Photovoltaic (PV) technologies have attracted great interest due to their capability of generating electricity directly from sunlight. Machine learning (ML) is a technique for computer to learn how to perform a specific task using known data. It can be used in many areas and has become a hot research topic recently due to the rapid accumulation of data and advancement of computer hardware. The application of ML techniques in the design and fabrication of solar cells started slowly but has recently gained tremendous momentum. An exhaustive compilation of the literatures indicates that all the major aspects in the research and development of solar cells can be effectively assisted by ML techniques. If combined with other tools and fed with additional theoretical and experimental data, more accurate and robust results can be achieved from ML techniques. The aspects can be grouped into four categories: prediction of material properties, optimization of device structures, optimization of fabrication processes, and reconstruction of measurement data. A statistical analysis of the literatures shows that artificial neural network (ANN) and genetic algorithm (GA) are the two most applied ML techniques and the topics in the optimization of device structures and optimization of fabrication processes are more popular. This article can be used as a reference by all PV researchers who are interested in ML techniques.

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Perovskite solar cells (PSCs) are solar cells that are efficient, low cost, and simple to fabricate. Over the last 9 years, researchers have conducted in-depth research on PSCs to increase their photoelectric conversion efficiency from 3.8% to 24.2%. PSCs have the potential to replace traditional energy sources in the future. However, the stability of these cells is poor, which limits their practical applications, because the perovskite material is susceptible to degradation by environmental factors, such as moisture, heat, and oxygen. In our review, some studies related to improving the stability of PSCs are summarized. Strategies that have been developed to improve the stability of PSCs are reviewed from the aspects of the electron transport, perovskite, and hole transport layers (HTLs). These strategies include doping the electron transport layer (ETL), using dopant-free HTL, grain passivation, employing double layers or graded hybrid structure of ETL or HTL, two-dimensional perovskites, and so on. We provide a reference for future studies on the stability of PSCs.

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The introduction of excess PbI2 into CH3NH3PbI3 precursors has been reported to boost the efficiency of CH3NH3PbI3 solar cell. It was assumed that the excess PbI2 helped to reduce the defect density in CH3NH3PbI3 solar cell. In this work, by adding non-equimolar PbI2 into CH3NH3PbI3 precursor solution, PbI2-rich CH3NH3PbI3 solar cells have been fabricated. The efficiency of CH3NH3PbI3 solar cell was significantly improved from 14.14% to 16.80%. Results obtained from scanning electron microscopy (SEM) and X-ray diffraction (XRD) indicate that the excess PbI2 does not affect the morphological and crystal properties of CH3NH3PbI3 thin film. Based on time-resolved photoluminescence (TRPL) measurement, it was found that the carrier lifetime of PbI2-rich perovskite thin film was significantly increased. Lower defect density was observed in PbI2-rich CH3NH3PbI3 solar cell by admittance spectroscopy (AS) characterization, indicating PbI2 can suppress the formation of defects in CH3NH3PbI3 solar cells.

Mesoporous MCM‐41(M(with template)) whose pore channels were filled with cationic surfactant n‐hexadecyltrimethyl‐ammonium bromide (CTAB) and a (hydrophobic) poly(propylene glycol) nucleus terminated by two primary hydroxyl groups (Pluronic F‐127, molecular weight = 11500) inside the pore channels and on the outer surface of the particle, MCM‐41 (M(without template)) whose mixture template inside the pore channels and on the outer surface of the particle were calcinated, were used as new fillers for waterborne epoxy. The corrosion studies were carried out on steel plates coated with formulations containing polyaniline prepared with M(with template) and M(without template). Correspondingly, corrosion performance of the coatings was studied by electrochemical impedance spectroscopy (EIS) in 3.5% NaCl aqueous solution and salt spray test. Coatings prepared from polyaniline (PANI)/M(without template) particles synthesized by in situ polymerization were found to exhibit excellent corrosion resistance much superior to PANI/M(with template) in aggressive environments due to the different interfacial structures between the fillers and the matrix.

Inertia effect and damping capacity, which are the basic characteristics of traditional power systems, are critical to grid frequency stability. However, the inertia and damping characteristics of grid-tied photovoltaic generation systems (GPVGS), which may affect the frequency stability of the grid with high proportional GPVGS, are not yet clear. Therefore, this paper takes the GPVGS based on droop control as the research object. Focusing on the DC voltage control (DVC) timescale dynamics, the mathematical model of the GPVGS is firstly established. Secondly, the electrical torque analysis method is used to analyze the influence law of inertia, damping and synchronization characteristics from the physical mechanism perspective. The research finds that the equivalent inertia, damping and synchronization coefficient of the system are determined by the control parameters, structural parameters and steady-state operating point parameters. Changing the control parameters is the simplest and most flexible way to influence the inertia, damping and synchronization ability of the system. The system inertia is influenced by the DC voltage outer loop proportional coefficient Kp and enhanced with the increase of Kp. The damping characteristic of the system is affected by the droop coefficient Dp and weakened with the increase of Dp. The synchronization effect is only controlled by DC voltage outer loop integral coefficient Ki and enhanced with the increase of Ki. In addition, the system dynamic is also affected by the structural parameters such as line impedance X, DC bus capacitance C, and steady-state operating point parameters such as the AC or DC bus voltage level of the system and steady-state operating power (power angle). Finally, the correctness of the above analysis are verified by the simulation and experimental results.

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