This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances (PQDs). A particular focus is given to voltage sags and notches, as voltage sags cause huge economic losses while research on voltage notches is still very incipient. A systematic method based on scientometrics, text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature. A bibliometric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time, top publishing countries, and the distribution by relevant topics. A set of articles is subsequently selected to be critically analyzed. The critical review is structured in steps for real-time detection and classification of PQDs, namely, input data preparation, preprocessing, transformation, feature extraction, feature selection, detection, classification, and characterization. Aspects associated with the type of disturbance(s) addressed in the literature are also explored throughout the review, including the perspectives of those studies aimed at multiple PQDs, or specifically focused on voltage sags or voltage notches. The real-time performance of the reviewed tools is also examined. Finally, unsolved issues are discussed, and prospects are highlighted.
This work presents a new control strategy for a DC–DC converter with dual active bridges used to interconnect two feeders in a DC microgrid. The proposed control strategy allows regulating the output voltage of the converter while maintaining the mean primary and secondary current value of the high-frequency transformer at zero, in order to avoid magnetic saturation. The controller is designed using the nonlinear control strategy based on feedback linearization, which is based on the converter generalized space-state averaged model. The performance of the proposed controller is validated via simulation and experimental results.
The resurgence of Western psychedelic research and practice has led to increasing concerns from many Indigenous Nations regarding cultural appropriation, lack of recognition of the sacred cultural positioning of these medicines, exclusionary practices in research and praxis, and patenting of traditional medicines. Indigenous voices and leadership have been notably absent from the Western psychedelic field currently widely represented by Westerners. An Indigenous-led globally represented group of practitioners, activists, scholars, lawyers, and human rights defenders came together with the purpose of formulating a set of ethical guidelines concerning traditional Indigenous medicines current use in Western psychedelic research and practice. A global Indigenous consensus process of knowledge-gathering was engaged which identified eight interconnected ethical principles, including: Reverence, Respect, Responsibility, Relevance, Regulation, Reparation, Restoration, and Reconciliation. A summary of the work is presented here with suggested ethical actions for moving forward within Western psychedelic research and practice spaces.
Background Mobile‐based assessment has been an active area of research in the field of mobile learning. Prior research has demonstrated that mobile‐based assessment systems positively affect student performance. However, it is still unclear why and how these systems positively affect student performance. Objectives This study aims to identify the determinants of student performance during students' use of a mobile‐based assessment application in a formative assessment activity as part of English as a Foreign Language (EFL) courses in higher education. Methods A structural model based on hypotheses was validated using PLS‐SEM with data from the interaction of 127 students of eight EFL courses from the A1 and A2 levels of English that used a mobile‐based assessment system for a period of 4 weeks. Automatic data collection in the application and self‐reported instruments were applied. Results and Conclusions Use of scaffolding mechanisms, time on‐task and reported effort are strong predictors of students' learning outcomes. The use of scaffolding strategies predicts students' time on‐task. The provision of corrective feedback is not a predictor of students' learning performance but predicts other constructs such as perceived usefulness and the behavioural intention to use. Implications Mobile‐based assessment systems should include scaffolding mechanisms and integrate strategies to increase the perceived relevance of the formative assessment activity to increase the student learning performance. Scaffolding mechanisms are also useful to increase the student time on‐task in the formative assessment activity. In mobile‐based formative assessment activities more elaborated forms of feedback other than corrective feedback are needed to increase student performance.
This paper deals with the problem regarding the optimal operation of photovoltaic (PV) generation sources in AC distribution networks with a single-phase structure, taking into consideration different objective functions. The problem is formulated as a multi-period optimal power flow applied to AC distribution grids, which generates a nonlinear programming (NLP) model with a non-convex structure. Three different objective functions are considered in the optimization model, each optimized using a single-objective function approach. These objective functions are (i) an operating costs function composed of the energy purchasing costs at the substation bus, added with the PV maintenance costs; (ii) the costs of energy losses; and (iii) the total CO2 emissions at the substation bus. All these functions are minimized while considering a frame of operation of 24 h, i.e., in a day-ahead operation environment. To solve the NLP model representing the studied problem, the General Algebraic Modeling System (GAMS) and its SNOPT solver are used. Two different test feeders are used for all the numerical validations, one of them adapted to the urban operation characteristics in the Metropolitan Area of Medellín, which is composed of 33 nodes, and the other one adapted to isolated rural operating conditions, which has 27 nodes and is located in the department of Chocó, Colombia (municipality of Capurganá). Numerical comparisons with multiple combinatorial optimization methods (particle swarm optimization, the continuous genetic algorithm, the Vortex Search algorithm, and the Ant Lion Optimizer) demonstrate the effectiveness of the GAMS software to reach the optimal day-ahead dispatch of all the PV sources in both distribution grids.
