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This paper presents a novel methodology for detecting faults in wind turbine blades using computational learning techniques. The study evaluates two models: the first employs logistic regression, which outperformed neural networks, decision trees, and the naive Bayes method, demonstrating its effectiveness in identifying fault-related patterns. The second model leverages clustering and achieves superior performance in terms of precision and data segmentation. The results indicate that clustering may better capture the underlying data characteristics compared to supervised methods. The proposed methodology offers a new approach to early fault detection in wind turbine blades, highlighting the potential of integrating different computational learning techniques to enhance system reliability. The use of accessible tools like Orange Data Mining underscores the practical application of these advanced solutions within the wind energy sector. Future work will focus on combining these methods to improve detection accuracy further and extend the application of these techniques to other critical components in energy infrastructure.

Potable water utilities are currently making great efforts to reduce leakage rates and assure long-term supply to the population due to the challenges of climate change, growing population and water shortage scenarios that have been on them over the last years. One of the most employed methods to reduce leakage includes the installation of pressurereducing valves along the water distribution network and the utilization of pressure management schemes. Pressure management includes different types of control models, which are applied according to the requirements of each site. The most advanced and sophisticated scheme is critical point control, which relies on a flow signal from a measuring device or online communication between the critical point and the valve. This paper proposes the utilization of a seasonal autoregressive integrated moving average, or the SARIMA model, to correlate pressure at the outlet of the valve and pressure on the critical point of the area supplied, aiming to set a fixed pressure in the critical point. The SARIMA model is developed according to historical data logged in the field and then validated. Later, the SARIMA model was tested on a real location in the village of Noja, Spain. The analysis of the field test results prove that the proposed model is feasible to be used since there is no significance difference between the target values set in the critical point and the real values measured in the field. The research proves that the SARIMA model can be used as an alternative for critical point control in water distribution networks when no flow signal is available or when communication between the critical point and the pressure reducing valve is not an option.

Fires in photovoltaic (PV) electrical systems are a real and serious problem because this phenomenon can have severe consequences for the safety of people and the environment. In some cases, fires result from a lack of maintenance or improper installation of PV modules. It is essential to consider prevention and continuous monitoring of the electrical parameters to minimize these risks, as these factors increase the temperature of the photovoltaic modules. The use of thermal analysis techniques can prevent hotspots and fires in photovoltaic systems; these techniques allow detecting and correcting problems in the installation, such as shadows, dirt, and poor-quality connections in PVs. This paper presents a case study of the implementation of thermal analysis in an installation of photovoltaic modules connected to a solar pumping system to identify the formation of hotspots through thermal images using an unmanned aerial vehicle (UAV). Here, a novel methodology is proposed based on the comparison of temperature increases concerning the values of short circuit current, open circuit voltage, and real efficiency of each PV module. In addition, an electrical safety methodology is proposed to design a photovoltaic system that prevents fires caused by hotspots, contemplating critical parameters such as photovoltaic power, number of photovoltaic modules, DC:AC conversion ratio, electrical conductor selection, control devices, and electrical protection; the performance power expected was obtained using standard power test conditions, including irradiance factor, photovoltaic module (PVM) temperature factor, and power reduction factor.

Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in the TEG modeling. This paper addresses this research gap by presenting a comparative evaluation of three optimization methods, Particle Swarm Optimization (PSO), Salps Search Algorithm (SSA), and Vortex Search Algorithm (VSA), for TEG parameter estimation. The proposed integrated approach is significant as it overcomes the limitations of existing methods and provides a more accurate and rapid estimation of TEG parameters. The performance of each optimization method is evaluated in terms of root mean square error (RMSE), standard deviation, and processing time. The results indicate that all three methods perform similarly, with average RMSE errors ranging from 0.0019 W to 0.0021 W, and minimum RMSE errors ranging from 0.0017 W to 0.0018 W. However, PSO has a higher standard deviation of the RMSE errors compared to the other two methods. In addition, we present the optimized parameters achieved through the proposed optimization methods, which serve as a reference for future research and enable the comparison of various optimization strategies. The disparities observed in the optimized outcomes underscore the intricacy of the issue and underscore the importance of the integrated approach suggested for precise TEG parameter estimation.

