American Journal of Electrical Engineering and Technology
The American Journal of Electrical and Electronic Engineering is an open-access, peer-reviewed journal dedicated to publishing high-quality research in the fields of electrical and electronic engineering. It serves as a platform for scholars and professionals to share innovative methodologies, applications, and case studies that advance the understanding and practice of these disciplines.
Articles in the American Journal of Electrical Engineering and Technology (AJEET) are distributed under the CC BY 4.0 license, allowing users to share and modify the material for any purpose, including commercial use. Proper attribution, a license link, and indication of changes are required, and no additional restrictions may be applied. Authors agree to these open-access terms upon submission.
Sparse View CT Reconstruction with Projection Consistency Constraints
Sparse-view CT reduces radiation exposure but increases streak artifacts due to insufficient projection angles. Motivated by convolution–self-attention denoising architectures such as CTLformer, this paper proposes a reconstruction framework that uses convolution–attention priors to regularize image updates while enforcing projection-domain consistency through an unrolled optimization scheme. The model alternates between a learned denoising block and a data-consistency step based on the forward projection operator. Experiments are conducted on two sparse-view CT benchmarks totaling 36,000 paired slices, with view counts ranging from 30 to 90. Comparisons against FBP, TV-regularized iterative reconstruction, RED-CNN post-processing, and transformerbased sparse-view models show PSNR gains of 1.2–2.0 dB and SSIM improvements of 0.015–0.028 under 60-view settings, with reduced streak intensity near high-contrast boundaries.
Copyright (c) 2026 James R. Walker, Emily K. Thompson, Daniel M. Chen (Author) https://creativecommons.org/licenses/by/4.0
Security Knowledge Distillation for Enhancing LLM Safety in Code Generation
We propose a distillation framework that transfers secure coding expertise into a compact model trained using expert demonstrations, static-analysis labels, and secure patterns. The distilled model (7B parameters) achieves 83% security compliance, nearly matching GPT-4’s 89% performance while requiring significantly less compute. Evaluated across CWE-20, CWE-78, CWE-89, the distilled model reduces injection vulnerabilities by 62% relative to baseline models. This work demonstrates that targeted distillation can teach smaller models to respect security best practices.
Copyright (c) 2026 Daniel Thompson, Sarah Mitchell, Jason Li (Author) https://creativecommons.org/licenses/by/4.0
Joint Energy-Delay Optimization of WuR-Assisted MAC Protocols in Energy Harvesting IoT
The proliferation of the Internet of Things has precipitated a critical need for sustainable energy management strategies, particularly in devices deployed in remote or inaccessible environments. Energy Harvesting has emerged as a promising solution to extend theoperational lifetime of such networks indefinitely; however, the stochastic nature of environmental energy sources introduces significant instability. Simultaneously, Wake-up Radio technology offers a pathway to minimize idle listening, which is the primary source of energy wastage in traditional Medium Access Control protocols. This paper presents a comprehensive theoretical and simulation-based analysis of a novel Medium Access Control protocol that integrates Wake-up Radio hardware with Energy Harvesting capabilities. We formulate a joint optimization problem that dynamically adjusts the wake-up intervals and transmission parameters based on the instantaneous energy state and data buffer occupancy. The primary objective is to balance the conflicting requirements of minimizing packet delivery latency and maximizing energy neutrality. Through rigorous system modeling and extensive simulations, we demonstrate that the proposed approach significantly outperforms static dutycycling mechanisms. The results indicate that our adaptive protocol achieves a superior Pareto optimal frontier, ensuring reliable data delivery while maintaining the energy neutrality of the sensor nodes under variable harvesting conditions.
