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This paper proposes an intelligent fault diagnosis technique to detect and classify possible faults occurring in Photovoltaic strings, based on the analysis of the symptoms observed in the I-V characteristic. The technique consists of two algorithms: The first one allows the classification of faults that have not the same symptoms evolution; whi le for the second one, two Artificial Neural Networks...
This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current–voltage (I–V) characteristics of the PV strings are calculated using a simulation model. The...
This paper proposes a simple automatic technique for fault diagnosis in photovoltaic (PV) arrays, based on the analysis of the an omalies observed in the I-V characteristic. Firstly, the I-V characteristic of the PV array is simulated using Matlab/Simscape tool for different faulty conditions; which is experimentally validated by generating different faults on a PV string installed at the Renewable...
In this work, an automatic fault detection method for grid-connected photovoltaic (GCPV) plants is presented. The proposed method generates a diagnostic signal which indicates possible faults occurring in the GCPV plant. In order to determine the location of the fault, the ratio between DC and AC power is monitored. The software tool developed identifies different types of faults like: fault in a...
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