This paper proposes an innovative approach to classify the losses related to photovoltaic (PV) systems, through the use of thermographic non-destructive tests (TNDTs) supported by artificial intelligence techniques. Low electricity production in PV systems can be caused by an efficiency decrease in PV modules due to abnormal operating conditions such as failures or malfunctions. The most common performance decreases are due to the presence of dirt on the surface of the module, the impact of which depends on many parameters and conditions, and can be identified through the use of the TNDTs. The proposed approach allows one to automatically classify the thermographic images from the convolutional neural network (CNN) of the system, achieving an accuracy of 98% in tests that last a couple of minutes. This approach, compared to approaches in literature, offers numerous advantages, including speed of execution, speed of diagnosis, reduced costs, reduction in electricity production losses.

We demonstrate an innovative CIGS-based solar cells model with a graded doping concentration absorber profile, capable of achieving high efficiency values. In detail, we start with an in-depth discussion concerning the parametrical study of conventional CIGS solar cells structures. We have used the wxAMPS software in order to numerically simulate cell electrical behaviour. By means of simulations, we have studied the variation of relevant physical and chemical parameters—characteristic of such devices—with changing energy gap and doping density of the absorber layer. Our results show that, in uniform CIGS cell, the efficiency, the open circuit voltage, and short circuit current heavily depend on CIGS band gap. Our numerical analysis highlights that the band gap value of 1.40 eV is optimal, but both the presence of Molybdenum back contact and the high carrier recombination near the junction noticeably reduce the crucial electrical parameters. For the above-mentioned reasons, we have demonstrated that the efficiency obtained by conventional CIGS cells is lower if compared to the values reached by our proposed graded carrier concentration profile structures (up to 21%).

In this paper, a linear generator for a highly reliable Wave Energy Conversion system is designed and tested. In order to store energy, the system is able to produce hydrogen The Wave Energy Conversion System consists of an electrical linear generator, a power conversion system and a sea-water electrolyzer. A small scale prototype of the system has been designed and built. The design is oriented to the enhancement of the system robustness and reliability and a Failure Mode and Effects and Criticality Analysis (FMECA) has been used. In order to guarantee an easy extension of the power capability of the marine plant, a modular architecture of the system has been adopted. The design strategy is described. The robustness and reliability of the proposed solution are discussed. Simulation and experimental results on the prototype are shown.