Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Fréchet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects.

Current–voltage (I–V) curve tracers are used for measuring voltage and current in photovoltaic (PV) modules. I–V curves allow identifying certain faults in the photovoltaic module, as well as quantifying the power performance of the device. I–V curve tracers are present in different topologies and configurations, by means of rheostats, capacitive loads, electronic loads, transistors, or by means of DC–DC converters. This article focuses on presenting all these configurations. The paper shows the electrical parameters to which the electronic elements of the equipment are exposed using LTSpice, facilitating the appropriate topology selection. Additionally, a comparison has been included between the different I–V tracers’ topologies, analyzing their advantages and disadvantages, considering different factors such as their flexibility, modularity, cost, precision, speed or rating, as well as the characteristics of the different DC–DC converters.

Nowadays, photovoltaic (PV) silicon plants dominate the growth in renewable energies generation. Utility-scale photovoltaic plants (USPVPs) have increased exponentially in size and power in the last decade and, therefore, it is crucial to develop optimum maintenance techniques. One of the most promising maintenance techniques is the study of electroluminescence (EL) images as a complement of infrared thermography (IRT) analysis. However, its high cost has prevented its use regularly up to date. This paper proposes a maintenance methodology to perform on-site EL inspections as efficiently as possible. First, current USPVP characteristics and the requirements to apply EL on them are studied. Next, an increase over the automation level by means of adding automatic elements in the current PV plant design is studied. The new elements and their configuration are explained, and a control strategy for applying this technique on large photovoltaic plants is developed. With the aim of getting on-site EL images on a real plant, a PV inverter has been developed to validate the proposed methodology on a small-scale solar plant. Both the electrical parameters measured during the tests and the images taken have been analysed. Finally, the implementation cost of the solution has been calculated and optimised. The results conclude the technical viability to perform on-site EL inspections on PV plants without the need to measure and analyse the panel defects out of the PV installation.

The measurement of current–voltage (I-V) curves of single photovoltaic (PV) modules is at this moment the most powerful technique regarding the monitoring and diagnostics of PV plants, providing accurate information about the possible failures or degradation at the module level. Automating these measurements and allowing them to be made online is strongly desirable in order to conceive a systematic tracking of plant health. Currently, I-V tracers present some drawbacks, such as being only for the string level, working offline, or being expensive. Facing this situation, the authors have developed two different low-cost online I-V tracers at the individual module level, which could allow for a cost-affordable future development of a fully automated environment for the tracking of the plant status. The first system proposed implements a completely distributed strategy, since all the electronics required for the I-V measurement are located within each of the modules and can be executed without a power line interruption. The second one uses a mixed strategy, where some common electronics are moved from PV modules to the inverter or combiner box and need an automated very short disconnection of the modules string under measurement. Experiments show that both strategies allow the tracing of individual panel I-V curves and sending of the data afterwards in numerical form to a central host with a minimum influence on the power production and with a low-cost design due to the simplicity of the electronics. A comparison between both strategies is exposed, and their costs are compared with the previous systems proposed in the literature, obtaining cost reductions of over 80–90% compared with actual commercial traces.

An I-V curve measurement technique is one of the most important techniques available for characterising photovoltaic cells. Measuring an accurate I-V curve at the single-cell level is a challenging task because of the low voltages and high currents implied, requiring the management of very low impedances. In this paper, the authors propose a low-cost device for I-V curve measurements of single (or small amounts) of cells in a series based on the charge transfer between two capacitors of equal capacitance. Our measurement strategy allows us to trace the usual first quadrant curve (the normal working region of solar cells) as well as the second and fourth quadrants of the I-V curve, which are quite important for research purposes. A prototype was built to demonstrate the feasibility and successful measurements of the three-quadrant I-V curve, obtained for more than 20 different cells. To use the device in a laboratory, without depending on the solar irradiation, a modular platform was 3D-printed, integrating a board with infrared LEDs as irradiating devices, and housing (to place the solar cell under test). The result is a useful low-cost setup for three-quadrant I-V curve tracing that works as expected.