Photovoltaic (PV) system diagnosis is a growing research domain likewise solar energy’s ongoing significant expansion. Indeed, efficient Fault Detection and Diagnosis (FDD) tools are crucial to guarantee reliability, avoid premature aging and improve the profitability of PV plants. In this paper, an on-line diagnosis method using the PV plant electrical output is presented. This entirely signal-based method combines variational mode decomposition (VMD) and multiscale dispersion entropy (MDE) for the purpose of detecting and isolating faults in a real grid-connected PV plant. The present method seeks a low-cost design, an ease of implementation and a low computation cost. Taking into account the innovation of applying these techniques to PV FDD, the VMD and MDE procedures as well as parameters identification are carefully detailed. The proposed FFD approach performance is assessed on a real rooftop PV plant with experimentally induced faults, and the first results reveal the MDE approach has good suitability for PV plants diagnosis.

To deal with energy transition due to climate change and a rise in average global temperature, photovoltaic (PV) conversion appears to be a promising technology in sunny regions. However, PV production is directly linked with weather conditions and the day/night cycle, which makes it intermittent and random. Therefore, it makes sense to combine it with Energy Storage Systems (ESS) to ensure long-term energy availability for non-interconnected micro-grids. Among all technological solutions, electrolytic hydrogen produced by renewable energies seems an interesting candidate. In this context, this paper proposes a control strategy dedicated to hydrogen storage integration in micro-grids for a better use of PV production. The objective is to optimize the management of the micro-grid with proton exchange membrane Fuel Cell (FC), alkaline Electrolyzer (El), lithium-ion Batteries Energy Storage System (BESS) and PV, according to the system state and PV production intermittency. First, a control strategy based on a Distributed explicit Model Predictive Control (DeMPC) is developed to define current references for FCs, Els and batteries. Secondly, the performance of the control strategy is validated in simulation and confirmed on a Power-Hardware-in-the-Loop test bench.

Oxygen bubble accumulation on the anodic side of a polymer exchange membrane water electrolyzer (PEMWE) may cause a decrease in performance. To understand the behavior of these bubbles, a deep-learning-based bubble flow recognition tool dedicated to a PEMWE is developed. Combining the transparent side of a single PEMWE cell with a high-resolution high-speed camera allows us to acquire images of the two-phase flow in the channels. From these images, a deep learning vision system using a fine-tuned YOLO V7 model is applied to detect oxygen bubbles. The tool achieved a high mean average precision of 70%, confirmed the main observations in the literature, and provided exciting insights into the characteristics of two-phase flow regimes. In fact, increasing the water flow rate from 0.05 to 0.4 L/min decreases the bubble coverage (by around 32%) and the mean single-bubble area. In addition, increasing the current density from 0.3 to 1.4 A/cm² leads to an increase in bubble coverage (by around 40%) and bubble amount.