This work addressed the problem regarding the optimal integration of battery systems (BS) in grid-connected networks (GCNs) with the purpose of reducing energy losses and CO2 emissions, for which it formulates a mathematical model that considers the constraints associated with the operation of GCNs in a distributed generation environment that includes BS and variable power generation related to photovoltaic (PV) distributed generation (DG) and demand. As solution strategies, three different master–slave methodologies are employed that are based on sequential programming methods, with the aim to avoid the implementation of commercial software. In the master stage, to solve the problem regarding the location and the type of batteries to be used, parallel-discrete versions of the Montecarlo method (PMC), a genetic algorithm (PDGA), and the search crow algorithm (PDSCA) are employed. In the slave stage, the particle swarm optimization algortihm (PSO) is employed to solve the problem pertaining to the operation of the batteries, using a matrix hourly power flow to assess the impact of each possible solution proposed by the master–slave methodologies on the objective functions and constraints. As a test scenario, a GCN based on the 33-bus test systems is used, which considers the generation, power demand, and CO2 emissions behavior of the city of Medellín (Colombia). Each algorithm is executed 1000 times, with the aim to evaluate the effectiveness of each solution in terms of its quality, standard deviation, and processing times. The simulation results obtained in this work demostrate that PMC/PSO is the master–slave methodology with the best performance in terms of solution quality, repeatability, and processing time.

Renewable energy arises as a tool for the supply of energy to the agriculture sector. Currently, there is a growing concern for the environment. This circumstance has led to technological progress in energy use in relation to natural resources and their availability for all productive sectors, including agriculture. The main objective of this work is to perform analysis from a bibliometric point of view and to analyze scientific advances in renewable energy and agriculture worldwide that have occurred in the last three years (2019–2021). The purpose of this study is to provide an overview of the last three years on the topic in order to contribute to the international scientific community, specifically towards collaboration between authors, institutions, and countries. A keyword analysis using community detection was applied to detect the five main clusters of this research and was largely dedicated to the following topics: renewable energy technologies in agriculture, bioenergy, sustainable agriculture, biomass energy, and the environmental impact of agriculture. The main countries found to be conducting research on renewable energy and agriculture include India, China, the United States, Italy, the United Kingdom, Poland, Indonesia, Germany, the Russian Federation, and Spain; the most important institutions conducting research in this area include the Ministry of Agriculture of the People’s Republic of China, the Tashkent Institute of Irrigation and Agricultural Mechanization Engineers at the National Research University in Uzbekistan, the Chinese Academy of Agricultural Sciences, and the Grupo de Investigação em Engenharia e Computação Inteligente para a Inovação e o Desenvolvimento in Portugal. These results may contribute to the identification of new research needs and therefore to the development of future directions of research on renewable energies in the agricultural sector.

In Mexico, the economic and industrial development is in the center and north; this represents more than 50% of the country’s total consumption. Data on population and energy consumption will be obtained from the following sources: the National Institute of Geography and Statistics (INEGI), and the Energy Information System. Regarding meteorological data, two databases are used: the Automatic Weather Stations (AWS) (for solar irradiance data) and the MERRA-2 reanalysis data (for wind data). These data will be analyzed for use in a geographic information system (GIS) using kriging interpolation to create maps of solar and wind energy. The area studied includes the following states: Mexico City, Puebla, State of Mexico, Hidalgo, Morelos, Zacatecas, Queretaro, San Luis Potosi, Guanajuato, Aguascalientes and Tlaxcala. The results showed that the areas with the highest solar potential are Hidalgo, Estado de México, Morelos, northern Puebla, southern Queretaro, northwestern Guanajuato, and northern Zacatecas, with 5.89 kWh/m2/day, and the months with the highest solar potential are March, April, May, and June. Regarding wind potential, the maximum wind power density is in Puebla, with 517 W/m2, and the windy season in central Mexico spans June, July, August, September, October, and November.

According to data provided by the International Energy Agency, buildings consume more than one-third of the energy produced globally and represent a major source of carbon dioxide-related emissions [...]

