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Full-Text Articles in Engineering
Utilization And Effect Of Plug-In Hybrid Electric Vehicles In The United States Power Grid, Steven D. Jenkins, John R. Rossmaier, Mehdi Ferdowsi
Utilization And Effect Of Plug-In Hybrid Electric Vehicles In The United States Power Grid, Steven D. Jenkins, John R. Rossmaier, Mehdi Ferdowsi
Electrical and Computer Engineering Faculty Research & Creative Works
Plug-in hybrid electric vehicles (PHEVs) are uniquely capable of providing both transportation and battery storage interconnection to the electric power grid. This ability allows PHEVs the possibility of serving the power grid in the capacity of a mobile energy storage unit, providing the grid with additional stability, reliability, costeffectiveness, and efficiency. Additionally, with the higher fuel efficiency of PHEVs, the transportation and power generation sectors can collectively reduce their ecologically harmful emissions and increase their reliance on environmentally friendly energy sources. These concepts are still new and under development; in this paper, the viability of the PHEV as a mobile …
Swarm Intelligence And Evolutionary Approaches For Reactive Power And Voltage Control, Ganesh K. Venayagamoorthy, G. Krost, G. A. Bakare, Lisa L. Grant
Swarm Intelligence And Evolutionary Approaches For Reactive Power And Voltage Control, Ganesh K. Venayagamoorthy, G. Krost, G. A. Bakare, Lisa L. Grant
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a comparison of swarm intelligence and evolutionary techniques based approaches for minimization of system losses and improvement of voltage profiles in a power network. Efficient distribution of reactive power in an electric network can be achieved by adjusting the excitation on generators, the on-load tap changer positions of transformers, and proper switching of discrete portions of inductors or capacitors. This is a mixed integer non-linear optimization problem where metaheuristics techniques have proven suitable for providing optimal solutions. Four algorithms explored in this paper include differential evolution (DE), particle swarm optimization (PSO), a hybrid combination of DE and …
Nsf Career: Scalable Learning And Adaptation With Intelligent Techniques And Neural Networks For Reconfiguration And Survivability Of Complex Systems, Ganesh K. Venayagamoorthy
Nsf Career: Scalable Learning And Adaptation With Intelligent Techniques And Neural Networks For Reconfiguration And Survivability Of Complex Systems, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
The NSF CAREER program is a premier program that emphasizes the importance the foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper describes the research and education experiences gained by the principal investigator and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the power, control and adaptive networks (PCAN) program of the electrical, communications and cyber systems division, effective June 1, 2004. In addition, suggestions on writing a winning NSF …
Dsp-Based Pso Implementation For Online Optimization Of Power System Stabilizers, Parviz Palangpour, Pinaki Mitra, Swakshar Ray, Ganesh K. Venayagamoorthy
Dsp-Based Pso Implementation For Online Optimization Of Power System Stabilizers, Parviz Palangpour, Pinaki Mitra, Swakshar Ray, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Real-time implementations of controllers require optimization algorithms which can be performed quickly. In this paper, a digital signal processor (DSP) implementation of particle swarm optimization (PSO) is presented. PSO is used to optimize the parameters of two stabilizers used in a power system. The controllers and PSO are both implemented on a single DSP in a hardware-in-loop configuration. Results showing the performance and feasibility for real-time implementations of PSO are presented.