The increasing Electric Vehicle (EV) market is driven by the desire for more efficient and reliable approaches to recharge EV batteries. Among various charging methodologies for EVs, Wireless Power Transfer (WPT) has gained more attention from EV users due to its features such as safety, low maintenance, comfort, automated operation, and reliability. The innovative WPT method replaces the conductive charging system while maintaining a similar power rating and efficiency. Numerous strategies have been devised to enhance the efficiency and reliability of the WPT model. The primary challenge in WPT is reduction in power transfer efficiency (PTE) as the gap between the coils is increased. Also, the improvement in the PTE depends on various design parameters of the WPT circuit. Therefore, this review article thoroughly investigates recent significant research literatures that explains WPT technology and tailored towards enhancement of PTE. The investigation is carried on various coil configurations, converter topologies and different critical factors to improve PTE such as impedance matching, compensation techniques. Moreover, the research gaps in WPT technology and future scope suitable for static, quasi-dynamic, and dynamic charging methods are presented. This research can play a crucial role in assisting developers in selecting an optimal design to enhance the WPT system.

This paper explores the synergistic role of Distributed Resources (DR), including Distributed Generation (DG) and Battery Energy Storage Systems (BESS), in enhancing modern power systems’ sustainability, reliability, and flexibility. It addresses the gap in concurrent distribution network reconfiguration and DR allocation, especially under the variability of renewable energy. The study aims to minimize energy costs, losses, and voltage deviations by integrating wind and solar PV-type DGs with BESS. A novel multi-objective function and an improved bi-directional coevolutionary (I-BiCo) Algorithm are employed to find the optimal RES and BESS placement and sizing, showing marked improvements over existing methods. Furthermore, statistical comparisons using hypervolume, objective function values (diversity), and near-global solutions (convergence) underscore the proposed algorithm’s superiority over existing MOEAs. The final non-dominated solution, obtained through fuzzy set theory, highlights simulation results that minimize power loss, achieve substantial energy savings, and smooth demand, particularly with the integration of BESS devices. Moreover, optimal network reconfiguration (ONR) is a key strategy for balancing load demand. Simulation results affirm that minimizing bi-objective and tri-objective functions, coupled with optimal feeder reconfiguration, significantly reduces power loss and enhances voltage profiles, approaching unity across all buses. The proposed ONR formulation, in conjunction with DGs and BESS, maximizes the overall performance of power distribution networks. Furthermore, the paper addresses various time-dependent constraints of BESS, DG, and ONR, formulating and efficiently solving these constraints by integrating different constraint-handling techniques with the proposed multi-objective evolutionary algorithm. The study contributes to academic discourse and provides practical insights for designing more efficient and sustainable power systems in the face of evolving energy landscapes.

The increasing expansion of the industrial and domestic sectors, combined with the integration of renewable energy sources, has led to an overload of the existing interconnected power systems, which in turn has led to significant bottlenecks. This confluence of factors manifests itself in severe frequency, voltage and tie-line power issues, highlighting the urgent need for intelligent control mechanisms. Automatic voltage regulation (AVR) and load frequency control (LFC) are key components that ensure the supply of high-quality energy to consumers while maintaining the nominal frequency, voltage and power deviations in the grid. These measures are essential to ensure the stability and safety of the IPS under these challenging conditions. This research addresses the control strategies for a four-area sophisticated IPS. The complexity of the system, which includes five generation units in each area, including gas, thermal reheating, hydropower and two renewable energy sources (wind and photovoltaic), requires a careful study of the control methods. In particular, the inclusion of various non-linear factors, such as the governor dead band (GDB), generation rate constraint (GRC) and boiler dynamics (BD) in each of the four areas, increases the realism of the study. In this context, a recently introduced meta-heuristic algorithm, the One-to-One-based Optimizer (OOBO), was used to determine the optimal parameters of the proportional-integral-proportional-derivative (PI-PD) controller. The tuning process involves using the integral of time multiplied by the squared error value (ITSE) as the error criterion for evaluating the fitness function. In the study, the voltage, frequency and power performance of the OOBO PI-PD controller is evaluated and compared with alternative control methods, namely Proportional-Integral-Derivative (PID), Integral-Proportional (I-P) and Integral-Proportional-Derivative (I-PD) controllers, all of which are tuned with OOBO. Under the influence of a 5% load step load perturbation (SLP), comprehensive comparisons show the superior performance of the OOBO PI-PD controller, which shows better responses in all areas. Furthermore, the reliability and effectiveness of the OOBO PI-PD method are validated by a sensitivity analysis that considers simultaneous variations of the turbine time constant and speed control within a range of ±25%. The results highlight the robustness of the OOBO-PI-PD control strategy and demonstrate its ability to effectively stabilize variations in terminal voltage, load frequency and tie-line power with a significantly shorter settling time within the complicated dynamics of a four-area IPS with nonlinearities.