The problem of optimally integrating PV DGs into electrical networks to reduce annual costs (which include energy purchase and investment costs) was addressed in this research by presenting a new solution methodology. For such purpose, we used a Discrete–Continuous Parallel Particle Swarm Optimization method (DCPPSO), which considers both the discrete and continuous variables associated with the location and sizing of DGs in an electrical network and employs a parallel processing tool to reduce processing times. The optimization parameters of the proposed solution methodology were tuned using an external optimization algorithm. To validate the performance of DCPPSO, we employed the 33- and 69-bus test systems and compared it with five other solution methods: the BONMIN solver of the General Algebraic Modeling System (GAMS) and other four discrete–continuous methodologies that have been recently proposed. According to the findings, the DCPPSO produced the best results in terms of quality of the solution, processing time, and repeatability in electrical networks of any size, since it showed a better performance as the size of the electrical system increased.

This study presents a master–slave methodology to solve the problem of optimally locating and sizing photovoltaic (PV) generation units in electrical networks. This problem is represented by means of a Mixed-Integer Nonlinear Programming (MINLP) model, whose objective function is to reduce the total annual operating costs of a network for a 20-year planning period. Such costs include (i) the costs of purchasing energy at the conventional generators (the main supply node in this particular case), (ii) the investment in the PV generation units, and (iii) their corresponding operation and maintenance costs. In the proposed master–slave method, the master stage uses the Discrete–Continuous version of the Crow Search Algorithm (DCCSA) to define the set of nodes where the PV generation units will be installed (location), as well as their nominal power (sizing), and the slave stage employs the successive approximation power flow technique to find the value of the objective function of each individual provided by the master stage. The numerical results obtained in the 33- and 69-node test systems demonstrated its applicability, efficiency, and robustness when compared to other methods reported in the specialized literature, such as the vortex search algorithm, the generalized normal distribution optimizer, and the particle swarm optimization algorithm. All simulations were performed in MATLAB using our own scripts.

In a world in which electricity is increasingly necessary, it is vitally important to ensure that the supply of this electricity is safe, reliable, sustainable, and environmentally friendly, reducing CO2 emissions into the atmosphere and the use of fossil fuels [...]

Currently, with the quick increase in global population, the energetic crisis, the environmental problematic, and the development of the power electronic devices generated the need to include new technologies for supporting and potentiating electrical distributions systems; Distribution Static Compensators (D-STATCOMs) are highly used for this task due to the advantages that this technology presents: reduction in power loss, operation costs, and chargeability of branches, among others. The possibility to include this kind of technology within the electrical system has shown the need to develop efficient methodologies from the point of view of quality solution, repeatability and processing times by considering operation and investment costs as well as the technical conditions of the electrical grids under a scenario of variable power demand and then representing the real operation of the electrical grid. With the aim to propose a solution for this requirement, this paper presents a new Discrete-Continuous Particle Swarm Optimization (DCPSO) algorithm to solve the problem of the optimal integration of D-STATCOMs into Electrical Distribution Systems (EDSs). In this case, the objective function is the minimization of annual operating costs by using a weighted mono-objective function composed of the annual power loss and the investment cost and by including all constraints associated with the operation of an EDS in a distributed reactive compensation environmentinside the mathematical formulation. In order to evaluate the effectiveness and robustness of the proposed solution method, this study implemented two tests systems (i.e., 33- and 69-bus), as well as four comparison methods, and different considerations related to the inclusion of D-STATCOMs in the EDSs. Furthermore, for evaluating the repeatability of the solution obtained by each solution methods used, each algorithm was executed 100 times in Matlab software. The results obtained demonstrated that the proposed DCPSO/HSA methodology achieved the best trade-off between solution quality and processing time, with low standard deviation values for EDSs of any size.

Water is an essential element for life [...]

