industry. Some of those quantities are especially crucial for operators and process safety. This is the case for the automotive diesel Flash Point Temperature (FT). Traditional methods for FT estimation are based on the study of the empirical inference between flammability properties and the denoted target magnitude. The necessary measures are taken indirectly by samples from the process and analyzing them in the laboratory, this process implies time (can take hours from collection to flash temperature measurement) and thus make it very difficult for real-time monitorization, which in fact results in security and economical losses. This study defines a procedure based on Machine Learning modules that demonstrate the power of real-time monitorization over real data from an important international refinery. As input, easily measured values provided in real-time, such as temperature, pressure, and hydraulic flow are used and a benchmark of different regressive algorithms for FT estimation is presented. The study highlights the importance of sequencing preprocessing techniques for the correct inference of values. The implementation of adaptive learning strategies achieves considerable economic benefits in the productization of this soft sensor. The validity of the method is tested in the reality of a refinery. In addition, real-world industrial data sets tend to be unstable and volatile, and the data is often affected by noise, outliers, irrelevant or unnecessary features, and missing data. This contribution demonstrates with the inclusion of a new concept, called an adaptive soft sensor, the importance of the dynamic adaptation of the conformed schemes based on Machine Learning through their combination with feature selection, dimensional reduction, and signal processing techniques. The economic benefits of applying this soft sensor in the refinery's production plant and presented as potential semi-annual savings.This work has received funding support from the SPRI-Basque Gov- ernment through the ELKARTEK program (OILTWIN project, ref. KK- 2020/00052)

Análisis no destructivo de obras de arte con espectroscopia Raman

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Molecular stratigraphic analysis with Raman spectroscopy of the shell of a mussel

Two different cristallographical forms of calcium carbonate (aragonite and calcite) has been identified in mussels with Raman spectroscopy.Peer ReviewedPostprint (published version

Honey Bee Colonies Remote Monitoring System

Bees are very important for terrestrial ecosystems and, above all, for the subsistence of many crops, due to their ability to pollinate flowers. Currently, the honey bee populations are decreasing due to colony collapse disorder (CCD). The reasons for CCD are not fully known, and as a result, it is essential to obtain all possible information on the environmental conditions surrounding the beehives. On the other hand, it is important to carry out such information gathering as non-intrusively as possible to avoid modifying the bees’ work conditions and to obtain more reliable data. We designed a wireless-sensor networks meet these requirements. We designed a remote monitoring system (called WBee) based on a hierarchical three-level model formed by the wireless node, a local data server, and a cloud data server. WBee is a low-cost, fully scalable, easily deployable system with regard to the number and types of sensors and the number of hives and their geographical distribution. WBee saves the data in each of the levels if there are failures in communication. In addition, the nodes include a backup battery, which allows for further data acquisition and storage in the event of a power outage. Unlike other systems that monitor a single point of a hive, the system we present monitors and stores the temperature and relative humidity of the beehive in three different spots. Additionally, the hive is continuously weighed on a weighing scale. Real-time weight measurement is an innovation in wireless beehive—monitoring systems. We designed an adaptation board to facilitate the connection of the sensors to the node. Through the Internet, researchers and beekeepers can access the cloud data server to find out the condition of their hives in real time

Road Sign Analysis Using Multisensory Data

This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device,all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters.Results show that the described calculation methodology offers a correct estimation for all types of traffic signs

Road Sign Analysis Using Multisensory Data

This paper deals with the problem of estimating the following road sign parameters: height, dimensions, visibility distance and partial occlusions. This work belongs to a framework whose main applications involve road sign maintenance, driver assistance, and inventory systems. From this paper we suggest a multisensory system composed from two cameras, a GPS receiver, and a distance measurement device,all of them installed in a car. The process consists of several steps which include road sign detection, recognition and tracking , and road signs parameters estimation. From some trigonometric properties, and a camera model, the information provided by the tracking subsystem and the distance measurement sensors, we estimate the road signs parameters.Results show that the described calculation methodology offers a correct estimation for all types of traffic signs

NGC 7469 as seen by MEGARA: new results from high-resolution IFU spectroscopy

We present our analysis of high-resolution (R ∼ 20 000) GTC/MEGARA integral-field unit spectroscopic observations, obtained during the commissioning run, in the inner region (12.5 arcsec × 11.3 arcsec) of the active galaxy NGC 7469, at spatial scales of 0.62 arcsec. We explore the kinematics, dynamics, ionization mechanisms, and oxygen abundances of the ionized gas, by modelling the H α-[N II] emission lines at high signal-to-noise (> 15) with multiple Gaussian components. MEGARA observations reveal, for the first time for NGC 7469, the presence of a very thin (20 pc) ionized gas disc supported by rotation (V/σ = 4.3), embedded in a thicker (222 pc), dynamically hotter (V/σ = 1.3) one. These discs nearly corotate with similar peak-to-peak velocities (163 versus 137 km s^(−1)), but with different average velocity dispersion (38 ± 1 versus 108 ± 4 km s^(−1)). The kinematics of both discs could be possibly perturbed by star-forming regions. We interpret the morphology and the kinematics of a third (broader) component (σ > 250 km s^(−1)) as suggestive of the presence of non-rotational turbulent motions possibly associated either to an outflow or to the lense. For the narrow component, the [N II]/H α ratios point to the star-formation as the dominant mechanism of ionization, being consistent with ionization from shocks in the case of the intermediate component. All components have roughly solar metallicity. In the nuclear region of NGC 7469, at r ≤ 1.85 arcsec, a very broad (FWHM = 2590 km s^(−1)) H α component is contributing (41 per cent) to the global H α-[N II] profile, being originated in the (unresolved) broad line region of the Seyfert 1.5 nucleus of NGC 7469

Aplicaciones del láser al análisis y tratamiento de obras de arte

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