A new methodology incorporating public participation within Cuba's ICZM program

Although public participation (PP) has influenced some integrated coastal zone management (ICZM) programs around the world, researchers have rarely analyzed this component specifically inside an ICZM cycle. Furthermore, the approach for integrating environmental energy planning tools within the ICZM Programs for the Small Island Developing States (SIDS) has presented an ongoing challenge for specialists involved in management issues. In Cuba, plans for coastal development are supported by land use planning activities and environmental planning tools. However, the functions and outputs of those tools are “non-obvious”, precluding sufficient integration among them. As these aforementioned actions have not been systematically carried out in the Cuban territory, and the systems of inter-institutional relationships with local communities have presented some insufficiencies, the national marine-terrestrial interphase has suffered some negative environmental impacts impossible to be solved by the national authorities. Designing a new methodology that incorporates PP and environmental energy planning tools in the stages of an ICZM program is the objective of this article. The methodology was named MePuPa and has improved current tools for land use planning and ICZM in Cuba. Previously selected “Local Indicators of Environmental Sustainability”, applied in two geo-systemic units in the southeastern region of Cuba, were used to demonstrate the methodology. The qualitative and qualitative methods in the proposed MePuPa were also applied. Finally, the MePuPa methodology was tested for four of its five stages. Six advantages and five learned lessons were identified during the Preparation to Proposal stages. MePuPa resulted in a useful local management tool for environmental energy planning, ICZM, economic and agricultural activities, strategic ecosystems recovery, as well as improvements to the governance and decision-making processes in one SIDS

Speed control in DC and AC drives

Three speed-control strategies for DC and AC drives are presented in this study: a proportional integral derivative (PID) control strategy; an internal model control (IMC); and a state-space control by pole assignment with full state observer (ESSO). The three strategies are applied to a case study, demonstrating the potential of each one. Experimental identification was used to obtain the drive models used for the synthesis of the controllers. The three strategies showed satisfactory results when compared with the requirements imposed on the system, in addition to the good rejection of disturbances. However, the IMC strategy showed itself to be a little softer and with no maximum overshoot, which in some cases and some applications is usually a restriction

Measurement of energy poverty in the Colombian Caribbean region: a comparative analysis

This research work is directed to analyze the level of energy poverty and its consequences on the quality of life of the population of the Colombian Caribbean region, by doing a comparison of the results obtained in that area with data regarding the population of Bogotá, capital of Colombia, and of the rest of the country. The method of meeting absolute energy needs was used to determine the energy poverty index at households (EPH). Results obtained indicate that EPH exceeds 60% in urban areas, and 96% in rural zones, where it was also evidenced a clear link between energy poverty and school dropout

Tools for the implementation of a SCADA system in a desalination process

In this work a system is designed and implemented in SCADA MOVICON 11.5, in which the operation of five seawater desalination lines by reverse osmosis which work in parallel, with four coastal wells and two end-of-line pumps of the permeated water obtainedare integrated, synchronized and supervised as a single plant. Each desalination line has its own control system and can operate independently. As a product, synchronization algorithms were obtained that were added to the system through script codes, which guarantee continuous productivity in the desalination process, achieving synchronization between the mentioned sub-processes. Simultaneous operations of starting, washing and stopping that affected the performance of the osmosis lines are avoided. Alarmsare generated, reports are created, historical records and trends for the decision making on failures prediction, predictive maintenance and troubleshooting

Energy, Economic, and Environmental Evaluation of a Proposed Solar-Wind Power On-Grid System Using HOMER Pro®: A Case Study in Colombia

The electrical sector in the Caribbean region of Colombia is currently facing problems that affect its reliability. Many thermo-electric plants are required to fill the gap and ensure energy supply. This paper thus proposes a hybrid renewable energy generation plant that could supply a percentage of the total energy demand and reduce the environmental impact of conventional energy generation. The hybrid plant works with a photovoltaic (PV) system and wind turbine systems, connected in parallel with the grid to supply a renewable fraction of the total energy demand. The investigation was conducted in three steps: the first stage determined locations where the energy system was able to take advantage of renewable sources, the second identified a location that could work more efficiently from an economic perspective, and finally, the third step estimated the number of PV solar panels and wind turbines required to guarantee optimal functioning for this location using, as a main method of calculation, the software HOMER pro® for hybrid optimization with multiple energy resources. The proposed system is expected to not only limit environmental impacts but also decrease total costs of electric grid consumption from thermoelectric plants. The simulations helped identify Puerto Bolivar, Colombia, as the location where the hybrid plant made the best use of non-conventional resources of energy. However, Rancho Grande was found to offer the system more efficiency, while generating a considerable amount of energy at the lowest possible cost. An optimal combination was also obtained—441 PV arrays and 3 wind turbines, resulting in a net present cost (NPC) of $11.8 million and low CO2 production of 244.1 tons per year

La informática y la gestión integrada de los sistemas de alertas tempranas dentro del manejo integrado de zonas costeras

