Solución de un modelo de arreglos fotovoltaicos serie-paralelo utilizando algoritmos de optimización global

Models of series-parallel (SP) photovoltaic (PV) arrays focus on the system of nonlinear equations that represents the array’s electrical behavior. The solution of the system of nonlinear equations can be posed as an optimization problem and solved with different methods; however, the models do not formulate the optimization problem and do not evaluate different optimization algorithms for its solution. This paper proposes a solution, using global optimization algorithms, of the mathematical model that describes the electrical behavior of a SP generator, operating under uniform and partial shading conditions. Such a model is constructed by dividing the generator into strings and representing each module in the string with the single-diode model. Consequently, for each string a system of nonlinear equations is build applying the Kirchhoff’s laws, where the unknowns are the modules’ voltages. The solution of the resulting nonlinear equation system is posed as an optimization problem, where the objective function is defined as the sum of the squared of each nonlinear equation. Minimum and maximum values of each voltage are defined from the datasheet information of the modules and bypass diodes. As a demonstrative example, we arbitrarily select two well-known algorithms to solve this problem: Genetic Algorithms and Particle Swarm Optimization. Simulation results show that both algorithms solve the optimization problem and allow the reproduction of the generator’s characteristic curves. Moreover, the results also indicate that the optimization problem is correctly defined, which opens the possibility explore other optimization algorithms to reduce the computation time.Los modelos de arreglos fotovoltaicos (FV) en serie-paralelo (SP) se enfocan en el sistema de ecuaciones no lineales que represental comportamiento eléctrico del arreglo. La solución del sitemas de ecuaciones se puede plantear como un problema de optimización y resolverse con diferentes métodos; sin embargo, los modelos no formulan el problema de optimización y no evaluan diferentes algoritmos de optimización para su solución. Este artículo propone una solución, utilizando algoritmos de optimización global, del modelo matemático que describe el comportamiento eléctrico de un generador fotovoltaico en serie-paralelo, que opera bajo condiciones uniformes y de sombreados parciales. Dicho modelo se construye dividiendo el generador en cadenas y representando cada módulo en la cadena con el modelo de diodo-único. En consecuencia, para cada cadena se construye un sistema de ecuaciones no lineales aplicando las leyes de Kirchhoff, en donde las incógnitas son los voltajes de los módulos. La solución del sistema de ecuaciones no lineales resultante se plantea como un problema de optimización, donde la función objetivo se define como la suma del cuadrado de cada ecuación no lineal. Los valores mínimos y máximos de cada voltaje se definen a partir de la información de la hoja de datos de los módulos y de los diodos de derivación. Como ejemplo demostrativo, se seleccionaron arbitrariamente dos algoritmos bien conocidos para resolver este problema: Algoritmos Genéticos y Optimización por Enjambre de Partículas. Los resultados de simulación muestran que los dos algoritmos ambos algoritmos resuelven el problema de optimización y permiten la reproducción de las curvas características del generador. Adicionalmente, los resultados también indican que el problema de optimización se definió correctamente, lo cual abre la posibilidad de explorar otros algoritmos de optimización para reducir el tiempo de cómputo

Solución de un modelo de arreglos fotovoltaicos serie-paralelo utilizando algoritmos de optimización global