According to United Nations data, half of the world’s population lives in cities and forecasts indicate that by the middle of the 21st century, this percentage will have increased to 65%. The increase in the urban population favors the creation of a network of interactions that entails a series of material and energy flows. These cause environmental impacts that affect the quality of life of citizens and the environment as a whole. According to data from the International Energy Agency, cities occupy 3% of the planet’s surface and are responsible for 67% of global energy consumption. The effects caused by this consumption, as well as its impact on the depletion of resources, make it necessary to carry out an exhaustive study of renewable energies and new energy saving systems. This Special Issue aims to present new advances and developments in renewable energy and energy saving systems that allow cities to evolve in a sustainable way.

In the present work, a methodology that allows optimizing the permanent magnet synchronous generator (PMSG) design by establishing limit values of magnet radius and length that maximize efficiency for the nominal parameters of the wind turbine is developed. The methodology consists of two fundamental models. One model calculates the generator parameters from the radius of the magnet base, and the other optimization model determines two optimum generators according to the optimization criteria of maximum efficiency and maximum efficiency with minimum weight starting from the axial length and the radius of the magnet base. For the optimization, the numerical method of the golden section was used. The model was validated from a 10 kW PMSG and the results of two optimum generators are presented according to the optimization criteria. In addition, when the obtained results are compared with the reference electric generator, an increase in efficiency of 1.15% and 0.81% and a reduction in weight of 30.79% and 39.15% of the optimized generators are obtained for maximum efficiency and minimum weight, respectively. Intermediate options between the maximum efficiency generator and the minimum weight generator allows for the selection of the optimum dimensioning for the electric generator as a function of the parameters from the wind turbine design.

In this paper, we present a nonlinear PID controller based on saturation functions with variable parameters in order to regulate the output voltage of a buck converter in the presence of changes in the input voltage. The main feature of the proposed controller is to bound the control input with a variable parameter to avoid the windup effect generated by the combination of the integral control action and some operation conditions. The main advantages of the proposed nonlinear PID controller are its low computing cost and the simple tuning task to implement the control strategy in an embedded system. The acceptable behavior of the closed-loop system is presented through the simulation and experimental results.

This article presents the analysis of the performance of a flexible wind turbine blade. The simulation analysis is based on a 3 m span blade prototype. The blade has a flexible surface and a cam mechanism that modifies the aerodynamic profile and adapts the surface to different configurations. The blade surface was built with a flexible fiberglass composite, and the internal mechanism consists of a flexible structure actuated with an eccentric cam. The cam mechanism deforms five sections of the blade, and the airfoil geometry for each section was measured from zero cam displacement to full cam displacement. The measured data were interpolated to obtain the aerodynamic profiles of the five sections to model the flexible blade in the simulation process. The simulation analysis consisted of determining the different aerodynamic coefficients for different deformed surfaces and a range of wind speeds. The aerodynamic coefficients were calculated with the BEM method (QBlade®); as a result, the data performance of the flexible blade was compared for the different deformation configurations. Finally, a decrease of up to approximately 6% in the mean bending moment suggests that the flexible turbine rotor presented in this article can be used to reduce extreme and fatigue loads on wind turbines.

In this paper, we propose a master–slave methodology to address the problem of optimal integration (location and sizing) of Distributed Generators (DGs) in Direct Current (DC) networks. This proposed methodology employs a parallel version of the Population-Based Incremental Learning (PPBIL) optimization method in the master stage to solve the location problem and the Vortex Search Algorithm (VSA) in the slave stage to solve the sizing problem. In addition, it uses the reduction of power losses as the objective function, considering all the constraints associated with the technical conditions specific to DGs and DC networks. To validate its effectiveness and robustness, we use as comparison methods, different solution methodologies that have been reported in the specialized literature, as well as two test systems (the 21 and 69-bus test systems). All simulations were performed in MATLAB. According to the results, the proposed hybrid (PPBIL–VSA) methodology provides the best trade-off between quality of the solution and processing times and exhibits an adequate repeatability every time it is executed.