Copyright (c) 2026 Liang Zhao, Christopher Bennett (Author) https://creativecommons.org/licenses/by/4.0
Multidisciplinary Monte Carlo Robust Optimization for AUVs under Polar Ice Multi-Source Uncertainties
The exploration of polar regions has become increasingly critical due to rapid climate change and the strategic significance of Arctic resources. Autonomous Underwater Vehicles (AUVs) serve as the primary modality for data acquisition in these harsh, ice-covered environments. However, the operational reliability of AUVs is severely compromised by multi-source uncertainties, including complex under-ice hydrodynamics, acoustic communication failures, variable sea ice draft, and stochastic sensor errors. Traditional deterministic optimization methods often fail to account for these variabilities, leading to designs that are optimal in theory but fragile in practice. This paper proposes a comprehensive Multidisciplinary Design Optimization (MDO) framework integrated with Monte Carlo simulations to enhance the robustness of AUVs operating under polar ice. We define a stochastic coupling mechanism that links hydrodynamic, structural, energy, and navigation subsystems. By embedding Monte Carlo sampling within the optimization loop, we evaluate the probability of mission success and enforce reliability constraints against environmental and systemic uncertainties. The results demonstrate that the proposed robust optimization approach significantly reduces the variance in mission performance metrics compared to deterministic baselines. We provide a detailed analysis of the trade-offs between optimality and reliability, offering a theoretical foundation for the next generation of polar-class AUVs.
Copyright (c) 2026 John Smith, Emily Davis, Robert Johnson, Sarah Wilson (Author) https://creativecommons.org/licenses/by/4.0
Diffusion Models for Automated Standard Cell Layout Generation in Advanced Node Design
Standard cell layout generation in advanced semiconductor nodes presents escalating complexity due to stringent design rule constraints, multi-patterning lithographic requirements, and severely diminishing layout flexibility at sub-5nm feature sizes. This paper proposes a diffusion model-based generative framework for automating the synthesis of standard cell layouts at 5nm and below technology nodes, addressing a critical bottleneck in modern electronic design automation (EDA) flows. The proposed approach employs a denoising diffusion probabilistic model (DDPM) conditioned on cell functionalspecifications, parasitic extraction targets, pin accessibility requirements, and design rule check (DRC) parameter vectors extracted directly from process design kits (PDKs). Inspired by the forward-reverse diffusion process originally demonstrated on structured 2D data distributions — wherein a learned reverse Markov chain reconstructs complex geometric patterns from Gaussian noise — the framework applies this principle to recover DRC-compliant multi-layer layout geometries through an iterative denoising trajectory guided by cell-specific conditioning signals. A U-Net denoising backbone with multi-scale encoder-decoder structure and cross-attention conditioning injection captures both local geometric precision and global layout topology simultaneously,enabling the generative model to produce diverse yet constraint-compliantlayout solutions. A DRC-aware auxiliary training loss explicitly penalizes constraint violations at each denoising step, directly embedding rule compliance into the learned score function. Experimental evaluation on an industry-representative benchmark suite of 48 standard cell types atthe 5nm technology node demonstrates that the proposed method achieves a DRC violation rate reduction of 34.2% compared to baseline GAN approaches, generates valid layouts for 98.3% of benchmark cells, and produces performance-power-area (PPA) metrics within 8.3% of reference engineer-designed layouts. The framework reduces average layout generation time to under two minutes per cell on a single GPU, representing an order-of-magnitude improvement over conventional manual workflows. These findings establish diffusion models as a highly promising paradigm for intelligent EDA at advanced technology nodes, with significant implications for standard cell library development timelines and design-technology co-optimization research.
denoising diffusion probabilistic models
Copyright (c) 2026 Yutong Shen (Author) https://creativecommons.org/licenses/by/4.0
Cascading Failure Modeling and Rapid Recovery of Coupled WaterPower Networks under Earthquakes
The interdependence between critical infrastructure systems, particularly power grids and water distribution networks, has created a complex landscape of vulnerability where failures can propagate across domain boundaries. This paper presents a comprehensive modeling framework for analyzing cascading failures in coupled water-power networks subjected to seismic hazards and proposes a rapid recovery strategy to mitigate systemic losses. We first establish a mathematicallyrigorous interdependent network model that accounts for the physical and functional coupling between electric power substations and water pumping facilities. Unlike traditional topological approaches, this study integrates hydraulic and power flow constraints to realistically simulate the propagation of failure following an earthquake. Subsequently, we introduce a resilience-oriented recovery optimization algorithm designed to prioritize the restoration of critical nodes that facilitate the reactivation of the largest connected components. Simulation results demonstrate that the proposed recovery strategy significantly outperforms random and static repair protocols, reducing the duration of service outages and minimizing unserved demand. The findings underscore the critical importance of synchronized restoration planning in managing modern infrastructure resilience.
