December 2018 Issue Vol.8 No.12
DESIGN AND IMPLEMENTATION OF
INDUCTIVE POWER TRANSFER FOR EV BATTERY CHARGING https://ia601507.us.archive.org/23/items/IJITCEvol8no1201/vol8no1201.pdf
V.AISHWARYA*, C.KAVITHA*, R. KAVIYA*, DR.R.SEYEZHAI** ,S.HARIKA***
UG Students*, Department of Electrical and Electronics Engineering, SSN College of Engineering, Chennai, India
Associate Professor**, Research Scholar***, Renewable Energy Conversion Lab, Department of Electrical and Electronics Engineering, SSN College of Engineering, Chennai, India
Abstract:
Abstract -- Leading to an increased consideration of clean and renewable alternatives to traditional technologies, the automotive industry has shown a growing interest in electric and hybrid electric vehicle (EV). However, the transition to all-electric transportation is now limited by the high cost of the vehicles, the limited range and the long charging time. Inductive Power Transfer (IPT) systems can be the solution to the range restrictions of EVs by charging the vehicle while driving and reducing required battery size as well as overall cost of the vehicle. These systems transfer electric energy from source to a load without any wired connection and it is achieved through the affordable inductive coupling between two coils termed as transmitter and receiver coil. This paper proposes a bridgeless Interleaved Boost Converter (IBC) as the front end converter for the IPT system. The compensation network and the Inductive coil is designed and simulation studies are carried out in MATLAB/SIMULINK. The functional parameters of bridgeless topology is compared with the conventional bridged configuration. The hardware of the Bridgeless IBC, Inverter and Compensation network is implemented and the results are verified.
Keywords:EV, IPT, IBC
Grey Wolf Optimization Algorithm for
Generation Rescheduling In Deregulated Power System for Congestion Management
https://ia601507.us.archive.org/1/items/vol8no1202/vol8no1202.pdf
Pawan C. Tapre1
Department of Electrical Engineering, CVRU, Ph.D. Scholar Bilaspur (C.G.), India
Dr. Dharmendra kumar Singh2
Department of Electronics Engineering Associate Professor, CVRU, Bilaspur (C.G.),India
Dr. Sudhir Paraskar3
Department of Electrical Engineering, Professor, SSGMCE, Shegaon(M.S.), India
Abstract:
Abstract—
The practitioners and researchers has received considerable attention solving complex optimization problems with metaheuristic algorithms during the past decade. Many of these algorithms are inspired by various phenomena of nature. One of the promising solutions for secure and continuous power flow in the transmission line is rescheduling based congestion management approach but the base problem is rescheduling cost.. To solve the congestion with minimized rescheduling cost , a new population based algorithm, the Grey Wolf Optimization (GWO ) Algorithm, is introduced in this paper . The basic motivation for development of this optimization algorithm is based on special lifestyle of grey wolf and their cooperation characteristics. Based on some benchmark Grey Wolf Optimization(GWO) Algorithm is compared
with the existing conventional algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Firefly (FF) by analyzing the convergence, cost, and congestion. In IEEE-14 and IEEE-30 bus system experimental investigation is carried out and the obtained results by the proposed
algorithm GWO (Grey Wolf Optimization ) Algorithm in comparison to the other algorithms used in this paper.
Keywords:Rescheduling; Congestion Management; Optimization Algorithm; GWO, flexible AC transmission systems.
Read complete December 2018
Read complete December 2018
Pawan C. Tapre1
Department of Electrical Engineering, CVRU, Ph.D. Scholar Bilaspur (C.G.), India
Dr. Dharmendra kumar Singh2
Department of Electronics Engineering Associate Professor, CVRU, Bilaspur (C.G.),India
Dr. Sudhir Paraskar3
Department of Electrical Engineering, Professor, SSGMCE, Shegaon(M.S.), India
Abstract: Abstract— The practitioners and researchers has received considerable attention solving complex optimization problems with metaheuristic algorithms during the past decade. Many of these algorithms are inspired by various phenomena of nature. One of the promising solutions for secure and continuous power flow in the transmission line is rescheduling based congestion management approach but the base problem is rescheduling cost.. To solve the congestion with minimized rescheduling cost , a new population based algorithm, the Grey Wolf Optimization (GWO ) Algorithm, is introduced in this paper . The basic motivation for development of this optimization algorithm is based on special lifestyle of grey wolf and their cooperation characteristics. Based on some benchmark Grey Wolf Optimization(GWO) Algorithm is compared with the existing conventional algorithms such as Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Artificial Bee Colony (ABC), and Firefly (FF) by analyzing the convergence, cost, and congestion. In IEEE-14 and IEEE-30 bus system experimental investigation is carried out and the obtained results by the proposed algorithm GWO (Grey Wolf Optimization ) Algorithm in comparison to the other algorithms used in this paper.
Keywords:Rescheduling; Congestion Management; Optimization Algorithm; GWO, flexible AC transmission systems.