This paper presents an analysis of sound pressure levels through theoretical modeling and experimental validation in a 1 kW small wind turbine. The models used in the theoretical analysis are BPM (Brooks, Pope, and Marcolini) and BM (Brooks and Marcolini), where wind turbine blades are divided in sections, and each section has its own contribution with respect to the total emitted sound pressure level. The noise propagation study and its experimental validation were accomplished within the requirements of the standard IEC 61400-11 Ed.3 and the standard NOM-081-SEMARNAT-1994. The comparative study of theoretical and experimental results showed that the BPM and BM methods have a maximum error of 5.5% corresponding to the rated wind speed of 10 m/s. However, at low wind speeds, the theoretical models fit well to experimental data, for example, in the range from 5 to 8 m/s. The experimental data showed that the rotor’s aerodynamic noise is more evident at low wind speed, because under these conditions, environmental noise is much less than wind turbine noise. Finally, to prevent possible negative effects on people’s health, there is a recommended minimum and suitable distance between small wind turbine installations and buildings.

The use of renewable energies, energy saving, and efficiency are needs of global society. According to the latest estimates, global energy demand could triple by 2050 and, by then, 70% of the world’s population will live in cities. Cities are currently responsible for 80% of greenhouse gas emissions, so they have a key role to play in shifting towards a sustainable energy future. Cities are threatened not only by overcrowding, but also by rising energy demand, obsolete infrastructure, volatile energy markets, and the effects of climate change. This Special Issue aims to advance the contribution of energy saving and the use of renewable energies in order to achieve more sustainable cities.

In line with Mexico’s interest in determining its wind resources, in this paper, 141 locations along the states of the Gulf of Mexico have been analyzed by calculating the main wind characteristics, such as the Weibull shape (c) and scale (k) parameters, and wind power density (WPD), by using re-analysis MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications version 2) data with hourly records from 1980−2017 at a 50-m height. The analysis has been carried out using the R free software, whose its principal function is for statistical computing and graphics, to characterize the wind speed and determine its annual and seasonal (spring, summer, autumn, and winter) behavior for each state. As a result, the analysis determined two different wind seasons along the Gulf of Mexico;, it was found that in the states of Tamaulipas, Veracruz, and Tabasco wind season took place during autumn, winter, and spring, while for the states of Campeche and Yucatan, the only two states that shared its coast with the Caribbean Sea and the Gulf of Mexico, the wind season occurred only in winter and spring. In addition, it was found that by considering a seasonal analysis, more accurate information on wind characteristics could be generated; thus, by applying the Weibull distribution function, optimal zones for determining wind as a resource of energy can be established. Furthermore, a k-means algorithm was applied to the wind data, obtaining three clusters that can be seen by month; these results and using the Weibull parameter c allow for selecting the optimum wind turbine based on its power coefficient or efficiency.

The Energetic Transition Law in Mexico has established that in the next years, the country has to produce at least 35% of its energy from clean sources in 2024. Based on this, a proposal in this study is the cogeneration between the principal thermal power plants along the Mexican states of the Gulf of Mexico with modeled wind farms near to these thermal plants with the objective to reduce peak electricity demand. These microscale models were done with hourly MERRA-2 data that included wind speed, wind direction, temperature, and atmospheric pressure with records from 1980−2018 and taking into account roughness, orography, and climatology of the site. Wind speed daily profile for each model was compared to electricity demand trajectory, and it was seen that wind speed has a peak at the same time. The amount of power delivered to the electric grid with this cogeneration in Rio Bravo and Altamira (Northeast region) is 2657.02 MW and for Tuxpan and Dos Bocas from the Eastern region is 3196.18 MW. This implies a reduction at the peak demand. In the Northeast region, the power demand at the peak is 8000 MW, and for Eastern region 7200 MW. If wind farms and thermal power plants work at the same time in Northeast and Eastern regions, the amount of power delivered by other sources of energy at this moment will be 5342.98 MW and 4003.82 MW, respectively.

