Abstract
In this paper, a monitoring method for DC-DC converters in photovoltaic applications is presented. The primary goal is to prevent catastrophic failures by detecting malfunctioning conditions during the operation of the electrical system. The proposed prognostic procedure is based on machine learning techniques and focuses on the variations of passive components with respect to their nominal range. A theoretical study is proposed to choose the best measurements for the prognostic analysis and adapt the monitoring method to a photovoltaic system. In order to facilitate this study, a graphical assessment of testability is presented, and the effects of the variable solar irradiance on the selected measurements are also considered from a graphical point of view. The main technique presented in this paper to identify the malfunction conditions is based on a Multilayer neural network with Multi-Valued Neurons. The performances of this classifier applied on a Zeta converter are compared to those of a Support Vector Machine algorithm. The simulations carried out in the Simulink environment show a classification rate higher than 90%, and this means that the monitoring method allows the identification of problems in the initial phases, thus guaranteeing the possibility to change the work set-up and organize maintenance operations for DC-DC converters.
Highlights
The development of smart cities leads to an increase in the complexity of electrical grids, and new challenges need to be addressed, such as the spread of electric vehicles and the management of renewable energy systems [1,2]
The control of DC-DC converters represents a very important aspect because they can be used as an interface with renewable energy systems producing a Direct Current (DC) and are essential for all those systems powered by batteries, such as electric vehicles
The results reported in the previous paragraph show excellent performances of the layer allows a classification rate
Summary
The development of smart cities leads to an increase in the complexity of electrical grids, and new challenges need to be addressed, such as the spread of electric vehicles and the management of renewable energy systems [1,2] In this sense, new devices, control techniques and monitoring methods are needed for proper energy management [3,4,5,6,7]. The technical optimization of the new electrical generators allows an increase in efficiency for renewable systems, but it is not sufficient for the correct distribution of this energy, which is difficult to predict and highly variable [8,9] For this reason, the development of new algorithms capable of predicting production from renewable sources and managing electrical loads will be a very important field of interest for many researchers [10,11,12]. In addition to controlling these devices, it is very important to monitor their state of health during the operation of the electrical system
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