The design of an efficient energy management system (EMS) for monopolar DC networks with high penetration of photovoltaic generation plants is addressed in this research through a convex optimization point of view. The EMS is formulated as a multi-objective optimization problem that involves economic, technical, and environmental objective functions subject to typical constraints regarding power balance equilibrium, thermal conductor capabilities, generation source capacities, and voltage regulation constraints, among others, using a nonlinear programming (NLP) model. The main characteristic of this NLP formulation of the EMS for PV plants is that it is a nonconvex optimization problem owing to the product of variables in the power balance constraint. To ensure an effective solution to this NLP problem, a linear approximation of the power balance constraints using the McCormick equivalent for the product of two variables is proposed. In addition, to eliminate the error introduced by the linearization method, an iterative solution methodology (ISM) is proposed. To solve the multi-objective optimization problem, the weighted optimization method is implemented for each pair of objective functions in conflict, with the main advantage that in this extreme the Pareto front has the optimal global solution for the single-objective function optimization approach. Numerical results in the monopolar version of the IEEE 33-bus grid demonstrated that the proposed ISM reaches the optimal global solution for each one of the objective functions under analysis. It demonstrated that the convex optimization theory is more effective in the EMS design when compared with multiple combinatorial optimization methods.
Asbestos is a carcinogenic mineral banned in Colombia since January 1, 2021; however, there is a considerable amount of asbestos‐containing building materials (ACBM) installed in the country in products such as roof tiles, tanks, pipes and downspouts. Installed ACBM represent an exposure risk when the mineral fibers are released into the air due to deterioration, damage or disturbance of the cement matrix within which the asbestos is contained. Due to potential detrimental impacts on human health, safe management and correct handling of ACBM is a matter of vital importance. This paper proposes evidence‐based environmental management guidelines, aimed at public policy making, for the removal and final disposal of installed ACBM in Colombia. A descriptive study was carried out, with a qualitative approach, based on an integrative literature review of international practices applied in the removal and disposal of installed ACBM. Forty scientific publications were reviewed, as well as the regulations for removal, transport, and final disposal of installed asbestos‐cement from Australia, United States, Italy, Chile, United Kingdom, and Canada. Guidelines for the removal and final disposal of installed ACBM are proposed considering the following stages: a) diagnosis and management plan of installed ACBM, b) removal of installed ACBM, c) Transport of ACBM waste, and d) Final disposal of ACBM waste. Expert opinion was collected in order to assess the local feasibility of the proposed guidelines. These guidelines may help direct national and regional agencies to establish comprehensive strategies with clear, measurable, and achievable goals for future replacement of installed ACBM. This article is protected by copyright. All rights reserved. Integr Environ Assess Manag 2023;00:0–0.
This study addresses the problem of selecting the conductor sizes for medium-voltage distribution networks with radial configurations. The optimization model that represents this problem is part of the mixed-integer non-linear programming (MINLP) models, in which a power flow must be solved for each possible combination of conductor sizes. The main objective of this optimization problem is to find the best set of conductor sizes that minimize an economic objective function composed of the total costs of conducting materials added with the expected annual costs of the energy losses by proposing a new hybrid optimization methodology from the family of combinatorial optimization methods. To solve the MINLP model, a master–slave optimization method based on the modified version of the gradient-based metaheuristic optimizer (MGbMO) combined with the successive approximation power flow method for unbalanced distribution networks is presented. The MGbMO defines the set of conductor sizes assignable for each distribution line using an integer codification. The slave stage (three-phase power flow) quantifies the total power losses and their expected annual operating costs. Numerical results in the IEEE 8-, 27-, and 85-bus grids demonstrate the effectiveness of the proposed master–slave optimizer when compared with multiple combinatorial optimization methods (vortex search algorithm, the Newton-metaheuristic optimizer, the traditional and Chu and Beasley genetic algorithms, and the tabu search approaches). Two scenarios regarding the demand behavior were analyzed for the IEEE 8- and 27-bus grids: a peak load operation was considered, and, for the IEEE 85-bus grid, the daily demand behavior, including the presence of renewable generators, was considered. The 85-bus grid allowed showing that the most realistic operative scenario for selecting conductors is the case where a demand curve is implemented since reductions over 40% in the annual investment and operating costs were found when compared to the peak load operating condition. All numerical validations were performed in MATLAB software.