Copyright (c) 2026 Zainab Mahmoud, Sarah Mitchell (Author) https://creativecommons.org/licenses/by/4.0
Multi Sensor Pedestrian Re Identification with Uncertainty Guided Fusion of Vision Language and LiDAR
In autonomous driving, pedestrian appearance cues from cameras can be unreliable under glare, fog, or low illumination, while LiDAR offers complementary geometry signals. Building on uncertainty-aware CLIP-based modal modeling, this work introduces a fusion framework that integrates vision–language embeddings with LiDAR-derived shape descriptors using uncertainty gated feature mixing. The gating module increases reliance on LiDAR when visual uncertainty rises and maintains vision–language dominance in clear conditions. Experiments are conducted on multi sensor datasets covering 210,000 paired camera–LiDAR observations and 26,000 identities. Baselines include camera-only ReID (OSNet, TransReID), CLIP-based ReID, and naive concatenation fusion. The proposed method improves overall mAP by 3.3%–4.8% and yields larger gains of 6.0%–7.5% in low-light subsets, while adding less than 8% inference latency compared with camera-only models.
Copyright (c) 2026 Carlos Martínez, Lucía Fernández, Javier Ruiz (Author) https://creativecommons.org/licenses/by/4.0
Design, Modeling, and Performance Analysis of an Enhanced Doubly Fed Reluctance Generator (DFRG) for Efficient and Reliable Renewable Energy Systems
This study investigates the performance and control strategies of an Enhanced Doubly Fed Reluctance Generator (DFRG) system, with the aim of improving torque generation, efficiency, and voltage stability in renewable energy applications. The motivation stems from limitations in traditional generators such as the Permanent Magnet Synchronous Generator (PMSG) and Doubly Fed Induction Generator (DFIG), including higher costs, reduced reliability under transient loads, and limited efficiency across operating ranges. The study addresses these challenges by employing analytical modeling and simulation to analyze the magnetic and dynamic behavior of the DFRG system. A comprehensive methodology was implemented using MATLAB/Simulink to simulate and evaluate the inductance variation, torque production mechanisms, stator voltage dynamics, vector and direct torque control strategies, and battery state-of-charge under hybrid scenarios. The study also conducted a comparative assessment of efficiency, power quality (THD), cost, reliability, and voltage regulation under diverse load conditions. Quantitatively, the DFRG system demonstrated a smooth inductance variation between 0.05 H and 0.15 H, producing torque peaks up to ±884 Nm with stator current at 420 A. Under vector control, torque reached 45 Nm, while DTC yielded 38 Nm, showing robust response. Voltage peaks were observed near 220 V, with flux linkage around 63 Wb. Efficiency under rated load was 92%, and up to 97% under high-speed, full-load conditions, surpassing the PMSG’s 96% peak. In fault conditions, efficiency dropped to 0%, indicating critical reliability issues in abnormal operations. The DFRG also maintained a THD of 2.1% under rated load, rising to 28.7% during faults. Voltage regulation was within 4.17%, confirming voltage stability. The hybrid system simulation showed effective energy cycling with a battery SOC increase from 50% to nearly 100%, and a discharge down to 10% within a 24-hour period. Cost and reliability analysis revealed DFRG as the most cost-effective ($800/kW) and reliable (0.98) compared to DFIG ($950/kW, 0.95) and PMSG ($1100/kW, 0.97). The findings support policy implications favoring the deployment of DFRG systems in renewable integration strategies where cost, efficiency, and reliability are prioritized. Recommendations include the incorporation of fault mitigationstrategies and filtering technologies to maintain power quality and continuity in hybrid and variable-speed operations
Copyright (c) 2026 Emmanuel C. Obuah, B. A. Wokoma, Oto-Obong John, Peace Biragbara, Ubong J. Joseph (Author) https://creativecommons.org/licenses/by/4.0
Selective Modality Weighting for Robust Anomaly Identification in Noisy Observability Environments