Concern about everything related to energy is increasingly latent in the world and therefore the use of energy saving concepts has been increasing over the past several years. The interest in the subject has allowed a conceptual evolution in the scientific community regarding the understanding of the adequate use of energy. The objective of this work is to determine the contribution made by international institutions to the specialized publications in the area of energy-saving from 1939 to 2018, using Scopus Database API Interface. The methodology followed in this research was to perform a bibliometric analysis of the whole scientific production indexed in Scopus. The world’s scientific production has been analysed in the following domains: First the trend over time, types of publications and countries, second, the main subjects and keywords, third, main institutions and their main topics, and fourth, the main journals and proceedings that publish on this topic. Then, these data are presented using community detection algorithms and graph visualization software. With these techniques, it is possible to determine the main areas of research activity as well as to identify the structures of the collaboration network in the field of renewable energy. The results of the work show that the literature in this field have substantially increased during the last 10 years.

This study aims to highlight the contribution made by different international institutions in the field of urban generation of renewable energy, as a key element to achieve sustainability. This has been possible through the use of the Scopus Elsevier database, and the application of bibliometric techniques through which the articles content published from 1977 to 2017 has been analysed. The results shown by Scopus (e.g., journal articles and conferences proceedings) have been taken into account for further analysis by using the following search pattern (TITLE-ABS-KEY ({Renewable energy} AND ({urban} OR ({cit*})). In order to carry out this study, key features of the publications have been taken into consideration, such as type of document, language, thematic area, type of publication, and keywords. As far as keywords are concerned, renewable energy, sustainability, sustainable development, urban areas, city, and energy efficiency, have been the most frequently used. The results found have been broken down both geographically and by institution, showing that China, the United States, the United Kingdom, Italy, Germany and India are the main research countries and Chinese Academy of Sciences, Ministry of Education China and Tsinghua University the major contributing institutes. With regard to the categories, Energy, Environmental Sciences, and Engineering are positioned as the most active categories. The scientific community agrees that the study of the renewable energy generation in cities is of vital importance to achieve more sustainable cities, and for the welfare of a growing urban population. Moreover, this is in line with the energy policies adopted by most of developed countries in order to mitigate climate change effects.

The aim of this study is to examine the possibility of using a stand-alone photovoltaic system (SAPVS) for electricity generation in urban areas in Southern Mexico. In Mexico, an urban area is defined as an area where more than 2500 inhabitants live. Due to constant migration from the countryside to the cities, the number of inhabitants of urban localities has been increasing. Global horizontal irradiation (GHI) data were recorded every 10 min during 2014–2016 in Coatzacoalcos in the state of Veracruz located on 18°08′09″ N and 94°27′48″ W. In this study, batteries represented 77% of the total cost, 12 PV panels of 310 W could export 5.41 MWh to the grid, and an inverter with an integrated controller and charger was selected, which decreased the initial cost. The city of Coatzacoalcos was chosen because the average annual temperature is 28°, with an average relative humidity of 75% and an average irradiance of 5.3 kWh/m2/day. An emission factor 0.505 tCO2/MWh of greenhouse gases (GHG) were obtained, based on the power system, the reduction of net annual GHG would be 11 tCO2 and a financial revenue of 36.951 × 103 $/tCO2 would be obtained. Financial parameters such as a 36.3% Internal Rate Return (IRR) and 3.4 years payback show the financial viability of this investment. SAPVSs in urban areas in Mexico could be a benefit as long as housing has a high consumption of electricity.

For populations that may be affected, the risks of earthquakes and tsunamis are a major concern worldwide. Therefore, early detection of an event of this type in good time is of the highest priority. The observatories that are capable of detecting Extremely Low Frequency (ELF) waves (

One of the most important variables in wind assessment as resource is Wind Power Density (WPD) which is finally the parameter that gives information about the site analyzed. To calculated WPD is strongly recommended to determine the two parameters of Weibull function, shape factor (k) and scale factor (c) to have a preliminary idea of how the wind resource is on the evaluated site. In this study has been analyzed wind speed as resource with Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) data, these are re-analysis hourly data from 1980-2016 at 50m height, over the Yucatan Peninsula. An algorithm was designed to separate data in four periods of time for an average day (00:00-05:00; 06:00-11:00; 12:00-17:00; 18:00-23:00) in order to obtain a more accurate wind description according to the Weibull function, its parameters k and c, as well as the prevailing direction on every site. The aim of this study is comparing WPD to energy demand curve in the Yucatan Peninsula, according to the different periods of the day.