The developed work in this paper is a part of the detection and identification of faults in systems by modern techniques of artificial intelligence, in a first step we have developed an MLP (Multi- Layer Perceptron ), type neural network to detect shunt faults and shading phenomenon in photovoltaic systems, and in the second part of the work we developed another RNN (recurrent neural networks ) type network in order to identify single and combined faults in PV systems. The results obtained clearly show the performance of the networks developed for the rapid detection of the appearance of faults with the estimation of their times as well as the robust decision to identify the type of faults in the PV system.

Mobile telecommunication sites are an essential station in our technological life, used to allow the communication through mobiles and internet. Many telecommunication sites are installed in remote areas where the grid is not available. For this, hybrid renewable energy systems (HRES) are used to power the stations and integrate the remote areas with the world. This article aims to evaluate the performance of the existing HRES of the remote mobile telecommunication station of Bougaroun, Collo, Algeria -which consists of PV modules, batteries and diesel generator (DG)- and to develop it using a mathematical model to demonstrate the effect of deploying a wind turbine to supply more green energy, minimize the operation cost (fuel consumption and maintenance), and reduce the greenhouses emitted by the DG. Based on the wind data at the site location, the obtained results show a significant amount of output power that can be used to minimize the DG functionality.

No abstract

The developed work in this paper is a part of the detection and identification of faults in systems by modern techniques of artificial intelligence. In a first step we have developed amulti-layer perceptron (MLP), type neural network to detect shunt faults and shading phenomenon in photovoltaic (PV) systems, and in the second part of the work we developed anotherrecurrent neural network (RNN) type network in order to identify single and combined faults in PV systems. The results obtained clearly show the performance of the networks developed for the rapid detection of the appearance of faults with the estimation of their times as well as the robust decision to identify the type of faults in the PV system.

No abstract