The application of directional overcurrent relays (DOCRs) plays an important role in protecting power systems and ensuring their safe, reliable, and efficient operation. However, coordinating DOCRs involves solving a highly constrained and nonlinear optimization problem. The primary objective of optimization is to minimize the total operating time of DOCRs by determining the optimal values for decision variables such as the time multiplier setting (TMS) and plug setting (PS). This article presents an efficient hybrid optimization algorithm that combines the modified firefly algorithm and genetic algorithm to achieve improved solutions. First, this study modifies the firefly algorithm to obtain a global solution by updating the firefly’s brightness and to prevent the distance between the individual fireflies from being too far. Additionally, the randomized movements are controlled to produce a high convergence rate. Second, the optimization problem is solved using the genetic algorithm. Finally, the solution obtained from the modified firefly algorithm is used as the initial population for the genetic algorithm. The proposed algorithms have been tested on the IEEE 3-bus, 8-bus, 9-bus and 15-bus networks. The results indicate the effectiveness and superiority of the proposed algorithms in minimizing the total operating time of DOCRs compared with other optimization methods presented in the literature.

The rise of electric vehicles (EVs) necessitates an efficient charging infrastructure capable of delivering a refueling experience akin to conventional vehicles. Innovations in Extreme Fast Charging (EFC) offer promising solutions in this regard. By harnessing renewable energy sources and employing sophisticated multiport converters, EFC systems can meet the evolving demands of EV refueling. A single-stage topology simplifies the converter design, focusing on efficient DC-AC conversion, vital for feeding solar power into the grid or charging stations. It provides power factor correction, harmonics filtering, and mitigates power quality issues, ensuring stable and efficient operations. Converters with Maximum Power Point Tracking (MPPT) capability facilitate the efficient integration of solar PV systems in charging stations, ensuring maximum solar energy utilization for EV charging. The ability to operate in different modes allows seamless integration with energy storage systems, storing excess solar energy for use during night-time or peak demand periods, enhancing overall efficiency and reliability. Advanced converters support bidirectional energy flow, enabling EV batteries to discharge back to the grid, aiding grid stability and energy management. However, robust control algorithms are needed to handle dynamic conditions like partial shading more effectively. Our review focuses on integrating renewable energy sources with multiport converters, providing insights into a novel EV charging station framework optimized for EFC topology. We highlight the advantages of multiport non-isolated converters over traditional line frequency transformers, particularly in medium voltage scenarios, offering enhanced efficiency and versatility for EFC applications.

As of now, there are multiple types of renewable energy sources available in nature which are hydro, wind, tidal, and solar. Among all of that the solar energy source is used in many applications because of its features are low maitainence cost, less human power for handling, a clean source, more availability in nature, and reduced carbon emissions. However, the disadvantages of solar networks are continuously depending on the weather conditions, high complexity of the solar energy storage, and lots of installation place is required. So, in this work, the Proton Exchange Membrane Fuel Stack (PEMFS) is utilized for supplying the power to the local consumers. The merits of this fuel stack are high power density, ability to work at very less temperature values, efficient heat maintenance, and water management. Also, this fuel stack gives a quick startup response. The only demerit of PEMFS is excessive current production, plus very less output voltage. To optimize the current supply of the fuel stack, a Wide Input Operation Single Switch Boost Converter (WIOSSBC) circuit is placed across the fuel stack output to improve the load voltage profile. The advantages of the WIOSSBC are less current ripples, uniform voltage supply, plus good voltage conversion ratio. Another issue of the fuel stack is nonlinear power production. To linearize the issue of fuel stack, the Grey Wolf Algorithm Dependent Fuzzy Logic Methodology (GWADFLM) is introduced in this article for maintaining the operating point of the fuel cell near to Maximum Power Point (MPP) place. The entire system is investigated by utilizing the MATLAB software.

The Internet of Things (IoT) consists of physical objects and devices embedded with network connectivity, software, and sensors to collect and transmit data. The development of the Internet of Things (IoT) has led to various security and privacy issues, including distributed denial-of-service (DDoS) attacks. Conventional attack detection methods face significant challenges related to privacy, scalability, and adaptability due to the dynamic nature of IoT environments. To address these limitations, this research proposes GraphFedAI, a novel framework that integrates adaptive session-based graph modeling, Pearson correlation-guided feature selection, interpolation-aware graph neural network (GNN) training, and federated learning to enable robust, scalable, and privacy-preserving DDoS detection in heterogeneous Internet of Things (IoT) networks.The framework represents the IoT network as dynamic graphs where communication patterns among devices are modeled as edges that evolve over time. Graph neural networks are utilized to extract both temporal and structural features from these graphs, thereby enhancing the accuracy of DDoS detection. Federated learning is incorporated to maintain data privacy by training models locally on each device without sharing raw data. This integration also ensures system scalability, as FL adapts training based on localized network topology.The system is evaluated using the CIC-IoT-2023 dataset, demonstrating its effectiveness in achieving high detection accuracy, low false positive rates, and strong resilience under dynamic IoT conditions.

This study extensively examined the impact of aluminium oxide (Al₂O₃) and titanium dioxide (TiO₂) nanoparticles addition in the biodiesel fuel derived from Guizotiaabyssinica (L.) oil. The assessment of fuel blends, which were created by combining nanoparticles and biodiesel was conducted using energy, exergy, and sustainability indices. The highest recorded power output of 2.81 kW was observed for the GAB20A engine operating at 1800 rpm. The experimental results revealed that the GAB20A exhibited the lowest fuel consumption, with a recorded value of 203 g/kWh, when operated at 1600 rpm among all the tested blend fuels. The blend GAB20A exhibited the highest level of energy efficiency at 1600 rpm of 29.5%, as determined by the study. Simultaneously, it was observed that GAB20 exhibited the lowest energy efficiency at 1200 rpm among all the blend fuels at 25%. The emission levels of nitrogen oxides (NOx) and carbon monoxide (CO) were observed to be quite low, although a little rise in carbon dioxide (CO₂) was detected. For validation of results the artificial neural network (ANN) was used and an average of 1.703% difference in energy efficiency, 2.246% decrease in exergy efficiency, and 1.416% difference in sustainability index was found.

Fiber Bragg gratings (FBGs) technology has demonstrated its suitability for many applications in recent fiber technologies. Sensing application is one of the main applications of FBGs. In this work, we present a comprehensive investigation for using apodized FBGs in sensing applications. Different evaluation parameters such as, reflectivity, sidelobes, and full-width-half-maximum (FWHM) are tested in order to determine the most proper apodization profile for sensors. According to our study, the Blackman apodization gives the best profile that can be used in sensing applications. The reflectivity of Blackman apodization is nearly unity with minimum sidelobes level, -60.3 dB, and narrow FWHM. The length of Blackman apodized FBG is 0.33 cm and Δn = 14.4 × 10 ^□4 and maximum reflectivity is 99.44%.