SummaryEven with significant progresses in the maximum power point (MPP) research area, the necessity to improve the existing methods becomes mandatory to increase the energy conversion efficiency. Since the power–voltage (P‐V) characteristic curve of photovoltaic (PV) arrays has multiple peaks under partially shaded (PS) conditions, the conventional MPP tracking (MPPT) control methods have the difficult challenge of locate the global MPP (GMPP) among many local MPPs (LMPPs). In recent years, numerous research papers have been focused on techniques to efficiently track the GMPP and alleviate the partial shading effects. One of the most popular evolutionary search technique is particle swarm optimization (PSO) that provides high tracking speed and the ability operate under different environmental conditions. For solving some conventional PSO technique common weaknesses, several modifications and improvements have emerged in the past years. This paper provides a comparative and comprehensive review of some relevant PSO‐based methods taking into account the effects of important key issues such as particles initialization criteria, search space, convergence speed, initial parameters, performance with and without partial shading, and efficiency. The simulation results are validated under numerous test conditions using MATLAB code and Simulink package.
The design and features of a Matlab's application to support applied researches for serial time computing is presented. The input data can be from historical record coming from chemical and thermal processes and also it can be generated by simulation. Up to 8 signals, linearly normalized and distributed can be visualized on an axis Matlab's object. By means of two cursors, the user can choose short windows of recorded signals. On this serial time sections, in this version, statisticians are computed and they facilitate the static modeling. They can be saved into an Excel file. It is an opened software application permitting to include new features. The Windows between 2 cursors command facilities the dynamic modeling. Its applicability is exemplified by times series from industry (from a 250 MW thermal power plant) and generated by simulation.Keywords: process monitoring, data-driven modeling, quasi-stationary states, chemometric methods, steam generators. Multi-variable Industrial: Una solución con Matlab para minar series temporales RESUMENSe presenta el diseño y las prestaciones de una aplicación desarrollada en Matlab orientada a dar soporte de cómputo a la exploración y extracción de información a partir de series temporales. La data de entrada podrá ser de un registro histórico de un proceso químico y termo energético y aquellos generados de experimentación simulada. La visualización es de hasta 8 señales linealmente normalizadas y distribuidas a lo alto del objeto axis. El usuario podrá seleccionar ventanas cortas de registro mediante dos cursores. Sobre estas secciones de series temporales, en esta versión, se computan estadígrafos que facilitan el modelado estático. Estos podrán ser salvados en un fichero Excel. Es una aplicación abierta permitiendo la inclusión de nuevas prestaciones. El comando Windows entre dos cursores facilita el modelado dinámico. Su aplicabilidad se ejemplifica con series temporales de la industria (de una central térmica de 250 MW) y generadas por simulación.Palabras Claves: Monitoreo de procesos, modelado basado en datos, estados cuasi-estacionarios, métodos químico-métricos, generadores de vapor. INTRODUCCIÓNLa evolución de los sistemas de automatización hacia aquellos denominados de Control Distribuido (del inglés; DCS) y el software instalado en los computadores de supervisión [1], así como la facilidad de establecer compactos sistemas registradores de datos (del inglés; Data logger) conducen a la generación de grandes volúmenes de datos.La supervisión de procesos se encarga de observar continuamente las variables del proceso en busca de la detección de anormalidades que puedan representar un problema operativo o de calidad. A esta se le destinan como sub-tareas la detección y diagnóstico de fallos y el análisis de procesos. Dependiendo del horizonte de tiempo con el que se trabaja, la supervisión se puede aplicar a 2 niveles:A corto plazo: En este nivel las variables del proceso se observan continuamente. La meta es detectar cualquier desviación con respecto al esta...
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