This paper presents an efficient master–slave methodology to solve the problem of integrating photovoltaic (PV) generators into DC grids for a planning period of 20 years. The problem is mathematically formulated as Mixed-Integer Nonlinear Programming (MINLP) with the objective of minimizing the total annual operating cost. The main stage, consisting of a discrete-continuous version of the Crow search algorithm (DCCSA), is in charge of determining the installation positions of the PV generators and their corresponding power ratings. On the other hand, at the slave level, the successive approximation power flow method is used to determine the objective function value. Numerical results on 33- and 69-bus test systems demonstrate the applicability, efficiency and robustness of the developed approach with respect to different methodologies previously discussed in the scientific literature, such as the vortex search algorithm, the generalized normal distribution optimizer and the particle swarm optimization algorithm. Numerical tests are performed in the MATLAB programming environment using proprietary scripts.
Establishing the indoor and outdoor humidity values in a greenhouse allows us to describe the crop yield during its entire developmental cycle. This study seeks to develop a predictive model of indoor relative humidity values in a greenhouse with high accuracy and interpretability through the use of optimized fuzzy inference systems, in order to offer greenhouse users a clear and simple description of their behaviour. The three-phase methodology applied made use of descriptive statistics techniques, correlation analysis, and prototyping paradigm for the iterative and incremental development of the predictive model, validated through error measurement. The research resulted in six models which define the behaviour of humidity as a result of temperature, CO2, and soil moisture, with percentages of effectiveness above 90%. The implementation of a Mamdani-type fuzzy inference system, optimized by a hybrid method combining genetic and interior point algorithms, allowed to predict the relative humidity in greenhouses with high interpretability and precision, with an effectiveness percentage of 90.97% and MSE (mean square error) of 8.2e − 3.
The problem regarding of optimal power flow in bipolar DC networks is addressed in this paper from the recursive programming stand of view. A hyperbolic relationship between constant power terminals and voltage profiles is used to resolve the optimal power flow in bipolar DC networks. The proposed approximation is based on the Taylors’ Taylor series expansion. In addition, nonlinear relationships between dispersed generators and voltage profiles are relaxed based on the small voltage voltage-magnitude variations in contrast with power output. The resulting optimization model transforms the exact nonlinear non-convex formulation into a quadratic convex approximation. The main advantage of the quadratic convex reformulation lies in finding the optimum global via recursive programming, which adjusts the point until the desired convergence is reached. Two test feeders composed of 21 and 33 buses are employed for all the numerical validations. The effectiveness of the proposed recursive convex model is verified through the implementation of different metaheuristic algorithms. All the simulations are carried out in the MATLAB programming environment using the convex disciplined tool known as CVX with the SEDUMI and SDPT3 solvers.
This research deals with the problem regarding the optimal siting and sizing of distribution static compensators (D-STATCOMs) via the application of a master–slave optimization technique. The master stage determines the nodes where the D-STATCOMs must be located and their nominal rates by applying the generalized normal distribution optimizer (GNDO) with a discrete–continuous codification. In the slave stage, the successive approximations power flow method is implemented in order to establish the technical feasibility of the solution provided by the master stage, i.e., voltage regulation and device capabilities, among other features. The main goal of the proposed master–slave optimizer is to minimize the expected annual operating costs of the distribution grid, which includes the energy loss and investment costs of the D-STATCOMs. With the purpose of improving the effectiveness of reactive power compensation during the daily operation of the distribution grid, an optimal reactive power flow (ORPF) approach is used that considers the nodes where D-STATCOMs are located as inputs in order to obtain their daily expected dynamical behavior with regard to reactive power injection to obtain additional net profits. The GNDO approach and the power flow method are implemented in the MATLAB programming environment, and the ORPF approach is implemented in the GAMS software using a test feeder comprising 33 nodes with both radial and meshed configurations. A complete comparative analysis with the Salp Swarm Algorithm is presented in order to demonstrate the effectiveness of the proposed two-stage optimization approach in the fixed operation scenario regarding the final objective function values. In addition, different tests considering the possibility of hourly power injection using D-STATCOMs through the ORPF solution demonstrate that additional gains can be obtained in the expected annual operative costs of the grid.
