Industrial and Technological Applications of Power Electronics Systems

The Special Issue "Industrial and Technological Applications of Power Electronics Systems" focuses on: - new strategies of control for electric machines, including sensorless control and fault diagnosis; - existing and emerging industrial applications of GaN and SiC-based converters; - modern methods for electromagnetic compatibility. The book covers topics such as control systems, fault diagnosis, converters, inverters, and electromagnetic interference in power electronics systems. The Special Issue includes 19 scientific papers by industry experts and worldwide professors in the area of electrical engineering

Integrating air quality and climate change strategies in Oman

Air pollution and climate change are two of the main environmental problems being faced at the moment across the globe and both have a major and costly impact on human health. Oman needs to avoid the negative scenario which could result, and this can best be done by the integration of air quality control and climate change mitigation strategies. This integration would improve air quality and meet climate goals and would be cost-effective as well as beneficial. This thesis investigates the potential for integrated climate and air quality strategies in Oman, and incorporates the mitigation of SLCPs, a group of pollutants that has not yet been considered in the country. The implications of the mitigation strategies on human health and the climate are analysed and the current air quality and climate change policies are examined. The thesis also develops the first emission inventory to be based on national data for the country, using the data for 2010; it presents Oman’s emissions of carbon dioxide, methane, carbon monoxide, sulphur dioxide, nitrogen oxides, non-methane volatile organic compounds, ammonia, particulate matter, black carbon and organic carbon. The emission inventory estimated total emissions of CO2 of 50 Mt, CH4 of 565 kt, SO2 of 160 kt, NMVOCs of 142 kt, NOx of 137 kt, primary PM2.5 of 61 kt, CO of 51 kt, NH3 of 18 kt, BC of 4 kt, and OC of 0.9 kt. The main sources of pollution in 2010 were emissions from electricity generation, oil and gas extraction, road transport and the manufacturing industry. Twelve emission scenarios are developed and analysed in this thesis: the Business As Usual (BAU) scenario and eleven mitigation scenarios. The mitigation scenarios are as follows: • the renewable energy scenario and the zero coal scenario, both for the electricity generation sector • the fugitive reduction scenario and the zero flaring scenario, both for the oil and gas sector • the efficient industry scenario and the clean fuel scenario in the manufacturing industry • the electric vehicle scenario, public transport scenario and paved road scenario for the road transport sector • the efficient air conditioning scenario for the residential sector and • the efficient air conditioning scenario for commercial and public services. If all the measures covered in all these scenarios were implemented, this thesis estimates that 191 Mt of CO2 would be avoided in 2050, as well as 840 kt of CH4, 144 kt of a SO2, 92 kt of NMVOCs, 211 kt of NOx, 34 kt of primary PM2.5 emissions, 81 kt of CO, 3 kt of NH3, 3 t of BC, and 3 t of OC. This thesis has estimated that in 2010, approximately 711 cases of premature death in Oman could be attributed to PM2.5; this figure would rise to 1810 cases in 2030, and to more than 4400 cases in 2050, according to the BAU scenario. Soil dust is the largest source of PM2.5 in all years, but the other main sources contributing to it are transport (81%), industrial process emissions (10%), and the oil and gas industry (5%). This thesis confirms the urgent need to develop an integrated air quality and climate change policy. This thesis also assesses the current legal framework that could be used to reduce emissions in Oman. Current policy was examined to understand the level of effort being made to reduce air pollution, SLCPs and GHGs. Despite public concern in Oman over these issues, especially climate change, little action has so far been taken to reduce emissions. Semi-structured interviews were held with representatives of government, industry, NGOs, media and the judiciary. From the interviews it was clear that the level of interest among the different sectors of the Omani establishment does not reflect – and is indeed much lower than - the level of concern felt by the public about these issues. This is shown by the lack of ambitious targets in Oman’s NDC and by the low level of attention paid to air pollution. This thesis shows that unless action is taken, the impact of air pollution will get worse, not only because of increasing pollution but also because of an aging and growing population. The measures in the different scenarios developed could lead to a 60% reduction in CO2 and a 33% reduction in methane, and also greatly reduce the negative impact on health. The analysis has also shown that this could lead to a net reduction of 7 % in warming by 2050, and offset the released warming from reducing SO2 and NOx emissions to protect human health. For this to happen, the current legislative framework could be used, but a positive outcome will depend on two key factors. First, all stakeholders need to become far more aware of the issues involved, as at present these are not widely understood; and second, there is need for further policy development and legislation and also for investment in capacity and infrastructure

