Impact of TOU and RTP Demand Response on Emission Constrained Economic Scheduling of Grid-Connected Microgrid
Coordinated Control of BESS and Capacitor-Based Reactive Compensation for Enhanced Voltage and Frequency Stability in High-Voltage Power Systems
AI-Optimized Multilevel Power Converter for High Efficiency Electric Vehicle Traction Drives under Dynamic Road and Grid Conditions
Application of STATCOM to Improve System Profile for a DFIG HVDC Dependent Wind Farms Under Grid Faults
GNN-Based Real-Time Fault Localization in Three-Phase Matrix Converters using Waveform Signatures
Design and Development Of Paddy Cutter Using Solar Energy
Design Of Double-Input DC-DC Converter (DIC) Solar PV-Battery Hybrid Power System
Comparison of Harmonics, THD and Temperature Analysis of 3-Phase Induction Motor with Normal Inverter Drive and 5-Level DCMI Drive
Application of Whale Optimization Algorithm for Distribution Feeder Reconfiguration
Detection and Classification of Single Line to Ground Boundary Faults in a 138 kV Six Phase Transmission Line using Hilbert Huang Transform
The Modeling of Analogue Systems through an Object-Oriented Design Method
Circuit Design Techniques for Electromagnetic Compliance
A Technological Forecast for Growth in Solid-State Commercial Lighting using LED Devices
Testing of Analogue Design Rules Using a Digital Interface
Simulation and Transient Analysis of PWM Inverter Fed Squirrel Cage Induction Motor Drives
In existing world, Distributed Generations (DGs), comprising renewable and non-renewable sources, are prioritized over conventional generation. To integrate these DGs into the utility grid, the idea of the microgrid has emerged. The proper operation of a microgrid leads to reduced electricity costs, lower carbon emissions, and enhanced utility grid reliability. However, the operation of a microgrid is not that simple due to the integration of unpredictable renewable generation. Sometimes generation might be higher than demand, and vice versa. While energy storage systems are generally used to store excess generation, demand response programs are used to reduce the peak demand. The operational efficiency of a microgrid can be increased by optimizing its resources. This paper addresses the emission-constrained economic scheduling of a grid-connected microgrid while implementing TOU and RTP demand response programs. The optimization model is developed as MINLP and solved using the General Algebraic Modeling System (GAMS) software. The simulation outcomes justify the effectiveness of the presented model.
High-voltage transmission systems are increasingly dominated by converter-interfaced renewable energy resources, resulting in reduced system inertia, diminished reactive support, and heightened vulnerability to voltage and frequency instability. Traditional capacitor banks offer economically effective reactive compensation, but they do not have dynamic controllability, and Battery Energy Storage Systems (BESS) can act to support both fast-response active and reactive power but are hampered by converter limits as well as range of operation. It introduces a coordinated control model of continuous BESS-based reactive and active support and discrete capacitor switching that provides grid stability. The hybrid load-flow formulation is created by complementarity functions for the purpose of explicitly describing converter limits, switching constraints, and the P–f, Q–V control interaction. The proposed approach is verified on IEEE 14- bus and IEEE 30-bus systems under different disturbance states. The findings show that coordinated BESS–capacitor control offers a substantial gain in voltage margins, maximum loadability, converter stress ratio, and frequency recovery when compared with standalone methods of compensation. The framework constitutes an effective pathway for integrating power-electronic and passive compensation resources into future high-inertia-deficient transmission grids.
The transition toward electric mobility demands traction inverters that deliver high efficiency, thermal stability, and robust performance under diverse real-world driving conditions. This study introduces an AI-optimized three-level T-type Silicon Carbide (SiC) traction inverter designed for intelligent, energy-efficient electric vehicle (EV) propulsion. A Proximal Policy Optimization (PPO)-based controller is developed to dynamically regulate modulation index, switching frequency, and switching sequence, enabling coordinated loss-thermal optimization. The controller is trained on WLTP and UDDS drive cycles using a physics-based inverter, motor, and thermal model derived from SiC MOSFET datasheets. High-fidelity figures were produced using scientific plotting rather than simulation screenshots to ensure journal-standard reproducibility. The proposed inverter achieves significant improvements: switching losses are reduced by up to 52%, peak junction temperature is lowered by approximately 23%, and average drive-cycle efficiency is increased from 92.1% to 96.8%. These gains translate to an increase of 20-24 km in WLTP driving range for a 75-kWh battery EV. The results demonstrate that AI-assisted modulation can substantially enhance converter performance, reduce thermal stress, and support sustainable green-energy transportation systems.
This study presents the implementation of a system that used conversion of wind energy that integrated a doubly fed induction generator (DFIG) and static compensator (STATCOM) when exposed to various faults. The analysis focuses on critical aspects like DC-link capacitor voltage, rotor speed, and torque using electromagnetism, and AC-side voltage and current at HVDC terminal buses. WECS was connected to an HVDC line via a voltage source converter (VSC), with a fault occurring in the vicinity of the wind generator network. The endurance of DFIG was enhanced by STATCOM through the involved control method. This strategy effectively mitigated oscillations in both the supplies for voltage and torque, resulting in improved power flow. The study also investigated the impact of STATCOM on faults and the stability of the system considered. By implementing the proposed control method, it alleviates the adverse side of grid faults on windenergy conversion systems. The observation describes the potential of the STATCOM to strengthen the total performance and reliability of the considered system under fault situations.
Three-phase matrix converters (MCs) are emerging as compact and efficient AC-AC power conversion systems for applications requiring bidirectional power flow and high-frequency operation. However, their susceptibility to switching faults, such as open circuits, short circuits, or gate failures, necessitates advanced fault detection mechanisms to ensure system reliability. This paper proposes a Graph Neural Network (GNN)-based approach for real-time fault localization in matrix converters using waveform signatures. The method encodes both temporal and topological characteristics of the power system, enabling precise identification and classification of fault types. Experimental validations using simulated datasets generated from MATLAB/Simulink reveal high fault localization accuracy, demonstrating the potential of GNNs in predictive maintenance for power electronics.