Anomaly detection in multimodal sensor environments presents significant challenges when data quality varies across different modalities due to environmental noise, sensor degradation, or communication disruptions. This paper proposes a Selective Modality Weighting (SMW) framework that dynamically adjusts the contribution of each sensory modality based on its reliability and information quality for robust anomaly identification. The framework integrates an adaptive attention mechanism that learns modality-specific confidence scores during training and applies selective weighting during inference to suppress unreliable data streams while amplifying trustworthy signals. Our methodology combines deep autoencoder architectures with cross-modal consistency validation to establish baseline normality patterns across heterogeneous sensor types.Experimental evaluations on industrial monitoring datasets demonstrate that SMW achieves superior anomaly detection accuracy compared to static fusion approaches, particularly in scenarios with simulated sensor noise levels ranging from 10% to 40%. Results indicate a 12.7% improvement in F1-score over baseline multimodal methods when operating under high-noise conditions. The proposed framework offers a practical solution for deployment in real-world environments where sensor reliability cannot be guaranteed, contributing to more resilient anomalydetection systems for critical infrastructure monitoring, industrial quality control, and autonomous system safety.
Copyright (c) 2026 Haolin Zheng, Mingrui Cao, Andrew Keller (Author) https://creativecommons.org/licenses/by/4.0
A Dynamic Policy Engine for Access Control in Zero Trust Architecture
Over-the-air (OTA) updates in aviation and industrial systems need reliable and secure access control to stop unauthorized actions during software updates. This study developed a zero-trust OTA access control framework using the Open Policy Agent (OPA) and a context-based verification module. The system checked each access request in real time using user, device, and session data. Tests were done in both aviation and industrial cloud setups with more than 10,000 access requests under different load conditions. The results showed an average policy delay of 4.3 ms, a false rejection rate of 0.6%, and an 85% decrease in unauthorized access compared with a standard role-based control model. These results show that the proposed zero-trust system improves OTA security while keeping low delay and steady operation. The framework can be used in secure software updates for safety-critical and industrial systems, but further large-scale and long-term testing is needed to confirm its stability and performance.
Copyright (c) 2026 Yuna Kim1, Carlos M. Ortega2, Sophie L. Nguyen3, Elena V. Petrova4 (Author) https://creativecommons.org/licenses/by/4.0
Fairness-Aware Policy Gradient Methods for Mitigating Demographic Bias in AI-Driven Student Performance Prediction
The proliferation of artificial intelligence systems in educational contexts has raised critical concerns regarding algorithmic fairness and demographic bias in student performance prediction. Traditional machine learning models, while achieving high predictive accuracy, often perpetuate or amplify existing disparities across gender, ethnicity, and socioeconomic backgrounds. This paper introduces a novel fairness-aware policy gradient framework that integrates demographic parity constraints into reinforcement learning architectures for student performance forecasting. Our approach employs constrained policy optimization to balance prediction accuracy with fairness metrics, utilizing a dual-objective reward function that penalizes discriminatory predictions while maintaining educational relevance. Through comprehensive experiments on diverse student datasets, we demonstrate that policy gradient methods with fairness constraints achieve superior bias mitigation compared to conventional supervised learning approaches, reducing demographic disparity by an average of 37.4% while preserving predictive performance within 2.8% of baseline models. The proposed framework incorporates adaptive fairness thresholds that dynamically adjust based on observed demographic distributions, enabling robust performance across heterogeneous educational environments. Our findings suggest that reinforcement learning-based fairness mechanisms provide a viable pathway for developing equitable AI systems in education while maintaining operational effectiveness for academic intervention strategies.
