The interest in vehicle-to-vehicle communication has gained a high demand in the last decade. This is due to the need for safe and robust smart communication, while this type of communication is vulnerable to latency and power. Therefore, this work proposes the Narrowband Internet-of-Things to enhance the robustness of the vehicular communication system. Accordingly, the system’s QoS is enhanced. This enhancement is based on proposing two parts to cover the latency and the harmonics issues, in addition to proposing a distributed antenna configuration for the moving vehicles under a machine learning benchmark, which uses the across-entropy algorithm. The proposed environment has been simulated and compared to the state-of-the-art work performance. The simulation results verify the proposed work performance based on three different parameters; namely the latency, the mean squared error rate, and the transmitted signal block error rate. From these results, the proposed work outperforms the literature; at the probability of 10⁻³, the proposed work reduces the peak power deficiency by almost 49%, an extra 23.5% enhancement has been attained from the self-interference cancellation side, and a bit error rate enhancement by a ratio of 31%.

While the first generation of silicon solar cells offers a clean and unlimited energy source, the technology has matured where costs dominate, and the theoretical power conversion efficiency is reaching its limits. The new generation of thin-film solar cells is emerging as an affordable alternative to their bulky counterparts. The technology offers a much cheaper method to quickly fabricate solar cells that use less material with good optical and electronic properties on a wide range of substrates, including flexible materials. In particular, Cu (In_xGa_1−x) (Se)₂ thin-film solar cells are investigated using SCAPS simulation to study the impact of series resistance and doping levels of different layers of the cell structure on the short-circuit current, open-circuit voltage, power conversion efficiency, and fill factor. It was found that an increase in the series resistance of the solar cell layers results in a decrease in the power conversion efficiency with a dependency on light intensities. In addition, the doping level in the absorber and buffer layers plays a significant role in controlling the solar cell’s power conversion efficiency and fill factor values with maximum values when acceptor doping levels are approximately equal to donor doping levels.

The voltage deviation is one of the most crucial power quality issues that occur in electrical power systems. Renewable energy plays a vital role in electrical distribution networks due to the high economic returns. However, the presence of photovoltaic systems changes the nature of the energy flow in the grid and causes many problems such as voltage deviation. In this work, several predictive models are examined for voltage regulation in the Jordanian Sabha distribution network equipped with photovoltaic farms. The augmented grey wolf optimizer is used to train the different predictive models. To evaluate the performance of models, a value of one for regression factor and a low value for root mean square error, mean square error, and mean absolute error are used as standards. In addition, a comparison between nineteen predictive models has been made. The results have proved the capability of linear regression and the gaussian process to restore the bus voltages in the distribution network accurately and quickly and to solve the shortening in the voltage dynamic response caused by the iterative nature of the heuristic algorithm.

This paper presents a practical approach to calculate the total owning cost (TOC) of a three-phase Induction Motor, which is based on the motor’s nameplate data and the purchasing price. The economic evaluation is performed considering both the induction motor electrical energy losses and its amortized annual capital cost. The proposed technique consists of three stages, where the total power losses are determined analytically in the first stage. The load loss factor (LSF) is statistically obtained to determine the total energy losses in the second stage. In the third stage, the economic evaluation was conducted. The obtained results show that the proposed approach is a helpful tool for the decision-maker when comparing the received offers from different vendors and finding the answer to the question of which offer has less TOC. Finally, the proposed method is illustrated through a numerical example and software using MATLAB was performed. Results and conclusions have been summarized and discussed.

In this paper, an online adaptive general regression neural network (OAGRNN) is presented as a direct online speed controller for a three-phase induction motor. To keep the induction motor running at its rated speed in real-time and under a variety of load conditions, the speed error and its derivative are continuously measured and fed back to the OAGRNN controller. The OAGRNN controller provides the inverter with the control signal it needs to produce the proper frequency and voltage for the induction motor instantly. Notably, the OAGRNN controller demonstrated remarkable performance without the need for a learning mode; it was able to track the desired motor speed, starting its operation from scratch. A setup utilizing a three-phase induction motor has been developed to show the high capacity of OAGRNN for tracking the desired speed of the motor while subjected to the varied load torque. The performance of OAGRNN is examined in two phases: the MATLAB simulation and the experimental setup. Furthermore, when the OAGRNN performance is compared with that of the proportional integral (PI) controller, it demonstrates its outstanding ability and superiority for online adjustments related to the three-phase induction motor's speed control.