New Developments in Renewable Energy

Renewable energy is defined as the energy which naturally occurs, covers a number of sources and technologies at different stages, and is theoretically inexhaustible. Renewable energy sources such as those who are generated from sun or wind are the most readily-available and possible solutions to address the challenge of growing energy demands in the world. Newer and environmentally friendly technologies are able to provide different social and environmental benefits such as employment and decent environment. Renewable energy technologies are crucial contributors to world energy security, reduce reliance on fossil fuels, and provide opportunities for mitigating greenhouse gases. International public opinion indicates that there is strong support for a variety of methods for solving energy supply problems, one of which is utilizing renewable energy sources. In recent years, countries realized that that the renewable energy and its sector are key components for greener economies

Essays on the Economics of Technological Change and the Environment

Technological change bears the promise of addressing environmental problems without reneging on economic development. However, taping its full potential requires an understanding of its drivers and barriers. The three chapters of this dissertation are a modest attempt at casting light on some of the factors that can foster technological change towards more environmental-friendly technologies. In Chapter One, I provide the first quantitative evidence that the Montreal Protocol, and its following amendments to protect the ozone layer, triggered a large increase in research and innovation on alternatives to ozone-depleting molecules. To do this, I use the full text of patents and scientific articles and implement a difference-in-differences strategy and a synthetic control method. To compare molecules’ chemical and industrial characteristics, I construct descriptive variables by applying machine learning techniques to the documents’ text. In Chapter Two, I investigate barriers to adopting solar lanterns in the context of rural Indian households. I design and implement a randomized controlled trial on people’s willingness to pay for such lanterns, and find that, despite the relative simplicity of the product, information barriers to adopting solar lanterns remain high. Chapter Three theoretically investigates firm-level barriers to green technological change. I outline a mechanism that explains why coordination at the industry level might be necessary. I argue that radical innovations (such as electric cars) require complementary innovations in interdependent components, and show that, when technological change requires investment by both suppliers and producers, coordination within an industry is needed and can be difficult to obtain

Incidencia de la calidad el aire en el desarrollo urbano sostenible. Metodología de pronóstico basado en herramientas de aprendizaje automático