Copyright (c) 2026 Ingrid Solheim, Marco De Santis (Author) https://creativecommons.org/licenses/by/4.0
Non-Equilibrium Dynamics in Optical Matter Systems under Variable Light Fields: A Computational Fluid Dynamics Perspective
The self-organization of nanoparticles into ordered arrays mediated by optical binding forces, known as optical matter, represents a frontier in soft matter physics and photonics. While static optical binding has been extensively characterized, the behavior of these systems under time-varying light fields remains less understood, particularly regarding the role of hydrodynamic interactions. This study employs a coupled Computational Fluid Dynamics (CFD) and electrodynamics simulation framework to investigate the non-equilibrium dynamics of optical matter in aqueous environments. By explicitly resolving the solvent velocity field using the Navier-Stokes equations alongside the Langevin dynamics of the particles, we elucidate the complex interplay between optical scattering forces and fluid-mediated coupling. Our results demonstrate that under variable illumination—specifically oscillating and rotating optical traps—hydrodynamic interactions significantly alter the trajectory and stability of particle arrays compared to predictions based solely on conservative optical forces. We observe emergent collective motion and fluid pumping effects that suggest new mechanisms for controlling transport at the mesoscale. This work underscores the necessity of full hydrodynamic treatments in modeling dynamic optical matter systems and offers a predictive platform for designing optically reconfigurable colloidal machines.
Copyright (c) 2026 Austra Lian (Author) https://creativecommons.org/licenses/by/4.0https://creativecommons.org/licenses/by/4.0
IoT-Based Real-Time Monitoring Systems for Electrical Distribution Networks: Enhancing Reliability and Operational Efficiency
The increasing complexity and demand on electrical distribution networks have emphasized the need for real-time, scalable, and intelligent monitoring solutions. Internet of Things (IoT)-based technologies offer a transformative approach to enhance network reliability, detect faults early, and optimize load distribution. This paper explores the architecture, components, applications, and challenges of IoT-based real-time monitoring systems in electrical distribution networks. Case studies from Pakistan are presented to highlight local implementations, technological gaps, and future directions for smart grid integration. The findings underscore the potential of IoT to reshape traditional power systems into data-driven, self-diagnostic infrastructures.
Copyright (c) 2025 Anna Kim (Author) https://creativecommons.org/licenses/by/4.0
Advanced Control Algorithms for Microgrids and Smart Grids: Optimization, Stability, and Integration Challenges
The evolution of power distribution systems into smart grids and microgrids necessitates advanced control mechanisms to ensure reliability, stability, and optimization. This paper explores cutting-edge control algorithms, including model predictive control (MPC), adaptive neuro-fuzzy inference systems (ANFIS), distributed control schemes, and AI-driven controllers for efficient energy management in microgrids. Emphasis is placed on their implementation in renewable energy-integrated systems, real-time optimization, and fault-tolerant operations. The study also presents case scenarios and simulation-based analyses to highlight performance improvements across various parameters. These advanced algorithms signify the shift from centralized control to intelligent, decentralized frameworks, which are crucial for the future of sustainable energy networks.
Copyright (c) 2025 Sarah Williams (Author) https://creativecommons.org/licenses/by/4.0
The Future of Electrical Engineering in the Aerospace Industry
Electrical engineering continues to be a cornerstone of aerospace innovation, driving advances in avionics, propulsion systems, communication, and autonomous technologies. The future of electrical engineering in aerospace will be shaped by emerging trends such as electrification of aircraft, smart sensor networks, advanced power management, AI-enabled systems, and cyber-physical integration. These developments promise enhanced efficiency, safety, and sustainability in aerospace operations. This article explores key areas where electrical engineering will transform aerospace technology and discusses challenges and opportunities ahead.
Copyright (c) 2025 Carlos Martinez (Author) https://creativecommons.org/licenses/by/4.0
Wireless Power Transfer in Electric Vehicles: A Comprehensive Review
Wireless Power Transfer (WPT) technology is emerging as a promising alternative to conventional plug-in charging methods for Electric Vehicles (EVs). It offers significant advantages such as convenience, safety, and enhanced user experience. This review comprehensively discusses the state-of-the-art WPT techniques, system architectures, coupling mechanisms, and standards relevant to EV charging. Various WPT methods including inductive coupling, resonant inductive coupling, capacitive coupling, and microwave power transfer are analyzed in terms of efficiency, range, power levels, and system complexity. The paper also addresses challenges such as alignment issues, electromagnetic interference, thermal management, and safety standards. Future trends including dynamic charging, integration with smart grids, and advances in materials and system design are examined. This review aims to provide researchers and industry practitioners with a detailed overview of WPT for EVs, highlighting current developments and directions for future research.