Due to the increasing demand for electricity around the world, different technologies have been developed to ensure the sustainability of each and every process involved in its production, transmission, and consumption. In addition to ensuring energy sustainability, these technologies seek to improve some of the characteristics of power systems and, in doing so, make them efficient from a financial, technical, and environmental perspective. In particular, solar photovoltaic (PV) technology is one of the power generation technologies that has had the most influence and development in recent years due to its easy implementation and low maintenance costs. Additionally, since PV systems can be located close to the load, power losses during distribution and transmission can be significantly reduced. However, in order to maximize the financial, technical, and environmental variables involved in the operation of an electrical system, a PV power generation project must guarantee the proper location and sizing of the generation sources. In the specialized literature, different studies have employed mathematical methods to determine the optimal location and size of generation sources. These methods model the operation of electrical systems and provide potential analysis scenarios following the deployment of solar PV units. The majority of such studies, however, do not assess the quality and repeatability of the solutions in short processing times. In light of this, the purpose of this study is to review the literature and contributions made in the field.
Given the importance of renewable energy sources in distribution systems, this article addresses the problem of locating and determining the capacity of these sources, namely, wind turbines and solar panels. To solve this optimization problem, a new algorithm based on the behavior of salp is used. The objective functions include reducing losses, improving voltage profiles, and reducing the costs of renewable energy sources. In this method, the allocation of renewable resources is considered for different load models in distribution systems and different load levels using smart meters. Due to the fact that these objective functions are multi-objective, the fuzzy decision-making method is used to select the optimal solution from the set of Pareto solutions. The considered objective functions lead to loss reduction, voltage profile improvement, and RES cost reduction (A allocating RES resources optimally without resource limitations; B: allocating RES resources optimally with resource limitations). In addition, daily wind, solar radiation, and temperature data are taken into account. The proposed method is applied to the IEEE standard 33-bus system. The simulation results show the better performance of the multi-objective salp swarm algorithm (MSSA) at improving voltage profiles and reducing losses in distribution systems. Lastly, the optimal results of the MSSA algorithm are compared with the PSO and GA algorithms.
La dosimetría Fricke es una técnica con múltiples aplicaciones, desde la industria alimentaria hasta la medicina. En las actividades cotidianas de la planta de irradiación gamma del Servicio Geológico Colombiano (SGC) se usan diferentes sistemas dosimétricos para el estudio de las dosis absorbidas en los materiales, entre ellos el sistema dosimétrico Fricke. En este trabajo se realizó la comparación de la dosis absorbida determinada en el Laboratorio Secundario de Calibración Dosimétrica (LSCD), a partir de patrones trazables calibrados en el Organismo Internacional de Energía Atómica (OIEA), y la aplicación del protocolo IAEA TRS 398, en el irradiador G100 Hopewell Design que contiene una fuente de Co-60, con las dosis obtenidas mediante el sistema Fricke usado rutinariamente. Se aplicaron los protocolos establecidos en la norma ISO-STM 51026 “Práctica estándar para el uso del sistema de dosimetría Fricke”. Se elaboró la curva de calibración de los dosímetros Fricke, de la cual se obtuvo el parámetro experimental para el cálculo de la dosis absorbida en las condiciones ambientales particulares de la instalación; el sistema mostró un comportamiento lineal en el rango de 100 Gy a 350 Gy. Teniendo en cuenta el valor experimental de dosis obtenido con dicho sistema, se realizó la intercomparación con la dosis conocida determinada en el LSCD obteniendo una diferencia en el rango de dosis mencionado no mayor al 3,7 % y una incertidumbre del 3 % con una confiabilidad del 95 %. A partir del valor experimental encontrado y que la instalación planta irradiación gamma se encuentra en similares condiciones ambientales que el laboratorio secundario de calibración y que los irradiadores de estas instalaciones suministran sus dosis de manera similar, se puede construir un procedimiento propio para el cálculo de la dosis, el cual permitirá mayor precisión y operatividad a la dosimetría de la instalación.
