Analysis of relevant variables to monitor a photovoltaic charging station through the Function to Data Matrix (FDM) method
Fecha
2018-01-01
Autores
Cárdenas-Gómez I.
Fernández-Montoya M.
Mejía-Gutiérrez R.
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Editor
Institution of Engineering and Technology
Resumen
The growth of the electric vehicle industry has brought the development of charging stations and the need for good performance of such systems. The large amount of information that can be monitored in these systems can represent a problem for a good operation in terms of control, computational cost and time. For this reason, it is necessary to make a selection of variables that allows to decrease the data-set’s size without compromising the quality of information, needed for a proper information management system. There are several methods for prioritizing variables, such as the Function to Data Matrix (FDM). This method takes into account the functional analysis of the system, as well as the operative states and their relationship with the basic functions and variables. This enables to obtain a Variable Relevance Indicator (VRI) to define which variables have a higher importance under a particular perspective based on the main function of a system. This article presents the process of analyzing a photovoltaic charging station through the FDM method in order to define the most relevant information to be deployed in a future remote monitoring system. © 2018 Institution of Engineering and Technology. All rights reserved.