A Multichannel Pulse Power Supply for Pulsed RF Amplifiers
Design and Analysis of PV-Based ZETA Converter for EV Charging
Simulation and Analysis of Single Phase Bidirectional Totem Pole On-Board Charger
IOT-Based Smart Plant Monitoring System using NodeMCU
Review on Multi Bit Flip-Flop Enhanced Shift Register: A Low Power Solution for UART
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
The ability to convert energy from one form to another is essential for both survival and the advancement of society. Wind and solar energy represent the most abundant and widely accessible renewable energy sources globally. It offers significant potential for sustainable power generation and reducing dependence on fossil fuels. Because of the tremendous advancements in power electronic systems in recent years, the output of electricity from wind and photovoltaic energy sources has expanded dramatically. The nonlinear programming (NLP) optimization method was used to determine the maximum electrical efficiency of the grid-connected SOFC subject to the constraints of fuel utilization factor, stack temperature, and output active power based on the benchmark Solid Oxide Fuel Cell (SOFC) dynamic model for power system studies and the analysis of the SOFC operating conditions. By solving the NLP issue with the power required by the air compressor, the optimal operating conditions of the grid-connected SOFC were determined. With the optimal SOFC operating conditions for maximum efficiency operation obtained at different active power output levels, a hierarchical load tracking control scheme for the grid-connected SOFC was proposed to have maximum electrical efficiency operation while keeping the stack temperature bounded.
This research involves the implementation of a dual-input DC-DC boost converter for integrating solar and fuel cell energy sources with a smart grid. The growing demand for renewable energy sources necessitates the efficient utilization and integration of multiple energy systems. The proposed dual-input converter enables simultaneous extraction and management of power from solar panels and fuel cells, resulting in improved energy generation and utilization. The converter incorporates a smart grid interface, allowing bidirectional power flow between the energy sources and the grid. The design and control strategy of the dual-input converter are discussed in detail, with a focus on achieving high conversion efficiency, maximum power point tracking, and optimal power sharing between the solar panels and fuel cells. The control algorithm utilizes a combination of Perturb and Observe (P&O) and Incremental Conductance (IncCond) methods for Maximum Power Point Tracking (MPPT), ensuring efficient power extraction from the solar panels. Additionally, a Proportional-Integral (PI) controller is implemented to regulate power flow between the energy sources and the grid. Simulation and experimental results are presented to validate the proposed converter's performance under various operating conditions.
The expectation of a huge number of Electric Vehicles (EVs) into the market creates multiple technical problems. The power system is at risk due to the unsecured and unreliable operation resulting from unplanned charging and discharging. The unplanned EV charging and discharging change the load profile of the electric grid. Hence, it is required to develop an effective scheduling scheme for charging and discharging EVs. This research focuses on the minimization of the charging cost of a large number of Electric Vehicles (EVs) in a residential area and parking station. When a huge number of EVs are aggregated in the residential and parking stations, charging and discharging power should be a significant and viable contributor to the power grid. V2G (Vehicle-to-Grid) technology offers additional potential benefits to improve power quality and also system reliability. EVs can also supply G2V (Grid-to-Vehicle) power through the discharge of the battery, which benefits both EV owners and electric utilities. It is very difficult to schedule the configurations of EV charging and discharging in an optimized way so as to decrease the charging cost of EVs. The usage of renewable energy sources like solar Photovoltaic (PV) power, reduces the burden on the grid. The solar PV systems are likely to have significant surplus power during the daytime. In this research, optimal charging and discharging scheduling schemes for EVs are developed and the proposed work is carried out under the MATLAB environment with and without Photovoltaic (PV) systems. The realistic load data is collected from Transmission Corporation of Andhra Pradesh (APTRANSCO), India and the solar data is taken from the Indian Solar Resource Data website. The effectiveness of the scheduling scheme is rated according to its capability to reduce the total cost. Extensive simulation studies have been performed on the distribution system under consideration and the results have been presented. The results demonstrate that the charging cost of EVs can be reduced significantly with the use of solar PV power.
Voltage sags have emerged as major problems in power quality in the recent years and the categorization of methods and tools available for reducing voltage sag is becoming increasingly crucial. Modern approaches and tools for minimizing voltage sag are based on an extensive research, aimed at developing effective categorization techniques to assist end users in selecting the most suitable tools and settings. To begin, this research provides a comprehensive assessment of the existing voltage sag control methods and equipment. Furthermore, it presents a classification scheme that divides voltage sag control strategies into three groups: those implemented by the power provider, the customer, and the equipment manufacturer. A classification system for voltage sag control equipment based on the number and type of power supply, taking into account the variations between single and dual power supply voltage sag control devices is also proposed. Additionally, to assist consumers in selecting the optimal device, this research demonstrates how primary voltage sag control equipment utilizes the preventive measures and an integrated software.
The transportation sector is a significant contributor to greenhouse gas emissions, which contribute to climate change. In recent years, Electric Vehicles (EVs) have emerged as a promising solution to reduce the carbon footprint of transportation. However, one of the biggest obstacles to its widespread adoption is the lack of adequate charging infrastructure. Solar-powered EV charging stations present a promising solution to this problem, as it can provide a clean and renewable energy for charging EVs while reducing the strain on the grid during peak hours. This paper presents a detailed analysis of the technical requirements for designing and developing solar-powered EV charging stations. This includes considerations such as the location, size, and capacity of the stations, as well as the types of Evs which are designed to charge. The research also examines the economic feasibility of these stations, including the initial capital costs, operating costs, and potential revenue streams.
