Photovoltaic (PV) systems are subject to failures during their operation due to the aging effects and external/environmental conditions. These faults may affect the different system components such as PV modules, connection lines, converters/inverters, which can lead to a decrease in the efficiency, performance, and further system collapse. Thus, a key factor to be taken into consideration in high-efficiency grid-connected PV systems is the fault detection and diagnosis (FDD). The performance of the FDD method depends mainly on the quality of the extracted features including real-time changes, phase changes, trend changes, and faulty modes. Thus, the data representation learning is the core stage of intelligent FDD techniques. Recently, due to the enhancement of computing capabilities, the increase of the big data use, and the development of effective algorithms, the deep learning (DL) tool has witnessed a great success in data science. Therefore, this paper proposes an extensive review on deep learning based FDD methods for PV systems. After a brief description of the DL-based strategies, techniques for diagnosing PV systems proposed in recent literature are overviewed and analyzed to point out their differences, advantages and limits. Future research directions towards the improvement of the performance of the DL-based FDD techniques are also discussed. This review paper aims to systematically present the development of DL-based FDD for PV systems and provide guidelines for future research in the field.

/pdf/deep-learning-based-fault-diagnosis-of-photovoltaic-systems-4km4iu1kry.pdf

Deep Learning-Based Fault Diagnosis of Photovoltaic Systems: A Comprehensive Review and Enhancement Prospects

Techno-economic feasibility of solar power plants considering PV/CSP with electrical/thermal energy storage system

Optimal scheduling of electric vehicles charging in battery swapping station considering wind- photovoltaic accommodation

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Hydrokinetic energy harnessing technologies: a review

China has become the world's largest clean energy country in terms of the total installation of wind and photovoltaic power and annual newly installed capacity. However, weather conditions render renewable energy unstable, thereby restricting its application to a power grid; reducing the randomness in wind or photovoltaic power is the major challenge of the utilization of solar and wind energy. This paper systematically reviews the explorations and the practices related to wind and photovoltaic power consumption in China. Considering the difference in the methods of supplementing the variable and intermittent output of wind and PV power, five consumption modes are outlined: distributed energy microgrid absorption, power grid peak shaving operation consumption, wind-photovoltaic-storage consumption, wind-photovoltaic-thermal complementation, and wind-photovoltaic-hydro complementation. The theories, features, current situation and growing trends of each mode are analyzed. Finally, several suggestions, including promoting multi-energy complementary microgrid application and installing large-scale pumped storage hydropower, are provided for improving the efficiency of renewable energy development in China.

How to make better use of intermittent and variable energy? A review of wind and photovoltaic power consumption in China

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How to make better use of intermittent and variable energy? A review of wind and photovoltaic power consumption in China