Tesis por compendio[ES] La calidad del aire es un determinante de la salud y bienestar de las poblaciones; su mejora es parte de algunas metas de los objetivos de desarrollo sostenible (ODS) con la Agenda 2030. Al respecto, se han definido a nivel mundial protocolos, acuerdos, convenios y lineamientos de política para lograr avanzar en el cumplimiento de los ODS. Existen además reportes nacionales de avance en la implementación de metas específicas, según la agenda de cada país y en algunos casos en el ámbito de ciudad, cuyos indicadores pueden integrarse en las dimensiones más conocidas del desarrollo sostenible: la dimensión ambiental, la social y la económica. Existe información sobre el monitoreo del estado de la calidad de los recursos y de condiciones del territorio en diversos temas. Sin embargo, no en todos los territorios, en sus diferentes escalas espaciales, se realiza una continua evaluación de su desempeño sostenible y, además factores de deterioro ambiental como la contaminación del aire, son tratados como determinantes aislados con la generación de reportes de su comportamiento y el desarrollo de planes de monitoreo y de mitigación. Del mismo modo, para los diferentes temas que hacen parte de las dimensiones de la sostenibilidad, existen herramientas de modelación para evaluar el comportamiento de sus indicadores; sin embargo, no se cuenta con un instrumento que pronostique el nivel de avance en el desarrollo sostenible y además que identifique la influencia de la calidad del aire en su comportamiento. Las herramientas de aprendizaje automático pueden aportar en la respuesta a dicha situación, al ser instrumentos útiles en el pronóstico del comportamiento de un conjunto de datos. Por consiguiente, el objetivo central de este trabajo doctoral es establecer la incidencia de la calidad del aire sobre el desarrollo urbano sostenible, en sus dimensiones ambiental, social y económica, mediante el uso de herramientas de aprendizaje automático, como soporte para la toma de decisiones. Este objetivo involucra el diseño y ejecución de una metodología para identificar la influencia de indicadores en materia de calidad del aire, sobre el desarrollo urbano sostenible. Este trabajo doctoral se desarrolló como compendio de un conjunto de publicaciones que incluyen 1) la revisión del estado del arte para la identificación de las variables y parámetros que podrían calificar las dimensiones individuales del desempeño sostenible, 2) la evaluación del nivel de avance en el desarrollo sostenible de una zona urbana y el análisis estadístico de su desempeño sostenible según las variables analizadas; 3) la identificación, selección y aplicación de las herramientas de aprendizaje automático y por último 4) la identificación del grado de influencia de la calidad del aire en el pronóstico del nivel de sostenibilidad establecido. Para ello se hizo uso del software ArcGIS para el análisis espacial y del software de acceso libre R para los análisis estadísticos y la aplicación de las herramientas de aprendizaje automático. Esta investigación se realizó a partir de un estudio de caso en una localidad de la ciudad de Bogotá, en Colombia que es la capital del país, situada sobre una planicie altitudinal en la cordillera oriental y a 2625 metros sobre el nivel del mar. Bogotá es una de las ciudades más pobladas en América Latina y es una de las capitales mundiales que ha presentado altos niveles de contaminación por material particulado, siendo éste un factor de riesgo para su población. La metodología construida permite evaluar la influencia de la calidad del aire en el desarrollo urbano sostenible mediante herramientas de aprendizaje automático. Es aplicable a zonas urbanas y orienta el paso a paso para la determinación de los factores de mayor relevancia en cada una de las dimensiones de la sostenibilidad, constituyéndose en un instrumento de soporte para la toma de decisiones respecto a la implem[CA] La qualitat de l'aire és un determinant de la salut i benestar de les poblacions; la seua millora és part d'algunes metes dels objectius de desenvolupament sostenible (ODS) amb l'Agenda 2030. Sobre aquest tema, s'han definit a nivell mundial protocols, acords, convenis i alineaments de política per a aconseguir avançar en el compliment dels ODS. Existeixen reportes nacionals d'avanç sobre la implementació de metes específiques, segons l'agenda de cada país i en alguns casos en l'àmbit de ciutat, els indicadors de la qual poden integrar-se en les dimensions més conegudes del desenvolupament sostenible: la dimensió ambiental, la social i l'econòmica. Existeix informació sobre el monitoratge de l'estat de la qualitat dels recursos i de les condicions del territori en diversos temes. No obstant això, no en tots els territoris, en les seues diferents escales espacials, es realitza contínua avaluació del seu acompliment sostenible i, a més a més, factors de deterioració ambiental com la contaminació de l'aire, són tractats com a determinants aïllats amb la generació de reportes del seu comportament i el desenvolupament de plans de monitoratge i de mitigació. De la mateixa manera, per als diferents temes que fan part de les dimensions de la sostenibilitat, existeixen eines de modelatge per a avaluar el comportament dels seus indicadors; no obstant això, no es compta amb un instrument que pronostique el nivell d'avanç en el desenvolupament sostenible i a més que identifique la influència de la qualitat de l'aire en el seu comportament. Les eines d'aprenentatge automàtic poden aportar en la resposta a aquesta situació, en ser instruments útils en el pronòstic del comportament d'un conjunt de dades. Per consegüent, l'objectiu central d'aquest treball doctoral és establir la incidència de la qualitat de l'aire sobre el desenvolupament urbà sostenible, en les seues dimensions ambiental, social i econòmica, mitjançant l'ús d'eines d'aprenentatge automàtic, com a suport per a la presa de decisions. Aquest objectiu involucra el disseny i execució d'una metodologia per a identificar la influència d'indicadors en matèria de qualitat de l'aire, sobre el desenvolupament urbà sostenible. Aquest treball doctoral es va desenvolupar com a compendi d'un conjunt de publicacions que inclouen 1) la revisió de l'estat de l'art per a la identificació de les variables i paràmetres que podrien qualificar les dimensions individuals de l'acompliment sostenible, 2) l'avaluació del nivell d'avanç en el desenvolupament sostenible d'una zona urbana i l'anàlisi estadística del seu acompliment sostenible segons les variables analitzades; 3) la identificació, selecció i aplicació de les eines d'aprenentatge automàtic i finalment 4) la identificació del grau d'influència de la qualitat de l'aire en el pronòstic del nivell de sostenibilitat establit. Per a això es va fer ús del programari ArcGIS per a l'anàlisi espacial i del programari d'accés lliure R per a les anàlisis estadístiques i l'aplicació de les eines d'aprenentatge automàtic. Aquesta investigació es va realitzar a partir d'un estudi de cas en una localitat de la ciutat de Bogotà, a Colòmbia que és la capital del país, situada sobre una planícia altitudinal en la serralada oriental i a 2625 metres sobre el nivell de la mar. Bogotà és una de les ciutats més poblades a Amèrica Llatina i és una de les capitals mundials que ha presentat alts nivells de contaminació per material particulat, sent aquest un factor de risc per a la seua població. La metodologia construïda permet avaluar la influència de la qualitat de l'aire en el desenvolupament urbà sostenible mitjançant l'ús d'eines d'aprenentatge automàtic. És aplicable a zones urbanes i orienta el pas a pas per a la determinació dels factors de major rellevància en cadascuna de les dimensions de la sostenibilitat, constituint-se en un instrument de suport per a la presa d[EN] Air quality is a determinant to the health and well-being of populations; its improvement is part of some of the targets of the Sustainable Development Goals (SDGs) with the 2030 Agenda. In this regard, protocols, agreements, pacts, and policy guidelines have been defined worldwide to progress in the SDGs' achievement. Additionally, there are national progress reports on reaching specific goals, based on each country's agenda. In certain cases, these include city-level reports, whose indicators, both at the national and city levels, can be integrated into the central and best-known dimensions of sustainable development, namely the environmental, social, and economic dimensions. There is information concerning the monitoring of the state of resource quality and territorial conditions in various areas. However, not all territories in their different spatial scales are continuously evaluated for their sustainable performance. Moreover, environmental deterioration factors such as air pollution are handled as isolated determinants with reports generated on their behavior, in addition to developing monitoring and mitigation plans. Likewise, there are modeling tools to evaluate the behavior of different components that are part of the dimensions of sustainability. However, there is no instrument that forecasts the level of progress in sustainable development that also identifies the influence of air quality on its behavior. Machine learning tools can contribute to responding to this situation, as they are able to predict the behavior of a data set. Therefore, the primary objective of this doctoral work is to establish the incidence of air quality on urban sustainable development, in its environmental, social, and economic dimensions, through the use of machine learning tools to support decision-making. This objective entails designing and implementing a methodology to identify the influence of air quality indicators on urban sustainable development. This doctoral thesis was developed as a compendium of a set of publications which include: 1) the review of the state of the art for identifying variables and parameters that could qualify the individual dimensions of sustainable performance; 2) the evaluation of the level of progress of the sustainable development of an urban area, and the statistical analysis of its sustainable performance based on the variables analyzed; 3) the identification, selection, and use of machine learning tools, and lastly 4) the identification of the influence of air quality on the prediction of the established sustainability level. The ArcGIS program was used for the spatial analysis, and the free-access software R for the statistical analysis, and the use of the machine learning tools. This research was performed based on a case study of a locality in the capital of Colombia; Bogotá, which is located on an altitudinal plain in the eastern mountain range at 2625 meters above sea level. Bogotá is one of the most populated cities in Latin America and is one of the world capitals with the highest levels of air pollution from particulate matter, which is a risk factor for its population. The methodology developed enables evaluating the influence of air quality on urban sustainable development with machine learning tools. This methodology is valid in urban areas, and through a step-by-step approach, determines the most relevant factors for each sustainability dimension. It has become a tool to support decision-making regarding the implementation and progress of the SDGs from the micro-territory level.Molina Gómez, NI. (2021). Incidencia de la calidad el aire en el desarrollo urbano sostenible. Metodología de pronóstico basado en herramientas de aprendizaje automático [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/168398TESISCompendi