Models of series-parallel (SP) photovoltaic (PV) arrays focus on the system of nonlinear equations that represents the array’s electrical behavior. The solution of the system of nonlinear equations can be posed as an optimization problem and solved with different methods; however, the models do not formulate the optimization problem and do not evaluate different optimization algorithms for its solution. This paper proposes a solution, using global optimization algorithms, of the mathematical model that describes the electrical behavior of a SP generator, operating under uniform and partial shading conditions. Such a model is constructed by dividing the generator into strings and representing each module in the string with the single-diode model. Consequently, for each string a system of nonlinear equations is build applying the Kirchhoff’s laws, where the unknowns are the modules’ voltages. The solution of the resulting nonlinear equation system is posed as an optimization problem, where the objective function is defined as the sum of the squared of each nonlinear equation. Minimum and maximum values of each voltage are defined from the datasheet information of the modules and bypass diodes. As a demonstrative example, we arbitrarily select two well-known algorithms to solve this problem: Genetic Algorithms and Particle Swarm Optimization. Simulation results show that both algorithms solve the optimization problem and allow the reproduction of the generator’s characteristic curves. Moreover, the results also indicate that the optimization problem is correctly defined, which opens the possibility explore other optimization algorithms to reduce the computation time.Los modelos de arreglos fotovoltaicos (FV) en serie-paralelo (SP) se enfocan en el sistema de ecuaciones no lineales que represental comportamiento eléctrico del arreglo. La solución del sitemas de ecuaciones se puede plantear como un problema de optimización y resolverse con diferentes métodos; sin embargo, los modelos no formulan el problema de optimización y no evaluan diferentes algoritmos de optimización para su solución. Este artículo propone una solución, utilizando algoritmos de optimización global, del modelo matemático que describe el comportamiento eléctrico de un generador fotovoltaico en serie-paralelo, que opera bajo condiciones uniformes y de sombreados parciales. Dicho modelo se construye dividiendo el generador en cadenas y representando cada módulo en la cadena con el modelo de diodo-único. En consecuencia, para cada cadena se construye un sistema de ecuaciones no lineales aplicando las leyes de Kirchhoff, en donde las incógnitas son los voltajes de los módulos. La solución del sistema de ecuaciones no lineales resultante se plantea como un problema de optimización, donde la función objetivo se define como la suma del cuadrado de cada ecuación no lineal. Los valores mínimos y máximos de cada voltaje se definen a partir de la información de la hoja de datos de los módulos y de los diodos de derivación. Como ejemplo demostrativo, se seleccionaron arbitrariamente dos algoritmos bien conocidos para resolver este problema: Algoritmos Genéticos y Optimización por Enjambre de Partículas. Los resultados de simulación muestran que los dos algoritmos ambos algoritmos resuelven el problema de optimización y permiten la reproducción de las curvas características del generador. Adicionalmente, los resultados también indican que el problema de optimización se definió correctamente, lo cual abre la posibilidad de explorar otros algoritmos de optimización para reducir el tiempo de cómputo

Algoritmos de optimización global aplicados en una estrategia de estimación de parámetros

Este artículo presenta un estudio comparativo utilizando dos algoritmos de optimización global, el de Optimización por Campo Electromagnético (EFO) y el de Búsqueda por Transferencia de Calor (HTS). Estas técnicas alternativas son eficientes cuando los métodos clásicos encuentran limitaciones para resolver problemas reales. Para verificar el desempeño de los métodos, se implementó el diseño de un disipador de calor de microcanales rectangulares formulando el respectivo problema inverso de transferencia de calor (IHTP). Los resultados experimentales se compararon competitivamente con los resultados tradicionales de Levenberg-Marquardt (LM). Además, los algoritmos globales lograron estimaciones con errores inferiores al 5%, y convergieron al menos tres veces más rápido que LM.This article presents a comparative study using two global optimization algorithms, Electromagnetic Field Optimization (EFO) and Heat Transfer Search (HTS). These techniques are efficient alternatives when classical methods find limitations to solve real problems. To verify methods performance, the rectangular microchannel heat sink design was implemented formulating the respective  Inverse Heat Transfer Problem (IHTP). Experimental results were competitively compared with the traditional Levenberg-Marquardt (LM) outcomes. Moreover, global algorithms achieved estimations with errors lower than 5%, and they converged at least three times faster than LM. &nbsp

IMPLEMENTACIÓN EN FPGA DE UN CLASIFICADOR DE MOVIMIENTOS DE LA MANO USANDO SEÑALES EMG

Este trabajo presenta el diseño e implementación de un clasificador de señales electromiográficas (EMG) para tres movimientos de la mano: flexión, extensión y cierre, usando dos músculos del antebrazo, palmar largo y extensor común de los dedos. El desarrollo comprende dos bloques principales, el hardware para la adquisición y adecuación de la señales EMG analógicas y el sistema de procesamiento para la identificación y clasificación del movimiento realizado; el sistema completo fue implementado en hardware usando un kit de desarrollo DE2-70 que cuenta con un FPGA Cyclone II de Altera. Para la extracción de características se implementó la Transformada Rápida de Fourier (FFT), para cada canal, a la cual se le calcularon  técnicas de procesamiento  como la varianza y el promedio.. Finalmente, se establece un umbral de decisión para identificar el movimiento realizado. El tiempo de respuesta del sistema total fue de 17,7 us, obteniendo una tasa de identificación mayor al 87%.FPGA implementation of a hand motions classifier using EMG signalsAbstractThis paper presents the design and implementation of a hand motions classifier using electromyographic (EMG) signals. The classified motions are: wrist flexion, wrist extension and hand closure. The motions are classified using two forearm muscles: longus palmar and extensor digitorum. This work was implemented in two principal blocks: the acquisition and adequacy of the EMG signal, and the processing system for the identification and classification of the motion made. The processing system was implemented on hardware using a development kit with a Cyclone II FPGA from Altera. For the feature extraction the Fast Fourier Transform (FFT) is performed at each channel and some features like variance and mean are calculated. Finally, a threshold decision block is used to identify the motion. The system have a time response of 17,7 us, obtaining an identification rate higher than 87%.Keywords: EMG signals, FPGA, motion classifier, real time

