The potential for utilizing clean energy technologies in Egypt is excellent given the abundant solar irradiation and wind resources. This paper provides detailed design, control strategy, and performance evaluation of a grid-connected large-scale PV/wind hybrid power system in Gabel El-Zeit region located along the coast of the Red Sea, Egypt. The proposed hybrid power system consists of 50 MW PV station and 200 MW wind farm and interconnected with the electrical grid through the main Point of Common Coupling (PCC) busbar to enhance the system performance. The hybrid power system is controlled to operate at the unity power factor and also the Maximum Power Point Tracking (MPPT) technique is applied to extract the maximum power during the climatic conditions changes. Modeling and simulation of the hybrid power system have been performed using MATLAB/SIMULINK environment. Moreover, the paper presented a comprehensive case study about the realistic monthly variations of solar irradiance and wind speed in the study region to validate the effectiveness of the proposed MPPT techniques and the used control strategy. The simulation results illustrate that the total annual electricity generation from the hybrid power system is 1509.85 GWh/year, where 118.15 GWh/year (7.83 %) generates from the PV station and 1391.7 GWh/year (92.17%) comes from the wind farm. Furthermore, the hybrid power system successfully operates at the unity power factor since the injected reactive power is kept at zero.

This study presents a detailed feasibility analysis of technical and financial assessment for grid-connected Hybrid Renewable Energy System (HRES) configurations by including grid-only, HRES-only and grid-HRES at four different provinces in the Kingdom of Saudi Arabia (KSA), namely; (Al Baha University, University of Jeddah, Prince Sattam Bin Abdulaziz University, and Tabuk University). The objective of this paper is to search the possibility of supplying the load demand with the optimum system that has the lowest net present cost (NPC) and greenhouse emission CO₂. The simulation results show that NPC of a proposed grid/PV system, at the current grid’s tariff, is more sufficient than other configurations with a result in a renewable fraction of more than 50%, a payback time of 17 years, and 54.3% reduction in CO₂. The results also show that the integration of 62 kW PV array with the main grid is the best configuration that leads to the minimum cost of energy (COE) of 0.0688 $/kWh and the sell back energy of 9.16% of total energy consumption at Al Baha University. Besides, optimization modeling addresses that HRES-only system can supply the full load demand without power shortage (<0.1%) with a major contribution from solar PV by 78.5%, wind energy shares 11.3% of load demand, and 10.2% from battery banks. The developed analysis concludes that the objective function is feasible for the selected locations. The study has three novelties. Firstly, the required load at different locations of the university’s buildings at KSA is supplied by minimizing COE. The objective function is achieved by considering a combination of HRES. Then, it applies the sensitivity analysis for several cases such as payback time, gird’s tariff variation, and load demand change. Finally, the current analyses are applicable to any university at KSA and around the world.

Electric vehicles (EVs) are unquestionably the future of transportation due to their numerous advantages, such as their ability to address environmental issues, decrease dependence on fossil fuel, and enhance energy security. Attaining optimum performance, efficiency, and safety in EVs requires accurate control over both the speed and torque of the electric motor used. The Doubly Fed Induction Motor (DFIM) is classified as a motor that offers precise control over both its speed and torque. Additionally, it has some features that render it suitable for EV applications. This study contributes to advancing the field of new control systems for a DFIM used in EV applications. A key novelty of this work is the introduction of the Backstepping-Direct Torque Control (BSC-DTC) system for DFIM in EV applications. The suggested controller accurately tracks the specified speed set by the EV driver (reference speed) without any deviation, which is a key characteristic in modern EVs. Furthermore, the torque of the DFIM is regulated using the developed DTC method, BSC-DTC. To assess the capability of the BSC-DTC to effectively monitor the reference speed and achieve torque control, a comparison with conventional DTC is provided. Furthermore, an experimental implementation was conducted using the DS1104 board developed by dSPACE to validate the simulation results, highlighting the practical feasibility of the proposed system. Comprehensive modeling of the EV drivetrain was conducted and simulated utilizing the MATLAB-Simulink environment. The simulation findings demonstrate that the suggested control system offers superior performance for the EV system in terms of accurate reference speed tracking with zero tracking error and a slight overshoot of 0.1 km/h, as well as dynamic torque response. However, ripples in the electromagnetic torque provided by the motor have been observed. To address this issue, another innovative aspect of the work involves focusing future efforts on implementing control approaches based on artificial intelligence methodologies, opening new research and innovation avenues.

Due to their simplicity, cheapness, and ease of maintenance, induction motors (IMs) are the most widely used motors in the industry. However, if they are not properly controlled, the load torque and motor speed will fluctuate in an unsatisfactory fashion. To effectively control the load torque and speed of these IMs, it is necessary to use specialized drives. The entire system (IMs + Drives) will experience uncertainty, nonlinearities, and disruptions, which calls for an outstanding performance control structure. The sensorless sliding mode predictive torque control (SSM-PTC) for both AC-DC converter and DC-AC inverter, which are utilized for feeding the IM, is investigated in this work. The AC-DC converter is controlled using the SSM-PTC method in order to follow the DC-link reference voltage throughout any changes in the operating point of the IM. While the DC-AC inverter is controlled using a sensorless predictive power control (SPPC). Within a unity power factor, this SPPC regulates the reactive power flow between the motor and the supply to account for the undesirable harmonic components of the grid current. In addition, an experimental performance improvement of SSM-PTC of IM supplied by a 5-leg AC-DC-AC power converter using extended Kalman filter (EKF) without weighting factor (WF) is also studied in this work. Design and implantation of the suggested control systems are performed using a dSPACE 1104 card. The experimental results of the proposed converter control demonstrate that the suggested approach effectively regulated the DC link, reducing load torque and speed fluctuations. In the context of inverter control, a prompt active power response yields a motor current waveform that resembles a sinusoidal pattern, exhibiting minimal levels of harmonic distortion.

Ensuring mission reliability is vital for the autonomous deployment of unmanned aerial vehicles (UAVs) in modern power and energy systems, particularly under spatial and operational constraints. This study presents a data-driven classification method that assesses the reliability of UAV-based inspection missions by identifying whether individual mission locations are suitable, at risk, or infeasible based on spatial and operational parameters. Leveraging the Cumulative UAV Routing Problem (CUAVRP) benchmark, four representative mission scenarios were analyzed, each characterized by unique UAV fleet sizes, sensor ranges, and endurance limits. Synthetic stress nodes were introduced to emulate edge-case conditions encountered in infrastructure inspection tasks. Each node was classified based on three categorical targets: Mission Feasibility, Coverage Reliability, and Deployment Suitability. A gradient boosting classification model was trained on spatial and operational features to determine node status. Evaluation across all scenarios yielded consistently high performance, with the cuavrp_d9_k6_r800 scenario achieving 97.05% accuracy, 96.33% precision, 97.72% recall, and 97.02% F1-score. Furthermore, incorporating physical-layer degradation factors such as signal attenuation, multipath fading, and interference is expected to enhance the realism of future reliability assessments and improve classification robustness. The proposed classification framework supports intelligent mission planning, enhances operational resilience, and facilitates automated UAV deployment strategies in critical inspection environments within the power and energy sector.

To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network’s operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method’s viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.