Copyright (c) 2025 Sophia Lee (Author) https://creativecommons.org/licenses/by/4.0
The Role of Electrical Engineering in Artificial Intelligence Hardware
Artificial Intelligence (AI) has rapidly transformed numerous industries, and its hardware backbone is critical for efficient AI processing. Electrical Engineering plays a pivotal role in designing, developing, and optimizing AI hardware components such as processors, memory units, sensors, and communication interfaces. This article explores the intersection of electrical engineering and AI hardware, emphasizing advancements in neuromorphic computing, ASICs, FPGAs, and system-on-chip (SoC) designs that accelerate AI tasks. Additionally, it discusses challenges related to power consumption, speed, and scalability, highlighting ongoing research in low-power architectures and hardware-software co-design. The article aims to provide a comprehensive overview of how electrical engineering innovations enable the deployment of AI across applications from autonomous vehicles to smart IoT devices
Copyright (c) 2025 David Johnson (Author) https://creativecommons.org/licenses/by/4.0
A Transformer-based Approach to Real-Time Task Scheduling in Latency-Critical Edge Systems
Edge computing has emerged as a critical paradigm for supporting latency-sensitive applications by bringing computational resources closer to data sources. However, efficient task scheduling in edge systems remains a significant challenge due to the heterogeneous nature of resources, dynamic workload patterns, and stringent latency requirements. Traditional heuristic-based scheduling algorithms often struggle to adapt to the rapidly changing conditions inherent in edge environments. This paper presents a novel Transformer-based approach for real-time task scheduling in latencycritical edge systems. By leveraging the self-attention mechanism of Transformer architecture, our proposed method learns complex temporal dependencies and spatial relationships among tasks and resources, enabling intelligent scheduling decisions that minimize response latency while maintaining high resource utilization. The methodology incorporates a multi-head attention mechanism to capture diverse task characteristics including priority levels and deadline constraints, positional encoding to preserve temporal information, and a reinforcement learning framework to optimize long-term performance. Experimental results demonstrate that the proposed approach achieves up to 37% reduction in average task completion time and 42% improvement in deadline satisfaction rate compared to state-of-the-art baseline algorithms including completion time-based assignment with hierarchical weighted assignment and weighted round-robin scheduling methods. The system maintains deadline satisfaction rates above 94% for time-critical tasks even under high workload intensities, adapting effectively to resource heterogeneity across edge-cloud continuum architectures.
Copyright (c) 2025 Klara Vuković, Alessandro Romano (Author) https://creativecommons.org/licenses/by/4.0
Modern Techniques for Power System Optimization and Control
Power systems worldwide are evolving rapidly to accommodate the growing demand for energy, integration of renewable resources, and the increasing complexity of grid operations. Modern techniques for power system optimization and control play a vital role in enhancing system reliability, efficiency, and sustainability. This article reviews contemporary optimization methods including heuristic and metaheuristic algorithms, machine learning-based approaches, and advanced control strategies such as Model Predictive Control (MPC) and decentralized control. The integration of smart grid technologies and real-time data analytics further enriches system operation. We also discuss challenges and future directions in the context of renewable integration, cyber-physical security, and computational scalability.
Copyright (c) 2025 Dr. Ayesha Malik (Author) https://creativecommons.org/licenses/by/4.0
Artificial Intelligence in Predictive Fault Diagnosis for Electrical Networks
The integration of Artificial Intelligence (AI) in predictive fault diagnosis for electrical networks has revolutionized power system reliability and maintenance efficiency. This paper explores state-of-the-art AI techniques including machine learning, deep learning, and hybrid models applied to fault detection, classification, and prediction in electrical distribution and transmission networks. Emphasis is placed on data-driven approaches using real-time sensor data and historical fault records. Challenges such as data imbalance, noisy measurements, and computational constraints are discussed. A case study on the Pakistani power grid demonstrates the effectiveness of AI-driven fault diagnosis in reducing downtime and improving grid resilience.