El Capítulo presenta el desarrollo de un sistema informático capaz de mantener informada de forma eficaz a los tomadores de decisiones, y a la población en general, mediante el uso de un sistema de alertas por amenazas meteorológicas extremas. Para su diseño se realizó una investigación sobre los Sistemas de Alertas Tempranas (SAT) e instituciones de vigilancia de estos eventos en Cuba, así como, sobre la estructura del funcionamiento, descripción conceptual y formas de articulación de las tecnologías de la información. Se demostraron además los vínculos existentes entre los SAT y los programas de Manejo Integrado de Zonas Costeras (MIZC). Como resultado principal del capítulo se desarrolla una herramienta informática web que integra y visualiza múltiples alertas tempranas ante fenómenos climáticos extremos de corta y prolongada duración, contribuyendo a una adecuada gestión en la reducción del riesgo de desastres. Índice 145 Para la Gestión Integrada de Sistemas de Alertas Tempranas (GISAT) se utilizan softwares libres. En el capítulo se describen las tecnologías Web, el vocabulario y soportes necesarios para el desarrollo de dicha herramienta, concluyendo que el mismo permite gestionar, visualizar, y enviar las distintas Alertas Tempranas a las diferentes instituciones municipales, provinciales y nacionales ante casos de eventos extremos. El GISAT, único en el país, se valida en la provincia de Santiago de Cuba. Su implementación potencia el monitoreo de fenómenos de larga duración como pueden ser sequías meteorológicas e hidrológicas, incendios forestales, entre otros fenómenos que conllevan a largos períodos de vigilancia y medidas constantes de adaptación y mitigación

A fuzzy logic proposal for diagnosis multiple incipient faults in a power transformer

For the safety and continuity of service in industrial electrical systems, the availability of transformers is essential. For this reason, it is necessary to develop intelligent fault diagnosis techniques to reduce repair and maintenance costs. Recently, several methods have been developed that use artificial intelligence techniques such as neural networks, support vector machines, hybrid techniques, etc., for the diagnosis of faults in power transformers using gas analysis. These methods, although they present very good results, encounter restrictions to determine the precise moment before the occurrence of multiple fault of small magnitude and are difficult to implement in practice. This document proposes a method to diagnose multiple incipient faults in a power transformer using fuzzy logic. The proposal, based on historical data from the composition of the gases dissolved in the oil, achieves a performance in the classification of multiple incipient fault of 98.3%. With reliable samples of dissolved gas, it guarantees an overall rate of accuracy in detecting incipient faults that is superior to that obtained by the most successful conventional methods in the industry. The proposal does not encounter generalization difficulties and constitutes a simple solution that allows determining the state of the transformer in service without affecting the continuity of the electricity supply

Shallow convolutional network excel for classifying motor imagery EEG in BCI applications

Many studies applying Brain-Computer Interfaces (BCIs) based on Motor Imagery (MI) tasks for rehabilitation have demonstrated the important role of detecting the Event-Related Desynchronization (ERD) to recognize the user’s motor intention. Nowadays, the development of MI-based BCI approaches without or with very few calibration stages session-by-session for different days or weeks is still an open and emergent scope. In this work, a new scheme is proposed by applying Convolutional Neural Networks (CNN) for MI classification, using an end-to-end Shallow architecture that contains two convolutional layers for temporal and spatial feature extraction. We hypothesize that a BCI designed for capturing event-related desynchronization/synchronization (ERD/ERS) at the CNN input, with an adequate network design, may enhance the MI classification with fewer calibration stages. The proposed system using the same architecture was tested on three public datasets through multiple experiments, including both subject-specific and non-subject-specific training. Comparable and also superior results with respect to the state-of-the-art were obtained. On subjects whose EEG data were never used in the training process, our scheme also achieved promising results with respect to existing non-subject-specific BCIs, which shows greater progress in facilitating clinical applications

Monte Carlo dropout for uncertainty estimation and motor imagery classification

Motor Imagery (MI)-based Brain–Computer Interfaces (BCIs) have been widely used as an alternative communication channel to patients with severe motor disabilities, achieving high classification accuracy through machine learning techniques. Recently, deep learning techniques have spotlighted the state-of-the-art of MI-based BCIs. These techniques still lack strategies to quantify predictive uncertainty and may produce overconfident predictions. In this work, methods to enhance the performance of existing MI-based BCIs are proposed in order to obtain a more reliable system for real application scenarios. First, the Monte Carlo dropout (MCD) method is proposed on MI deep neural models to improve classification and provide uncertainty estimation. This approach was implemented using Shallow Convolutional Neural Network (SCNN-MCD) and with an ensemble model (E-SCNN-MCD). As another contribution, to discriminate MI task predictions of high uncertainty, a threshold approach is introduced and tested for both SCNN-MCD and E-SCNN-MCD approaches. The BCI Competition IV Databases 2a and 2b were used to evaluate the proposed methods for both subject-specific and non-subject-specific strategies, obtaining encouraging results for MI recognition

Empleo del software Sunny Design Web con vistas a dimensionar el sistema conectado a Red en la Universidad Técnica de Manabí

La energía eléctrica generada a partir de fuentes de energía renovables presenta la ventaja de brindar una autosuficiencia que no solo conduce a una mejora del medio ambiente sino también incrementa la rentabilidad de una instalación. Siendo conscientes de estas ventajas, este trabajo investigativo se orientó al estudio energético y económico de un proyecto que, a partir de fuentes renovables de energía en el esquema de generación distribuida, es decir se realizó el dimensionamiento de una Unidad de Producción de Autoconsumo (UPAC) para la Universidad Técnica de Manabí (UTM). Además, se realizó una evaluación energética para encontrar oportunidades de racionalización de consumos de energía; como caso práctico el sistema de iluminación pública del campus universitario. En este caso específico se realiza el análisis con el empleo del software Sunny Design Web, brindándose los resultados alcanzados