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Recent research has been focussed on renewable energy due to the rising need for electrical energy. Renewable energy has a low environmental impact compared to other energy sources. As a result, renewable energy sources (RESs) are the best option for generating electricity. Solar photovoltaic is one of the largest renewable power generators. Solar photovoltaic (PV) is connected to the load via power electronic converters. Most PV installations need a two-stage conversion process consisting of a boost converter to increase the load voltage and an AC-to-DC voltage source inverter to power the load. The Z-source inverter (ZSI) can confront the shortcomings of VSI and two-stage conversions. ZSI connects the PV system to the load and is used to increase the system's performance. This paper discusses the performance of various topologies of ZSI, such as traditional Z-source inverters (XZSIs); for integrating a PV source into a load, switched inductor Z-source inverters (SIZSIs) and transient Z-source inverters (TZSIs) are used. Also, artificial neural networks (ANNs), fuzzy logic controller (FLC), and adaptive neuro-fuzzy inference system (ANFIS)-based MPPT techniques are discussed for obtaining maximum power from PV panels. Based on the maximum power, the shoot-through duty ratio has been adjusted. [ABSTRACT FROM AUTHOR]

In this research, a solar photovoltaic system with maximum power point tracking (MPPT) and battery storage is integrated into a grid-connected system using an improved three-level neutral-point-clamped (NPC) inverter. An NPC inverter with adjustable neutral-point clamping may achieve this result. To achieve this result, a modernized NPC inverter is used. Using the three-level vector modulation approach, the correct AC voltage may be generated when DC voltage conditions are present in an unbalanced situation involving an NPC inverter. A higher degree of precision is made possible by this. In-depth information on the many possible NPC inverter designs and modulation strategies was provided. By incorporating the necessary sophisticated algorithms into the NPC inverter, the necessary solar PV-MPPT functionality is made available, allowing for the regulation of power transfer among the solar PV system, the battery, and the grid. Modified INC method is proposed which has a tracking efficiency of 99.5% for varying irradiance. The use of sufficiently sophisticated algorithms makes this a reality. Using simulation with MATLAB/Simulink, we were able to examine an NPC inverter's performance in several setups. Several amounts of solar irradiation were used to test the battery's charging and discharging capabilities. The probe turned out to be fruitful. Laboratory testing ensures that the proposed method works by simulating real-world conditions. [ABSTRACT FROM AUTHOR]

According to the stability process of smart grids, which starts by gathering information of consumers, and then evaluating this information based on specifications of a power supply, and finally, information of a price is sent to the consumers as a report about the utilization. From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). Recent advances in the accuracy of neural network have effective solutions to solving the smart grid stability prediction issues, but it remains necessary to develop high performance neural networks that give higher accuracy. In this paper, an artificial neural network (ANN) is proposed to predict a smart grid stability for Decentral Smart Grid Control (DSGC) systems. This neural network is applied to a dataset aggregated from simulations of grid stability, executed on a four-node network with star topology, and engaged in two classes of grid stability–stable and unstable. Keras framework is used to train the proposed neural network, and a hyperparameter tuning method is utilized to achieve high accuracy. Receiver operating characteristic (ROC) curves and confusion matrices are experimentally utilized to evaluate the performance of the proposed neural network. The neural network provides high performance, with a testing loss rate of 0.0619, and a testing accuracy of 97.36%. The weighted average recall, precision, and F1-score for the proposed neural network are 98.02%, 98.03%, and 98.02%, respectively, while the area under the ROC curves (AUCs) is 100%. This neural network with the utilized dataset indeed provides an accurate and quick approach of predicting grid stability to analyze DSGC systems. [ABSTRACT FROM AUTHOR]

Microgrids with distributed generation (DG) are rapidly coming into distribution networks to supply load demand in order to preserve ecological balance and reduce greenhouse gas emissions. Power electronic advancements are making renewables dispatchable to loads while turning passive networks to active with bidirectional power flow. The IEEE-1547-2018 regulations enforced certain standards on microgrids, including the ability to detect unintended failures, island the microgrid in less than 2 seconds, and feed connected loads while maintaining voltage, frequency, and power quality. There are numerous islanding techniques accessible, including passive, active, hybrid, and communication techniques. The active techniques degrade power quality due to injection of deviations, passive type leaves larger nondetection zone (NDZ), and communication type are costly. The hybrid type combines both passive and active methods. To get away from all these issues, a passive technique is formulated in this paper, which measures the differential phase angle of voltage and current at DG output, to detect islanding. This approach reliably identifies islanding in 20 ms at nearly zero NDZ. This approach is also stable during transient conditions such as load switching and throwing off. There is also no power quality issue because there are no injections during testing. This method is tested in MATLAB/Simulink and evaluated using the differential frequency technique to get performance indices in accordance with UL-1741 testing standards. [ABSTRACT FROM AUTHOR]

Titanium dioxide nanofluid is used in the thermosyphon solar flat plate collector at volume fractions of 0.1%, 0.3%, 0.5%, 0.7%, and 1% to test the collector's effectiveness. The heat transfer coefficient, Nusselt number, friction factor, and pressure drop calculations were used to analyze the impact of adding nanoparticles to the working fluid. The effectiveness of the solar flat plate collector and the heat transfer coefficient are both greatly affected by the addition of titanium dioxide to the working fluid, with a tolerable rise in pressure drop. With concomitant variances of 15% and 13%, correlations were constructed for the Nusselt number and friction factor. [ABSTRACT FROM AUTHOR]