Evaluation of a Local Fault Detection Algorithm for HVDC Systems

A great increase in the amount of energy generated from clean and renewable sources integrated in the electric power system is expected worldwide in the coming years. High Voltage Direct Current (HVDC) systems are seen as a promising alternative to the traditional Alternating Current (AC) systems for the expansion of the electric power system. However, to achieve this vision, there are some remaining challenges regarding HVDC systems which need to be solved. One of the main challenges is related to fault detection and location in HVDC grids. This paper reviews the main protection algorithms available and presents the evaluation of a local fault detection algorithm for DC faults in a multi-terminal Voltage Source Conversion (VSC) based HVDC grid. The paper analyses the influence of the DC voltage sampling frequency and the cable length in the performance of the algorithm. © 2019, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ).The authors thank the support from the Spanish Ministry of Economy, Industry and Competitiveness (project ENE2016-79145-R AEI/FEDER, UE) and GISEL research group IT1083-16), as well as from the University of the Basque Country UPV/EHU (research group funding PPG17/23)

LONG-REMI : an AI-Based Technological Application to Promote Healthy Mental Longevity Grounded in Reminiscence Therapy

Altres ajuts: Fundación General CSIC (0551_PSL_6_E POCTEP)Reminiscence therapy (RT) consists of thinking about one's own experiences through the presentation of memory-facilitating stimuli, and it has as its fundamental axis the activation of emotions. An innovative way of offering RT involves the use of technology-assisted applications, which must also satisfy the needs of the user. This study aimed to develop an AI-based computer application that recreates RT in a personalized way, meeting the characteristics of RT guided by a therapist or a caregiver. The material guiding RT focuses on intangible cultural heritage. The application incorporates facial expression analysis and reinforcement learning techniques, with the aim of identifying the user's emotions and, with them, guiding the computer system that emulates RT dynamically and in real time. A pilot study was carried out at five senior centers in Barcelona and Portugal. The results obtained are very positive, showing high user satisfaction. Moreover, the results indicate that the high frequency of positive emotions increased in the participants at the end of the intervention, while the low frequencies of negative emotions were maintained at the end of the intervention

LONG-REMI: An AI-based technological application to promote healthy mental longevity grounded in reminiscence therapy

Reminiscence therapy (RT) consists of thinking about one’s own experiences through the presentation of memory-facilitating stimuli, and it has as its fundamental axis the activation of emotions. An innovative way of offering RT involves the use of technology-assisted applications, which must also satisfy the needs of the user. This study aimed to develop an AI-based computer application that recreates RT in a personalized way, meeting the characteristics of RT guided by a therapist or a caregiver. The material guiding RT focuses on intangible cultural heritage. The application incorporates facial expression analysis and reinforcement learning techniques, with the aim of identifying the user’s emotions and, with them, guiding the computer system that emulates RT dynamically and in real time. A pilot study was carried out at five senior centers in Barcelona and Portugal. The results obtained are very positive, showing high user satisfaction. Moreover, the results indicate that the high frequency of positive emotions increased in the participants at the end of the intervention, while the low frequencies of negative emotions were maintained at the end of the intervention.This research was supported by the Fundación General CSIC (FGCSIC), the Programa para una Sociedad Longeva (0551_PSL_6_E POCTEP), and the Fondo Europeo de Desarrollo Regional (FEDER).Peer ReviewedPostprint (published version

Grid integration and power quality testing of Marine Energy Converters: Research activities in the MaRINET Project

MaRINET Project funded by the EU through the 7th Framework Programme, aims to accelerate the development of marine renewable energy (wave, tidal, offshore-wind) by bringing together world-class testing facilities at all scales. It involves a total of 29 partners across the European Union and, besides providing free access to the test facilities, the main objectives of the project are to standardize the test procedures, and to innovate for improving test infrastructures and techniques. This paper will present the work carried out within the research activities regarding grid integration of Marine Energy Converters. For this purpose, a collation of European grid codes has been carried out, the impact of such grid codes on the structure and control of the marine farms has been analysed and finally a research on how marine energy devices should be tested to meet the grid integration constraints has been done

Reminiscence program supported by artificial intelligence and based on intangible cultural heritage aimed at the elderly: a pilot study

Introducción: Las aplicaciones tecnológicas son una forma innovadora de brindar la terapia de reminiscencia (TR) y deben satisfacer las necesidades de las personas usuarias. El patrimonio cultural inmaterial (PCI) como tópico para dicha terapia no ha sido todavía explorado. Evaluamos la disponibilidad de una nueva aplicación apoyada en inteligencia artificial (IA) para la TR basada en el PCI dirigida a personas mayores. Material y métodos: Estudio observacional prospectivo realizado con personas de 65 años o más, sin deterioro cognitivo y con deterioro cognitivo leve y moderado que acudían a seis centros de mayores en España y Portugal. Las personas participantes testaron el primer prototipo del programa individualizado LONG-REMI en cuatro sesiones semanales consecutivas. La usabilidad y la satisfacción de la experiencia fueron evaluadas mediante la Escala visual analógica (EVA) al finalizar la intervención. Las emociones se evaluaron mediante la escala de afecto positivo y afecto negativo (PANAS) antes y al finalizar la intervención. Resultados: Se analizaron datos de 56 personas, la usabilidad y la satisfacción fueron altamente percibidas, con puntuaciones de 7,75±1,88 y 8,38±1,57, respectivamente. La subescala de subafecto positivo PANAS mostró cambios significativos (28,86±8,88 antes de la intervención vs. 36,70±9,43 post intervención, Z = -4,18, p = 0,000). No hubo cambios significativos en la subescala de afecto negativo PANAS. Conclusiones: El primer prototipo de la aplicación tecnológica LONG-REMI puede ser usado en personas mayores sin y con deterioro cognitivo. Este puede ser un instrumento potencial para futuras terapias cognitivas con actividades estimulantes y beneficio en las emociones"Trabajo cofinanciado por la Fundación General CSIC como parte del proyecto «Programa para una Sociedad Longeva» (0551 PSL 6 E), perteneciente al Programa Interreg V-A Espan˜a- Portugal (POCTEP) 2014-2020, del Fondo Europeo de Desarrollo Regional (FEDER)"Peer ReviewedPostprint (author's final draft