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Dionysios C. Aliprantis
Researcher at Purdue University
Publications - 104
Citations - 2822
Dionysios C. Aliprantis is an academic researcher from Purdue University. The author has contributed to research in topics: Electric power system & Stator. The author has an hindex of 25, co-authored 94 publications receiving 2511 citations. Previous affiliations of Dionysios C. Aliprantis include Iowa State University.
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Distributed Volt/VAr Control by PV Inverters
TL;DR: In this paper, the authors describe the implementation of a voltage control loop within PV inverters that maintains the voltage within acceptable bounds by absorbing or supplying reactive power, which can be considered to be a form of distributed Volt/VAr control.
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Load Scheduling and Dispatch for Aggregators of Plug-In Electric Vehicles
TL;DR: A minimum-cost load scheduling algorithm is designed, which determines the purchase of energy in the day-ahead market based on the forecast electricity price and PEV power demands, and a dynamic dispatch algorithm is developed, used for distributing the purchased energy to PEVs on the operating day.
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Electric Energy and Power Consumption by Light-Duty Plug-In Electric Vehicles
TL;DR: In this article, the authors used the travel patterns of light-duty vehicles in the U.S. obtained from the 2009 National Household Travel Survey to estimate the electric energy and power consumption of plug-in electric vehicles.
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Modeling of LCC-HVDC Systems Using Dynamic Phasors
Mehdi Daryabak,Shaahin Filizadeh,Juri Jatskevich,Ali Davoudi,Maryam Saeedifard,Vijay K. Sood,J. A. Martinez,Dionysios C. Aliprantis,Jose M. Cano,Ali Mehrizi-Sani +9 more
TL;DR: In this article, an average-value model of a line-commutated converter-based HVDC system using dynamic phasors is presented, which represents the lowfrequency dynamics of the converter and its ac and dc systems, and has lower computational requirements than a conventional electromagnetic-transient (EMT) switching model.
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An induction machine model for predicting inverter-machine interaction
TL;DR: In this article, an induction machine model specifically designed for use with inverter models to study machine-inverter interaction is presented, which is considerably more accurate than the traditional qd model, particularly in its ability to predict switching frequency phenomena.