The problem of parametric estimation in single-phase transformers is addressed in this research from the point of view of metaheuristic optimization. The parameters of interest are the series resistance and reactance as well as the magnetization resistance and reactance. To obtain these parameters considering only the voltage and the currents measured in the terminals of the transformer, a nonlinear optimization model that deals with the minimization of the mean square error among the measured and calculated voltage and current variables is formulated. The nonlinear programming model is solved through the implementation of a simple but efficient metaheuristic optimization technique known as the black-hole optimizer. Numerical simulations demonstrate that the proposed optimization method allows for the reduction in the estimation error among the measured and calculated variables when compared with methods that are well established in the literature such as particle swarm optimization and genetic algorithms, among others. All the simulations were carried out in the MATLAB programming environment.
This paper deals with the problem of the optimal placement and sizing of distributed generators (DGs) in alternating current (AC) distribution networks by proposing a hybrid master–slave optimization procedure. In the master stage, the discrete version of the sine–cosine algorithm (SCA) determines the optimal location of the DGs, i.e., the nodes where these must be located, by using an integer codification. In the slave stage, the problem of the optimal sizing of the DGs is solved through the implementation of the second-order cone programming (SOCP) equivalent model to obtain solutions for the resulting optimal power flow problem. As the main advantage, the proposed approach allows converting the original mixed-integer nonlinear programming formulation into a mixed-integer SOCP equivalent. That is, each combination of nodes provided by the master level SCA algorithm to locate distributed generators brings an optimal solution in terms of its sizing; since SOCP is a convex optimization model that ensures the global optimum finding. Numerical validations of the proposed hybrid SCA-SOCP to optimal placement and sizing of DGs in AC distribution networks show its capacity to find global optimal solutions. Some classical distribution networks (33 and 69 nodes) were tested, and some comparisons were made using reported results from literature. In addition, simulation cases with unity and variable power factor are made, including the possibility of locating photovoltaic sources considering daily load and generation curves. All the simulations were carried out in the MATLAB software using the CVX optimization tool.
This paper deals with the problem of optimal location and reallocation of battery energy storage systems (BESS) in direct current (dc) microgrids with constant power loads. The optimization model that represents this problem is formulated with two objective functions. The first model corresponds to the minimization of the total daily cost of buying energy in the spot market by conventional generators and the second to the minimization of the costs of the daily energy losses in all branches of the network. Both the models are constrained by classical nonlinear power flow equations, distributed generation capabilities, and voltage regulation, among others. These formulations generate a nonlinear mixed-integer programming (MINLP) model that requires special methods to be solved. A dc microgrid composed of 21-nodes with existing BESS is used for validating the proposed mathematical formula. This system allows to identify the optimal location or reallocation points for these batteries by improving the daily operative costs regarding the base cases. All the simulations are conducted via the general algebraic modeling system, widely known as the General Algebraic Modeling System (GAMS).
Autor a quien debe ser dirigida la correspondencia. ResumenSe describen los elementos fundamentales que componen la radio cognitiva y se analiza la asignación de bandas espectrales. Dentro de los principales problemas en la inserción de nuevas aplicaciones y tecnologías inalámbricas, está la falta de espectro radioeléctrico para su asignación, debido a la manera ineficiente de distribución del espectro radioeléctrico disponible, que es actualmente asignado de forma estática. La radio cognitiva nace como un método que propone una solución a la problemática, gestionando dinámicamente el recurso. Una de las etapas que integran esta tecnología es la decisión de espectro, en la que se selecciona y asigna las bandas frecuenciales a partir de los requisitos de calidad de servicio de los usuarios cognitivos. Este artículo plantea una propuesta para el desarrollo de un proyecto de investigación que optimice el proceso de selección de canales reduciendo el tiempo de asignación para elevar su rendimiento. Palabras clave: radio cognitiva, toma de decisiones espectrales, asignación estática, asignación dinámicaAbstract Fundamental elements of the cognitive radio and assignment of spectral bands are described and analyzed. Among the main problems in the insertion of new applications and wireless technologies is the lack of radio spectrum for allocation due to the inefficient distribution of available radio spectrum, which is statically allocated at present. The cognitive radio paradigm was born as a method for proposing a solution to the problem, dynamically managing the resource. One of the stages that make up this technology is the decision of the spectrum that selects and allocates frequency bands based on quality of service requirements of the cognitive users. This article presents a proposal for the development of a research project that optimizes the selection of channel allocation by reducing the time to improve their performance.
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