Integrated Magneto Mechanical Power Transmission: A Comparative Study of Magnetic and Conventional Gearbox Systems
Modeling and Analysis of Mechanical Transmission Systems in Electromechanical Applications
A Novel Current-Source-Based Active Gate Driver for High Power IGBT Modules with Enhanced Switching Controllability
Pseudo Random Modulation Algorithms for Electromagnetic Interference Shaping in DC/DC Power Converters
Hybrid Grey Model and Machine Learning Approach for Outage Forecasting in Medium Voltage Power Distribution Networks
Multi Area Load Frequency Control of a Hybrid Power System with Advanced Machine Learning Controller: Case Study of Andhra Pradesh
A New Hybrid Cuckoo Search-Artificial Bee Colony Approach for Optimal Placing of UPFC Considering Contingencies
Efficiency and Investment Comparison of Monocrystalline, Polycrystalline, and Thin Film Solar Panel Types at Karabuk Conditions
Design of a Grid Connected PV System and Effect of Various Parameters on Energy Generation
Comparative Analysis of Harmonics by Shunt Active Filter using Resonant Current Control in Distribution System
Optimal Distributed Generation Placement for Maximum Loss Reduction using Teaching Learning Based Optimization through Matlab GUI
Development of Power Flow Controller for Grid Connected Renewable Energy Sources Using Lyapunov function
Detection and Location of Faults in Three Phase 11kv Underground Power Cables By Discrete Wavelet Transform (DWT)
Design of PV-Wind Hybrid Micro-Grid System for Domestic Loading
Applications of Artificial Neural Networks in various areas of Power System; A Review
The increasing demand for efficient, low noise, and low maintenance transmission systems in automotive applications has catalysed interest in magnetic gears. This study presents a comprehensive investigation into an innovative magnetic gearbox prototype that integrates clutch, torque limiter, and transmission functions into a single system. Utilizing both simulation and experimental methodologies, the research contrasts traditional mechanical gearboxes with magnetic counterparts, focusing on vibration behavior, torque transmission, NVH (Noise, Vibration, and Harshness) characteristics, and dynamic performance. A novel dual stage planetary magnetic gearbox is optimized using a Mult objective algorithm and validated through FEM simulation and real world testing. Results demonstrate significant improvements in noise reduction and system modularity, suggesting strong potential for magnetic transmission integration in electric and hybrid powertrains.
This study presents the modelling, simulation, and analysis of mechanical transmission systems with a focus on their application in electromechanical drive environments. By developing a lumped parameter model, the research investigates the dynamic interactions between gear elements specifically torque ripple, mesh stiffness variation, and damping characteristics. Using MATLAB/Simulink, the complete drivetrain was simulated under a range of load and speed conditions to evaluate stability, vibration behavior, and control responsiveness. Results indicate that time-varying stiffness and gear backlash are primary contributors to transmission instability, while effective damping and PI-based control substantially mitigate oscillations and shift shocks. The findings offer critical insights for the design and control of efficient, quiet, and reliable gear systems, particularly for hybrid and electric vehicle platforms. Future integration with experimental validation and real-time adaptive control is proposed to enhance real-world applicability.
High power insulated gate bipolar transistors (IGBTs) are integral to modern power conversion systems across railway traction, renewable energy, and HVDC transmission. Traditional gate drivers offer limited adaptability under dynamic conditions, leading to increased switching losses and reduced reliability. This paper proposes a novel current source based active gate driver (CS-AGD) tailored for high-power IGBT modules, enabling dynamic control of transient switching characteristics. We design and prototype a CS-AGD circuit, integrate it within a wide-range double-pulse test platform, and experimentally validate its performance under voltages up to 1000 V and currents up to 600 A. Results demonstrate significant reductions in switching losses and peak voltage/current overshoots compared to conventional gate driving strategies. The proposed solution offers enhanced controllability, opening new pathways for reliability- optimized and high-efficiency power electronic systems.
Electromagnetic interference (EMI) generated by DC/DC power converters presents a significant challenge, particularly within the 9–150 kHz frequency range. This paper introduces novel pseudo-random modulation (RanM) techniques designed to shape the EMI spectrum without compromising energy efficiency or power density. Implemented using a cyber-physical system (CPS) with a National Instruments PXI and FPGA platform, these techniques demonstrate effective EMI mitigation. The approach provides a 30% reduction in Frame Error Rate (FER) in Power Line Communication (PLC) systems and conforms with standard EMI compliance requirements. Comprehensive hardware implementation and validation confirm the effectiveness and practical feasibility of the proposed techniques.
In modern power systems, the increasing complexity of medium voltage (MV) distribution networks and rising environmental risks necessitate accurate and timely outage forecasting. This paper proposes a hybrid data driven framework that combines grey prediction modelling and support vector machine (SVM) classification for multi scale outage prediction. Annual and monthly outage counts are estimated using an optimized Grey Model GM (1,1) enhanced by Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). For day-ahead prediction, SVM is employed to classify outage risks using historical weather and fault data. The proposed method is validated using real operational data from the Italian distribution grid between 2008 and 2017. Experimental results demonstrate that the hybrid approach significantly improves prediction accuracy and supports proactive maintenance and resource allocation in MV networks.
