Power, Control, and Data Processing Systems

Abstract In this article it is tried to design a PFC (Power Factor Correction) circuit on the base of Buck – Boost converter in order to increase the power quality transmission of the input voltage source. The first step of this circuit’s processing is to rectify the AC input voltage to a DC voltage but due to the use of diodes (in the H – bridge structure; which conduct the current just in a short time of each period the input current’s THD (Total harmonic distortion) will increase. The lower THD of input current, the higher input power factor of the PFC converter as a result it is tried to increase the input power factor of the PFC converter. The output of the power factor correction process is a huge DC (Direct current) voltage which is regulated in the range of 400 volts with the help of control circuit. In this article the Boost mode of the Buck – Boost converter is considered.

Abstract This paper presents the design and simulation study of a compact four-port Multiple-Input Multiple-Output (MIMO) antenna with overall dimensions of 12×16×1 mm³. The design and full-wave electromagnetic analysis were performed using the High-Frequency Structure Simulator (HFSS). The antenna is comprised of four modified U-shaped radiating elements and an integrated claw-shaped parasitic element to achieve quad-band operation. The proposed design resonates at 2.5 GHz, 3.8 GHz, 5.4 GHz, and 6.9 GHz, covering key bands for Wi-Fi and 5G applications. Simulation results demonstrate good impedance matching, with a simulated return loss better than -10 dB at all target bands. The simulated peak gains are 1.8 dBi, 2.9 dBi, 3.95 dBi, and 4.45 dBi at 2.5 GHz, 3.8 GHz, 5.4 GHz, and 6.9 GHz, respectively. Furthermore, the MIMO performance is evaluated through simulation, showing a low simulated envelope correlation coefficient (ECC < 0.05) and high simulated diversity gain (DG > 9.95 dB), which confirms excellent channel isolation in simulation. Owing to its miniature size, simulated multi-band performance, and good simulated MIMO characteristics, the proposed antenna presents a promising

Abstract In recent years, environmental concerns and the depletion of fossil fuels have drawn significant attention to renewable energy resources, particularly wind turbines and solar energy, among power system operators and designers. However, the installation and commissioning costs of solar, wind, and other renewable-based generation systems are, on average, higher than those of conventional fossil-fuel power plants. Moreover, the expansion of grid infrastructure and the deployment of energy storage systems impose additional expenses. Another major challenge arises from integrating renewable energy sources into existing distribution networks. This process often requires infrastructure upgrades, modifications to load management systems, and the establishment of communication with smart devices within the grid. Consequently, electrical energy storage systems (EESS) are increasingly employed as complementary resources. In the presence of grid-connected storage units, energy exchange between the power grid and storage systems can be managed in a way that significantly enhances the resilience of the grid against unpredictable events. the problem of optimal placement and sizing of electrical energy storage systems in smart power networks involves numerous technical and economic challenges, while also contributing to enhanced grid resilience. Accordingly, the objective function of this study is formulated to account for both the design and operational costs of storage systems, while explicitly considering resilience indices as a key performance measure.

Abstract The Thyristor-controlled phase-shifting transformer (TCPST), a member of the flexible AC transmission systems (FACTS) family, offers an effective solution for mitigating transmission congestion while enhancing power system security and reliability. This paper introduces an innovative framework for determining the optimal location of TCPST to improve power system reliability under uncertainty. The proposed method accounts for power system uncertainties, including the availability of generation units and network components, modeled using forced outage rates (FORs) through scenario generation based on Monte Carlo simulation (MCS). To address the non-convex nature of the resulting mixed-integer non-linear programming problem, a linear approximation of power flow equations is employed, transforming the problem into a mixed-integer linear programming (MILP) formulation. The objective is to minimize load curtailment by identifying the optimal TCPST locations across different scenarios. The results are then ranked based on their contribution to reliability improvement, using the Probability of Load Curtailment (PLC) index. Extensive numerical studies on a modified reliability test system (RTS) demonstrate the efficacy of the proposed approach. Results show a 17.14% reduction in the annual Expected Energy Not Supplied (EENS) (from 646,640 MWh/year to 535,805 MWh/year) and a 25.29% improvement in the PLC index (from 0.344 to 0.257). These findings validate the effectiveness of the proposed stochastic optimization framework in enhancing power system reliability under uncertain conditions.

Abstract The growth in renewable energies (REs) has created new challenges for balancing supply and demand, and for managing uncertainty in power systems. Integrated energy systems (IESs) based on hydrogen are a promising solution for increasing network resilience. This paper provides a daily optimization framework for electricity and hydrogen integrated system, which fuel cell electric vehicles (FCEV) not only as a consumer, but also as mobile storage resources. For accurate uncertainty modeling, a scenario generation method based on the Copula function has been used to maintain the correlation between random variables (solar radiation, wind speed, load, and energy price). Further, to reduce the computational burden, the scenarios have been reduced with the K-means optimization algorithm. The problem is formulated as mixed integer linear programming (MILP) to simultaneously minimize operating costs and environmental pollution. The numerical results show that implementing demand response reduced the peak-to-valley difference by 16.6%, 16.59%, and 22.5%, decreased operational costs by 10.2%, 7%, and 9.3%, and lowered environmental costs by 3%, 1.5%, and 4.48% across different seasons.

Abstract The convergence of Artificial Intelligence (AI), Blockchain, and the Internet of Things (IoT) has emerged as a pivotal enabler for Industry 5.0 and next-generation smart city infrastructures. This study presents a PRISMA-guided systematic review of 82 peer-reviewed articles published between 2015 and 2025, retrieved from Scopus, Web of Science, IEEE Xplore, ScienceDirect, and SpringerLink. The analysis highlights a dominant layered architecture in which IoT facilitates realtime data acquisition, Blockchain ensures decentralized security, transparency, and trust, and AI supports intelligent analytics and autonomous decision-making. Beyond synthesizing existing research, this study proposes a unified taxonomy of AI–Blockchain–IoT integration models, identifies critical research gaps, and outlines a conceptual roadmap for scalable, human-centric implementations in industrial and urban environments. Despite rapid advancements, challenges such as interoperability, energy efficiency, latency, scalability, and the lack of unified standards remain significant barriers to large-scale deployment. The findings provide actionable insights and strategic guidance for developing secure, efficient, and deployable integrated frameworks for real-world applications.