Controlled self-organisation using learning classifier systems

The complexity of technical systems increases, breakdowns occur quite often. The mission of organic computing is to tame these challenges by providing degrees of freedom for self-organised behaviour. To achieve these goals, new methods have to be developed. The proposed observer/controller architecture constitutes one way to achieve controlled self-organisation. To improve its design, multi-agent scenarios are investigated. Especially, learning using learning classifier systems is addressed

Alternative Sources of Energy Modeling, Automation, Optimal Planning and Operation

An economic development model analyzes the adoption of alternative strategy capable of leveraging the economy, based essentially on RES. The combination of wind turbine, PV installation with new technology battery energy storage, DSM network and RES forecasting algorithms maximizes RES integration in isolated islands. An innovative model of power system (PS) imbalances is presented, which aims to capture various features of the stochastic behavior of imbalances and to reduce in average reserve requirements and PS risk. Deep learning techniques for medium-term wind speed and solar irradiance forecasting are presented, using for first time a specific cloud index. Scalability-replicability of the FLEXITRANSTORE technology innovations integrates hardware-software solutions in all areas of the transmission system and the wholesale markets, promoting increased RES. A deep learning and GIS approach are combined for the optimal positioning of wave energy converters. An innovative methodology to hybridize battery-based energy storage using supercapacitors for smoother power profile, a new control scheme and battery degradation mechanism and their economic viability are presented. An innovative module-level photovoltaic (PV) architecture in parallel configuration is introduced maximizing power extraction under partial shading. A new method for detecting demagnetization faults in axial flux permanent magnet synchronous wind generators is presented. The stochastic operating temperature (OT) optimization integrated with Markov Chain simulation ascertains a more accurate OT for guiding the coal gasification practice

Proceedings of the 13th Project integration meeting

Progress made by the Low Cost Solar Array Project during the period April through August 1979 is presented. Reports are given on project analysis and integration; technology development in silicon material, large area sheet silicon, and encapsulation; production process and equipment development; engineering and operations, and a discussion of the steps taken to integrate these efforts. A report on, and copies of viewgraphs presented at the Project Integration Meeting held August 22-23, 1979 are presented

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering