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.

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.

The present article describes a dataset encompassing model outputs generated by the Weather Research and Forecasting (WRF) regional climate model. A high-resolution (1km) downscaling simulation was performed over two tropical islands, Reunion and Mauritius, situated in the South-West Indian Ocean (SWIO), with initial and boundary conditions provided by the ERA5 reanalysis with a global resolution of 0.25° × 0.25°. The simulation used three nested domains sequentially configured with spatial resolutions of 9, 3, and 1km, respectively, with a downscaling ratio of 3. The physical configurations of this simulation were determined through previous modeling studies and sensitivity tests. The published simulation data currently covers a period of 10 years, starting from 1991 (with the possibility to be extended to 30 years). Over 60 output variables were selected for publication with open access, including those related to the intermittent energy resources (e.g., surface solar radiation and its direct/diffuse components, wind speed/direction at multiple vertical levels, and precipitation, of interest for the run-off-river hydropower), as well as the widely used climatic/meteorological variables (e.g., temperature, pressure, humidity, etc.) at a temporal resolution varying from a day up to 30 minutes. All the data are available through an open-access data server, where an intelligent algorithm is applied to simplify the download process for data users. For the first time, a long-term, high-resolution climate/meteorological dataset covering Reunion and Mauritius has been simulated and published as open-access data, yielding substantial benefits to studies on climate modeling, weather forecasting, and even those related to climate change in the SWIO region. In particular, this dataset will enable a better understanding of the temporal and spatial characteristics of intermittent climate-related energy resources, consequently facilitating their implementation towards a green and low-carbon future.