Energies and Its Worldwide Research

Energy efficiency and management is certainly one of the key drivers of human progress. Thus, the trends in the energy research are a topic of interest for the scientific community. The aim of this study is to highlight global research trends in this field through the analysis of a scientific journal indexed exclusively in the energy and fuels category. For this purpose, a journal has been selected that is in the center of the category considering its impact factor, which is only indexed in this category and of open access, Energies of the publisher MDPI. Therefore, a bibliometric analysis of all the contents of the journal between 2008 and 2020, 13,740 documents published, has been carried out. Analyzing the articles that are linked to each other by their citations, 14 clusters or research topics have been detected: smart grids; climate change–electric energy community; energy storage; bioenergy sources; prediction algorithms applied to power; optimization of the grid link for renewable energy; wind power; sustainability of power systems; hydrocarbon improvements; conversion of thermal/electrical energy; electric motor advancements; marine renewable energy; hydropower and energy storage; and preventive techniques in power transformers. The main keywords found were electric vehicle, renewable energy, microgrid, smart grid, and energy efficiency. In short, energy research remains necessary to meet the future challenge of sustainable energy with high efficiency and the exploration of new renewable resources, all for increasingly sustainable cities

Nouvelles observations sur la géologie de la région de Saumur (Maine-et-Loire)

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Quadrature Current Compensation in Non-Sinusoidal Circuits Using Geometric Algebra and Evolutionary Algorithms

Non-linear loads in circuits cause the appearance of harmonic disturbances both in voltage and current. In order to minimize the effects of these disturbances and, therefore, to control the flow of electricity between the source and the load, passive or active filters are often used. Nevertheless, determining the type of filter and the characteristics of their elements is not a trivial task. In fact, the development of algorithms for calculating the parameters of filters is still an open question. This paper analyzes the use of genetic algorithms to maximize the power factor compensation in non-sinusoidal circuits using passive filters, while concepts of geometric algebra theory are used to represent the flow of power in the circuits. According to the results obtained in different case studies, it can be concluded that the genetic algorithm obtains high quality solutions that could be generalized to similar problems of any dimension

Power Quality: Scientific Collaboration Networks and Research Trends

Power quality is a research field related to the proper operation of devices and technological equipment in industry, service, and domestic activities. The level of power quality is determined by variations in voltage, frequency, and waveforms with respect to reference values. These variations correspond to different types of disturbances, including power fluctuations, interruptions, and transients. Several studies have been focused on analysing power quality issues. However, there is a lack of studies on the analysis of both the trending topics and the scientific collaboration network underlying the field of power quality. To address these aspects, an advanced model is used to retrieve data from publications related to power quality and analyse this information using a graph visualisation software and statistical tools. The results suggest that research interests are mainly focused on the analysis of power quality problems and mitigation techniques. Furthermore, they are observed important collaboration networks between researchers within and across countries

Multi-Objective Evolutionary Algorithms to Find Community Structures in Large Networks

Real-world complex systems are often modeled by networks such that the elements are represented by vertices and their interactions are represented by edges. An important characteristic of these networks is that they contain clusters of vertices densely linked amongst themselves and more sparsely connected to nodes outside the cluster. Community detection in networks has become an emerging area of investigation in recent years, but most papers aim to solve single-objective formulations, often focused on optimizing structural metrics, including the modularity measure. However, several studies have highlighted that considering modularityas a unique objective often involves resolution limit and imbalance inconveniences. This paper opens a new avenue of research in the study of multi-objective variants of the classical community detection problem by applying multi-objective evolutionary algorithms that simultaneously optimize different objectives. In particular, they analyzed two multi-objective variants involving not only modularity but also the conductance metric and the imbalance in the number of nodes of the communities. With this aim, a new Pareto-based multi-objective evolutionary algorithm is presented that includes advanced initialization strategies and search operators. The results obtained when solving large-scale networks representing real-life power systems show the good performance of these methods and demonstrate that it is possible to obtain a balanced number of nodes in the clusters formed while also having high modularity and conductance values