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.

The proposed measured data combines PV plant electrical data with associated solar and meteorological data during normal and faulty conditions. Data are collected regarding a domestic rooftop PV plant of 4 kW, located in the La Réunion Island, in the South-West of Indian Ocean. The present dataset includes healthy behavior and different types of shading faults, identified and labelled by means of a numeric variable. The electrical data (voltage, current and power at AC and DC side as well as produced energy and grid frequency) are collected thanks to PV inverters. Global and diffuse irradiance, PV temperature and ambient temperature are acquired thanks to additional sensors. Electrical and meteorological data sampling frequencies are set to 0.2 Hz and 1 Hz respectively. At present, 12 months of data are available and the database is still being updated. The data streams from each connected device require proper techniques to ensure their persistence. To be able to provide both efficient ingestion and retrieval of these time series collections, the NoSQL database management system InfluxDB has been implemented. The whole dataset is available on Zenodo repository, and can be used, for instance, for PV modeling, PV plant behavior analysis, PV production forecasting and PV Fault Detection and Diagnosis (FDD) tool development.

This work proposes an Energy Management System (EMS) for a Micro-grid composed of a Photovoltaic system (PVs) with Maximum Power Point Tracking (MPPT), a Proton Exchange Membrane Fuel Cell (PEMFC), an electrolyzer and a storage system composed of batteries and hydrogen tanks. When PV production is lower than load demand, the lack of power demand is provided by the fuel cell with low dynamics and batteries with high dynamics. There current references are calculated using a constrained Explicit Model Predictive Control (EMPC) taking into account the dynamic of each element. When PV production is higher than load demand, the fuel cell is idle and its current reference is zero. According to the State of Charge (SoC) of the batteries, the storage elements charging current reference is balanced between them. The EMS performance is evaluated by simulation results.

With the objectives of energy self-sufficiency and zero emissions in La Reunion, photovoltaics are becoming an increasingly important part of the local energy mix. Installation reliability and safety are crucial to ensure network stability and security, therefore PV system fault diagnosis is an essential tool in the expansion of this electricity production method. The DETECT Project (Diagnosis onlinE of sTate of health of EleCTric systems) is a research project aiming at diagnosis method development. In this way, signal processing provides us with promising tools in the form of decomposition algorithms. Thanks to their low computation cost, empirical mode decomposition (EMD) and variational mode decomposition (VMD) allow undertaking a real-time diagnosis, with on-line PV electrical signal time-series data analysis.