Monitoring energy quality events is crucial for maintaining the stability and reliability of power grids. This paper presents a novel system integrating Discrete Wavelet Transform (DWT) and Extreme Learning Machine (ELM) for detecting and classifying power quality disturbances. The DWT performs multi-resolution analysis to decompose signals into time-frequency components, capturing various disturbances such as sags, swells, and harmonics. The ELM classifier, trained on these decomposed signals, achieves an impressive classification accuracy of 99.69%, significantly outperforming conventional methods like STFT with SVM (97.22%) and FFT with ANN (99.30%). The system was validated on a Xilinx Zynq-7000 SoC FPGA, demonstrating real-time processing capabilities with a latency of 1.5 milliseconds and a power consumption of 1.8 W. These findings highlight the effectiveness of the proposed method for real-time, accurate, and energy-efficient power quality monitoring.

The rapid adoption of Unmanned Aerial Vehicles (UAVs) in the construction industry has revolutionized safety, surveying, quality monitoring, and maintenance assessment. UAVs are increasingly used to prevent accidents caused by falls from heights or being struck by falling objects by ensuring workers comply with safety protocols. This study focuses on leveraging UAV technology to enhance labor safety by monitoring the use of personal protective equipment, particularly helmets, among construction workers. The developed UAV system utilizes the tensorflow technique and an alert system to detect and identify workers not wearing helmets. Employing the high-precision, high-speed, and widely applicable Faster R-CNN method, the UAV can accurately detect construction workers with and without helmets in real-time across various site conditions. This proactive approach ensures immediate feedback and intervention, significantly reducing the risk of injuries and fatalities. Additionally, the implementation of UAVs minimizes the workload of site supervisors by automating safety inspections and monitoring, allowing for more efficient and continuous oversight. The experimental results indicate that the UAV system’s high precision, recall, and processing capabilities make it a reliable and cost-effective solution for improving construction site safety. The precision, mAP, and FPS of the developed system with the R-CNN are 93.1%, 58.45%, and 27 FPS. This study demonstrates the potential of UAV technology to enhance safety compliance, protect workers, and improve the overall quality of safety management in the construction industry.

Copper-graphene nanocomposites have emerged as promising materials in tribology owing to their unique combination of high mechanical strength, excellent thermal conductivity (K), and self-lubricating behavior. Incorporation of graphene derivatives such as graphene nanoplatelets (GNPs) and carbon nanotubes (CNTs) has been shown to reduce the coefficient of friction (COF) by up to 60% and wear rates by 50–70%, primarily through the formation of stable graphene-based tribofilms. Advanced processing techniques, including spark plasma sintering (SPS), molecular-level mixing, and electrodeposition (ED), enable uniform graphene dispersion and strong interfacial bonding, yielding composites with 20–50% lower friction and 30–70% lower wear rates at reinforcement levels as low as 0.1–0.5 vol%. Furthermore, hybrid strategies, such as combining CNTs with silicon carbide, enhance wear resistance by up to 60% under high-load conditions. Mechanical enhancements have also been reported, with yield strength improvements exceeding 200% and hardness increases of 50–100% in optimized systems. However, excessive reinforcement (> 2.0 vol%) can lead to agglomeration, porosity, and performance degradation. This review critically analyzes the processing-structure-tribology relationships of Cu-Gr nanocomposites, emphasizing the balance between reinforcement level, dispersion quality, and interfacial engineering. The findings underscore their potential as next-generation self-lubricating materials for bearings, electrical contacts, and high-performance sliding systems.