Copyright (c) 2025 Dr. Ayesha Siddiqui (Author) https://creativecommons.org/licenses/by/4.0
Design Considerations for Smart Cities’ Electrical Power Infrastructure
Smart cities integrate advanced digital technologies with urban infrastructure to enhance sustainability, efficiency, and quality of life. Central to this transformation is the design of a resilient, flexible, and efficient electrical power infrastructure that supports the increasing demands of IoT devices, electric vehicles, renewable integration, and smart grid functionalities. This paper explores key design considerations for smart cities’ electrical power infrastructure, including grid modernization, distributed generation, energy storage, demand response, cybersecurity, and environmental impact. Challenges and future research directions are also discussed to guide planners, policymakers, and engineers in developing sustainable smart city power systems.
Copyright (c) 2025 Dr. Ayesha Khan (Author) https://creativecommons.org/licenses/by/4.0
Impact of High-Efficiency Electrical Machines in Industry 4.0
Industry 4.0 represents the fourth industrial revolution characterized by digitalization, automation, and smart manufacturing. High-efficiency electrical machines play a pivotal role in enabling the energy-efficient and reliable operation of industrial processes in this new era. This paper explores the impact of high-efficiency electrical machines, including motors, generators, and actuators, within Industry 4.0 frameworks. We discuss advancements in machine design, control techniques, integration with IoT and AI, and their contribution to energy savings, operational reliability, and reduced carbon footprint. Furthermore, challenges and future research directions in implementing these machines in smart factories are outlined.
Copyright (c) 2025 Dr. Ayesha Tariq, (Author) https://creativecommons.org/licenses/by/4.0
Developments in Electrical Engineering for Energy Efficiency in Manufacturing
The manufacturing sector is a significant consumer of electrical energy, prompting the need for improved energy efficiency to reduce operational costs and environmental impact. Electrical engineering innovations have played a pivotal role in enhancing energy efficiency in manufacturing processes by optimizing electrical systems, incorporating advanced control strategies, and deploying smart energy management solutions. This article reviews recent developments in electrical engineering techniques and technologies aimed at improving energy efficiency in manufacturing industries, including motor drives, power electronics, renewable integration, and smart grid applications. Case studies from Pakistani industries are also discussed, highlighting practical implementations and challenges.
Copyright (c) 2025 American Journal of Electrical Engineering and Technology https://creativecommons.org/licenses/by/4.0
The Impact of Energy Storage on Electrical System Stability
The integration of energy storage systems (ESS) into electrical grids has become a critical factor in enhancing system stability amid increasing penetration of renewable energy sources and fluctuating loads. This paper investigates the role of various energy storage technologies in improving voltage stability, frequency regulation, and overall grid resilience. Through modeling and simulation studies, the impact of battery energy storage systems (BESS), flywheels, and supercapacitors on mitigating disturbances and maintaining system equilibrium is analyzed. Results indicate that appropriately sized and strategically located ESS can significantly reduce frequency deviations and improve transient response, thereby enhancing the reliability and robustness of modern electrical power systems.
Copyright (c) 2025 Dr. Amina Khan (Author) https://creativecommons.org/licenses/by/4.0
Power System Management Using Distributed Ledger Technologies
The integration of Distributed Ledger Technologies (DLT), such as blockchain, in power system management is rapidly gaining attention for its potential to enhance grid transparency, security, and operational efficiency. This paper explores the role of DLT in power systems, focusing on decentralized energy trading, grid data integrity, and smart contract-based automation for demand response. It highlights the benefits of DLT in managing distributed energy resources (DERs), securing transactions, and improving trust among stakeholders. Challenges related to scalability, energy consumption, and regulatory compliance are also discussed. Case studies from pilot projects demonstrate promising results, underscoring DLT’s transformative potential in modernizing power grids.
