PV-grid Performance improvement through Integrated Intelligent Water Drop Optimization with Neural Network for Maximum Power Point Tracking
A Digital Healthcare Monitoring System with Real-Time Analysis
Advancements in Smart Meter Design and Integration for Enhanced Energy Management and Efficiency
Electric Vehicles in Modern Transportation: Environmental Impacts, Configurations, and Future Trends – A Review
GTO Technique Based Hybrid Power System Controller Design
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
This paper presents a novel integration scheme of solar Photovoltaic (PV) with a large-capacity Doubly Excited Induction Generator (DFIG)-based wind energy system. The proposed scheme leverages both the grid and rotor-side power converters of the DFIG to inject PV power into the grid, eliminating the need for a dedicated converter for PV power processing and offering a cost-effective PV-grid integration solution. The system effectively delivers a substantial amount of PV power to the grid when compared to an equivalent rating inverter used in conventional PV-grid systems. Moreover, the proposed scheme prevents circulating power during sub-synchronous operation in the presence of solar radiation, enhancing overall system efficiency. Additionally, the system's stability benefits from turbine inertia, allowing for higher PV penetration into the power grid. The intermittent complementary nature of solar PV and wind energy sources significantly improves the utilization of the converters. Furthermore, the proposed scheme minimally impacts Maximum Power Point Tracking (MPPT) for PV and wind sources, except in rare environmental glitches, which the PV power control algorithm is adept at handling. The study provides a comprehensive system model used to design the control strategy, supported by analysis, simulations, and experiments conducted on a laboratory prototype.
Electrical energy is transmitted through the electrical network from power generating station to the consumers. For this purpose, the overhead transmission line is used through which bulk power can be transferred. The conventional electrical network transmits the energy with power loss. The reason for the power loss is due to inherent characteristic of the transmission line and corona discharge which affects the performance of the transmission line. In this work, performance analysis of overhead transmission line is analyzed by developed MATLAB code and tested for different weather (fair and stormy) conditions by using peek's formula.
Nowadays the growth of Module Integrated Converters (MIC) concept is increasing. This concept was developed for PV applications to improve the efficiency of the converters. In this paper we propose a submodule MPP tracking algorithm to track the maximum power from the partially shaded cells. Generally, PV module have three submodules. Each submodule is formed by series connection of two strings. Here we take a different P&O algorithm for each submodule to track maximum power from the all three submodules. Each submodule will have their own DC-DC converter. In DC stage, DC-DC converters are connected in three configurations to serve sufficient energy to inverter for single phase grid connected systems.
The growing integration of renewable energy sources in hybrid microgrid systems necessitates effective frequency stability controllers. This paper presents a thorough comparison of Cuckoo Search Optimization (CSO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Proportional-Integral-Derivative (PID) controllers in a realistic 20 kW hybrid microgrid environment. Through meticulous simulations, tuning parameters for each method are fine-tuned for optimal performance. The study quantitatively evaluates critical metrics, including lagging time, overshoot, settling time, and steady-state error, providing insights into the strengths and weaknesses of each approach. The paper introduces a percentage improvement analysis, showcasing advancements made by each method. The outcomes serve as a benchmark for practical implementation, aiding in the selection of the most suitable controller tuning method for achieving enhanced frequency stability in hybrid microgrid systems.
This paper presents a hybrid algorithm using a Hybrid Firefly and Particle Swarm Optimization (HFPSO) algorithm to determine the optimal placement of SSSC devices. A multi-objective function is used to increase voltage stability, voltage profile and minimize overall power losses of the transmission system. At first, PSO algorithm is used to find the optimal SSSC location and further more to reduce the power losses and voltage profile enhancement to overcome the sub-optimal operation of existing algorithms. The HFPSO algorithm is used to determine the optimal placement of SSSC series FACTS device and verification of the proposed algorithm was achieved on standard IEEE 14-bus and IEEE 30-bus transmission systems in the MATLAB environment. Comprehensive simulation results of two different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms to enhance voltage stability and reducing active and reactive power losses in electrical power transmission systems.