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.

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.

Hybrid renewable energy systems (HRESs) are increasingly used because they integrate diverse energy resources whose availability can complement one another over the day–night cycle. However, the high cost of system components, particularly batteries, and the dependence of energy generation on weather conditions can undermine their economic viability. Researchers, therefore, seek to optimize HRES configurations to achieve a balance between reliability and lifecycle cost by performing techno-economic studies using different techniques. One of the industries that started relying more on renewable energy is the mobile telecommunication industry, especially in the remote stations where the expansion of the grid is expensive and the renewable resources are abundant. This study introduces the scope of hybrid renewable energy system optimization and applies a study case to a remote telecommunication station where the existing energy system was installed without prior techno-economic assessment. Using mathematical models, single and multi-objective (lexicographic) optimizations are conducted using the particle swarm optimization algorithm and a deterministic exhaustive search numerical algorithm, to minimize the lifecycle cost of the system, maintain reliability for a full continuity of service, and reduce the dependency to the diesel generator to contribute to the decarbonization objective of the world. The computational results show a significant reduction in the cost of the system from USD 525,512.83 for the existing system to approximately USD 197,879.03 (reduction of 62.3% of the actual cost), and the operating hours of the diesel generator from 1401 h/year to 770 h/year. More details are presented in the Discussion section.