Advanced Electrical Materials for High-Temperature Superconducting Applications
High-temperature superconductors (HTS) have revolutionized electrical engineering by enabling lossless power transmission, powerful electromagnets, and advanced electronic devices. This paper reviews recent advances in electrical materials tailored for HTS applications, focusing on novel fabrication techniques, material properties, and integration challenges. Emphasis is placed on ceramic-based cuprates, iron-based superconductors, and emerging composite materials that offer enhanced critical temperature (Tc), critical current density (Jc), and magnetic field tolerance. Additionally, the paper explores material engineering strategies to improve thermal stability and mechanical robustness for industrial deployment. Finally, the study highlights the ongoing research efforts in Pakistan and proposes future directions for material innovations in HTS applications.
Copyright (c) 2025 Dr. Saad Ahmed (Author) https://creativecommons.org/licenses/by/4.0
Electrical System Monitoring with Big Data and Artificial Intelligence
The integration of Big Data analytics and Artificial Intelligence (AI) into electrical system monitoring has transformed the traditional approaches to maintaining grid stability, fault detection, and predictive maintenance. This article reviews the state-of-the-art techniques that leverage massive datasets generated by smart meters, sensors, and grid components, combined with AI algorithms such as machine learning and deep learning, to enhance real-time monitoring and fault diagnosis in electrical power systems. We discuss challenges specific to the Pakistani power grid, including data heterogeneity, scalability, and cybersecurity concerns, and propose frameworks for AI-driven intelligent monitoring. Case studies demonstrate significant improvements in outage prediction, load forecasting, and system reliability. The article concludes with future research directions for AI and Big Data convergence in smart grid applications.
Energy Efficiency Improvements in Large-Scale Electrical Power Plants
Energy efficiency in large-scale electrical power plants is a critical factor for sustainable energy production and environmental conservation. This article reviews key technological, operational, and management strategies to improve efficiency in thermal, hydro, and combined cycle power plants. Emphasis is given to advanced materials, combined heat and power (CHP) integration, digital monitoring systems, and retrofitting existing plants with modern equipment. The study also highlights challenges faced by Pakistani power plants and proposes actionable recommendations to enhance overall plant performance and reduce carbon emissions. Quantitative analyses demonstrate potential efficiency gains and environmental benefits, emphasizing the role of policy and investment in upgrading aging infrastructure.
Copyright (c) 2025 Dr. Saima Khan (Author) https://creativecommons.org/licenses/by/4.0
Renewable Energy Systems: Design and Optimization for Urban Environments
The rapid urbanization worldwide has escalated the demand for sustainable and clean energy solutions within urban environments. Renewable energy systems (RES) designed for cities face unique challenges due to limited space, diverse load demands, and the need for integration with existing infrastructure. This paper reviews the design principles and optimization techniques for deploying solar photovoltaic (PV), wind, and hybrid renewable energy systems in urban settings. It emphasizes site-specific considerations, energy storage integration, and smart grid compatibility. Case studies from major Pakistani cities demonstrate the technical and economic feasibility of optimized renewable installations. Advanced optimization methods such as genetic algorithms, particle swarm optimization, and multi-objective optimization are explored to maximize energy yield, minimize costs, and reduce environmental impacts. The study concludes with recommendations for policy frameworks and urban planning to foster renewable energy adoption in Pakistan’s urban environments.
Advanced Control Techniques for Electric Power Distribution Systems
Electric power distribution systems are critical infrastructures facing challenges like increasing demand, integration of distributed energy resources (DERs), and maintaining reliability and power quality. This paper reviews and analyzes advanced control techniques applied to modern distribution systems, emphasizing adaptive, predictive, and decentralized control methods. The study evaluates their effectiveness in voltage regulation, fault detection, load balancing, and integration of renewable energy. Simulation results and practical case studies demonstrate significant improvements in system stability and operational efficiency. The paper also discusses challenges and future research directions in control strategies for smart grids.
Copyright (c) 2024 Dr. Saad Ahmed Khan (Author) https://creativecommons.org/licenses/by/4.0