The pollution associated with road runoff water can generate significant impacts on the receiving natural environment due to the significant masses mobilized under certain climate, morphological, and anthropic conditions. The aim of this paper is to show an analysis of the possible surrogate conventional physicochemical parameters of pollution by heavy metals (HMs) in urban road runoff. The best surrogate physicochemical parameters are detected by a differentiated analysis between the HM concentrations (Fe, Al, As, Ba, Cd, Co, Cu, Cr, Mn, Hg, Ni, Pb, V, and Zn) in the total, particulate, and dissolved fractions. This analysis is also performed under two scenarios of runoff event energy according to the mobilized TSS load. The results suggested that it was easier to detect surrogate parameters for total HM concentrations during higher-energy runoff events. The outcomes hinted that regardless of the runoff event energy, it was easier to detect conventional surrogate parameters for the particulate HM concentration compared to the dissolved HM concentration. The findings showed for total HM concentration that the best surrogate parameter during higher-energy runoff events was TSS. The best surrogate HM during these runoff events was Fe. The results also suggested that HMs with high percentages of association with the particulate fraction (>70%) of road runoff were the best surrogates for the other HMs under study. For lower-energy runoff events, the best surrogate parameter was VSS, although TSS also showed good behavior.
The black hole optimization (BHO) method is applied in this research to solve the problem of the optimal reactive power compensation with fixed-step capacitor banks in three-phase networks considering the phase-balancing problem simultaneously. A master–slave optimization approach based on the BHO in the master stage considers a discrete codification and the successive approximation power flow method in the slave stage. Two different evaluations are proposed to measure the impact of the shunt reactive power compensation and the phase-balancing strategies. These evaluations include a cascade solution methodology (CSM) approach and a simultaneous solution methodology (SSM). The CSM approach solves the phase-balancing problem in the first stage. This solution is implemented in the distribution network to determine the fixed-step capacitor banks installed in the second stage. In the SSM, both problems are solved using a unique codification vector. Numerical results in the IEEE 8- and IEEE 27-bus systems demonstrate the effectiveness of the proposed solution methodology, where the SSM presents the better numerical results in both test feeders with reductions of about 32.27% and 33.52%, respectively, when compared with the CSM. To validate all the numerical achievements in the MATLAB programming environment, the DIgSILENT software was used for making cross-validations. Note that the selection of the DIgISLENT software is based on its wide recognition in the scientific literature and industry for making quasi-experimental validations as a previous stage to the physical implementation of any grid intervention in power and distribution networks.
This document presents a master–slave methodology for solving the problem of optimal operation of photovoltaic (PV) distributed generators (DGs) in direct current (DC) networks. This problem was modeled using a nonlinear programming model (NLP) that considers the minimization of three different objective functions in a daily operation of the system. The first one corresponds to the minimization of the total operational cost of the system, including the energy purchasing cost to the conventional generators and maintenance costs of the PV sources; the second objective function corresponds to the reduction of the energy losses associated with the transport of energy in the network, and the third objective function is related to the minimization of the total emissions of CO2 by the conventional generators installed on the DC grid. The minimization of these objective functions is achieved by using a master–slave optimization approach through the application of the Vortex Search algorithm combined with a matrix hourly power flow. To evaluate the effectiveness and robustness of the proposed approach, two test scenarios were used, which correspond to a grid-connected and a standalone network located in two different regions of Colombia. The grid-connected system emulates the behavior of the solar resource and power demand of the city of Medellín-Antioquia, and the standalone network corresponds to an adaptation of the generation and demand curves for the municipality of Capurganá-Choco. A numerical comparison was performed with four optimization methodologies reported in the literature: particle swarm optimization, multiverse optimizer, crow search algorithm, and salp swarm algorithm. The results obtained demonstrate that the proposed optimization approach achieved excellent solutions in terms of response quality, repeatability, and processing times.
En el artículo se analiza la progresión desde la desigualdad social hacia la vulnerabilidad frente a eventos climáticos extremos, en la comunidad de la vereda Escalones en Boyacá, Colombia. Esta es una investigación exploratoria en la que se usaron grupos focales, a los que se les aplicó una adaptación de la herramienta de clasificación de bienestar de la Comisión Nacional de Áreas Naturales Protegidas y la Deutsche Gesellschaft für Internationale Zusammenarbe it, y el análisis se hizo en el marco del enfoque teórico del modelo PAR. Se encontró que las desigualdades sociales aumentan la vulnerabilidad de los habitantes de la vereda, ya que se manifiestan en condiciones inseguras que aumentan la posibilidad de que una amenaza climática se convierta en un desastre.
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