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Wind power generation has been the fastest-growing energy alternative in recent years, however, it still has to compete with cheaper fossil energy sources. This is one of the motivations to constantly improve the efficiency of wind turbines and develop new Operation and Maintenance (O&M) methodologies. The decisions regarding O&M are based on different types of models, which cover a wide range of scenarios and variables and share the same goal, which is to minimize the Cost of Energy (COE) and maximize the profitability of a wind farm (WF). In this context, this review aims to identify and classify, from a comprehensive perspective, the different types of models used at the strategic, tactical, and operational decision levels of wind turbine maintenance, emphasizing mathematical models (MatMs). The investigation allows the conclusion that even though the evolution of the models and methodologies is ongoing, decision making in all the areas of the wind industry is currently based on artificial intelligence and machine learning models.

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.

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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.

This article analyzes the relationship between artificial intelligence (AI) and photovoltaic (PV) systems. Solar energy is one of the most important renewable energies, and the investment of businesses and governments is increasing every year. AI is used to solve the most important problems found in PV systems, such as the tracking of the Max Power Point of the PV modules, the forecasting of the energy produced by the PV system, the estimation of the parameters of the equivalent model of PV modules or the detection of faults found in PV modules or cells. AI techniques perform better than classical approaches, even though they have some limitations such as the amount of data and the high computation times needed for performing the training . Research is still being conducted in order to solve these problems and find techniques with better performance. This article analyzes the most relevant scientific works that use artificial intelligence to deal with the key PV problems by searching terms related with artificial intelligence and photovoltaic systems in the most important academic research databases. The number of publications shows that this field is of great interest to researchers. The findings also show that these kinds of algorithms really have helped to solve these issues or to improve the previous solutions in terms of efficiency or accuracy.

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.