Maximum Power Point Tracking (MPPT) controllers play an important role in improving the efficiency of Solar Photovoltaic (SPV) modules. These controllers achieve maximum power transfer from PV modules through impedance matching between the PV modules and the load connected. Several MPPT techniques have been proposed for searching the optimal matching between the PV module and load resistance. These techniques vary in complexity, tracking speed, cost, accuracy, sensor and hardware requirements. This paper presents the design and modelling of the Adaptive Neuro-Fuzzy Inference System (ANFIS) based MPPT controller. The design consists of a PV module, ANFIS reference model, DC-DC boost converter and the Fuzzy Logic (FL) power controller for generating the control signal for the converter. The performance of the proposed ANFIS based MPPT controller is evaluated through simulations in the MATLAB/Simulink environment. The simulations results demonstrated the effectiveness of the proposed technique since the controller can extract the maximum available power for both steady-state and varying weather conditions. Moreover, a comparative study between the proposed ANFIS based MPPT controller and the commonly used, Perturbation and Observation (P&O) MPPT technique, is presented. The simulation results reveal that the proposed ANFIS based MPPT controller is more efficient than the P& O method since it shows a better dynamic response with few oscillations about the Maximum Power Point (MPP). In addition, the proposed FL power controller for generating the duty cycle of the DC-DC boost converter also gave satisfying results for MPPT.

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.

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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.