In Egypt, the production of power and the associated environmental problems are starting to take the stage. One environmentally responsible way to lessen the power crisis is to employ renewable energy sources effectively and efficiently. This paper proposes to develop a hydrogen energy storage-based green (or environmentally friendly) power plant on many Egyptian cities such as Sohag city. To produce green hydrogen, the proposed power station uses energy storage, solar, and wind power. Energy storage systems are used to store extra energy produced by wind turbines and solar panels and to supply energy when the output of renewable energy is low. An optimized design of the proposed power plant uses hydrogen energy to satisfy peak load requirements and reduce GHG (greenhouse gas) emissions. Electrolysis is the method used in the proposed solar/wind power plant to create hydrogen. Water can be split into hydrogen and oxygen via electrolysis, a process that uses electricity. Renewable energy sources can be used to power this procedure, ensuring that the hydrogen produced is "green" and does not contribute to greenhouse gas emissions. The design of the power plant incorporates advanced electrolysis technology, such as proton exchange membrane (PEM) electrolyzers, which are efficient and well-suited for integrating with renewable energy sources.

Airplane manufacturers are frequently faced with formidable challenges to improving both aircraft performance and customer safety. Ice accumulation on the wings of aircraft is one of the challenges, which could result in major accidents and a reduction in aerodynamic performance. Anti-icing systems, which use the hot bleed airflow from the engine compressor, are considered one of the most significant solutions utilized in aircraft applications to prevent ice accumulation. In the current study, a novel approach based on machine learning (ML) and the Internet of Things (IoT) is proposed to predict the thermal performance characteristics of a partial span wing anti-icing system constructed using the NACA 23014 airfoil section. To verify the proposed strategy, the obtained results are compared with those obtained using computational ANSYS 2019 software. An artificial neural network (ANN) is used to build a forecasting model of wing temperature based on experimental data and computational fluid dynamics (CFD) data. In addition, the ThingSpeak platform is applied in this article to realize the concept of the IoT, collect the measured data, and publish the data in a private channel. Different performance metrics, namely, mean square error (MSE), maximum relative error (MAE), and absolute variance ($R^2$), are used to evaluate the prediction model. Based on the performance indices, the results prove the efficiency of the proposed approach based on ANN and the IoT in designing a forecasting model to predict the wing temperature compared to the numerical CFD method, which consumes a lot of time and requires high-speed simulation devices. Therefore, it is suggested that the ANN-IoT approach be applied in aviation.

This study proposes an innovative stacking ensemble learning framework for classifying faults in induction motors utilized in Electric Vehicles (EVs). Employing a comprehensive dataset comprising motor data, such as speed, torque, current, and voltage, the analysis encompasses six distinct conditions: normal operating mode, over-voltage fault, under-voltage fault, overloading fault, phase-to-phase fault, and phase-to-ground fault. The proposed model integrates Gradient Boosting (GB), K-Nearest Neighbors (KNN), Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) algorithms in a synergistic manner. The findings reveal that the RF–GB–DT–XGBoost combination achieves a remarkable accuracy of 98.53%, significantly surpassing other methods reported in the literature. Performance is evaluated through metrics including accuracy, precision, sensitivity, and F1-score, with results analyzed in comparison to practical applications and existing studies. Validated with real-world data, this study demonstrates that the proposed model offers a groundbreaking solution for predictive maintenance systems in the EV industry, exhibiting high generalization capacity despite complex operating conditions. This approach holds transformative potential for both academic research and industrial applications. The dataset used in this study was generated using a MATLAB 2018/Simulink-based Variable Frequency Drive (VFD) model that emulates real-world EV operating conditions rather than relying solely on laboratory data. This ensures that the developed model accurately reflects practical electric vehicle environments.

To enhance the stability and reliability of the system, the converters’ parallel operation can be cascaded to address the constraints posed by the substantial integration of renewable resources. Buck-boost DC-DC converters are often controlled via a cascaded control approach to allow parallel operation. The converter’s output current and its voltage will be controlled by nested loop control. This study proposes adaptive droop control parameters that are updated and verified online using the principal current sharing loops to minimize the fluctuation in load current sharing. When the converters in the microgrid are paralleled, load sharing will be accomplished using the droop control approach in addition to nested proportional-integral-based voltage and current control loops. To restore the correct voltage across the DC microgrid, an outer addition voltage secondary loop will be used, rectifying any voltage disparities caused by the droop management strategy. Several common load resistances and input voltage variations are used to test the suggested method. Using a linearized model, this work assesses the stability and performance of the proposed method. It then confirms the findings with an adequate model created in MATLAB/SIMULINK, Real-Time Simulation Fundamentals, and hardware-based experiments.

This paper investigates enhancing the efficiency of solar water pumping systems (SWPS) by implementing a Maximum Power Point Tracking technique based on the Bat Metaheuristic Optimizer (MPPT-bat) for the photovoltaic generator (PVG) side, coupled with Direct Torque Control (DTC) for the induction motor powering the pump. Unlike traditional techniques, which make no compromise between tracking speed, oscillation and robustness. The integration of the MPPT-bat represents a significant advance, making it possible to improve PVG performance whatever the weather conditions. The main objective remains to improve the energy efficiency of this type of application by maximizing the electrical power allocated to the SWPS. At the same time, a DTC controller applied to the pump motor aims to optimize the use of the energy generated by the MPPT-bat. This intelligent approach adjusts the motor power according to the power extracted from the PVG, thus avoiding inappropriate profiles for the pumping system. The study confirms that optimizing SWPS using this approach based on MPPT-bat and DTC, significantly improves overall performances in terms of tracking error, oscillations, tracking speed and robustness, promotes more efficient pump rotation and, consequently, increases the flow rate of pumped water, and that these improvements persist under different climate conditions.