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Development of a remote sensing and control system for greenhouse applications

Real-time monitoring provides reliable, timely information of crop and soil status, important in taking decisions for crop production improvement. This work presents a real-time monitoring and control system for climatological variables in greenhouse. The system has wireless communication capabilities, which allow it to cover extensive surfaces in real-time, without extra resources. The system implementation is based on the micro controllers “PIC18F4550” and “DSPIC 30F5011”, user interface was programmed under LINUX. The proposed system performance was compared with commercial Data Loggers, readings present a linear adjustment with R2=0.9656

Development of a remote sensing and control system for greenhouse applications

Real-time monitoring provides reliable, timely information of crop and soil status, important in taking decisions for crop production improvement. This work presents a real-time monitoring and control system for climatological variables in greenhouse. The system has wireless communication capabilities, which allow it to cover extensive surfaces in real-time, without extra resources. The system implementation is based on the micro controllers “PIC18F4550” and “DSPIC 30F5011”, user interface was programmed under LINUX. The proposed system performance was compared with commercial Data Loggers, readings present a linear adjustment with R2=0.9656

Acute aquatic toxicity to zebrafish and bioaccumulation in marine mussels of antimony tin oxide nanoparticles

Antimony tin oxide (Sb2O5/SnO2) is effective in the absorption of infrared radiation for applications, such as skylights. As a nanoparticle (NP), it can be incorporated into films or sheets providing infrared radiation attenuation while allowing for a transparent final product. The acute toxicity exerted by commercial Sb2O5/SnO2 (ATO) NPs was studied in adults and embryos of zebrafish (Danio rerio). Our results suggest that these NPs do not induce an acute toxicity in zebrafish, either adults or embryos. However, some sub-lethal parameters were altered: heart rate and spontaneous movements. Finally, the possible bioaccumulation of these NPs in the aquacultured marine mussel Mytilus sp. was studied. A quantitative analysis was performed using single particle inductively coupled plasma mass spectrometry (sp-ICP-MS). The results indicated that, despite being scarce (2.31 × 106 ± 9.05 × 105 NPs/g), there is some accumulation of the ATO NPs in the mussel. In conclusion, commercial ATO NPs seem to be quite innocuous to aquatic organisms; however, the fact that some of the developmental parameters in zebrafish embryos are altered should be considered for further investigation. More in-depth analysis of these NPs transformations in the digestive tract of humans is needed to assess whether their accumulation in mussels presents an actual risk to humans.Fundação para a Ciência e Tecnologia | Ref. 2020.04021.CEECIN

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO

Performance of CMS muon reconstruction in pp collision events at sqrt(s) = 7 TeV

The performance of muon reconstruction, identification, and triggering in CMS has been studied using 40 inverse picobarns of data collected in pp collisions at sqrt(s) = 7 TeV at the LHC in 2010. A few benchmark sets of selection criteria covering a wide range of physics analysis needs have been examined. For all considered selections, the efficiency to reconstruct and identify a muon with a transverse momentum pT larger than a few GeV is above 95% over the whole region of pseudorapidity covered by the CMS muon system, abs(eta) < 2.4, while the probability to misidentify a hadron as a muon is well below 1%. The efficiency to trigger on single muons with pT above a few GeV is higher than 90% over the full eta range, and typically substantially better. The overall momentum scale is measured to a precision of 0.2% with muons from Z decays. The transverse momentum resolution varies from 1% to 6% depending on pseudorapidity for muons with pT below 100 GeV and, using cosmic rays, it is shown to be better than 10% in the central region up to pT = 1 TeV. Observed distributions of all quantities are well reproduced by the Monte Carlo simulation.Comment: Replaced with published version. Added journal reference and DO