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Ashwin M. Khambadkone
Researcher at National University of Singapore
Publications - 129
Citations - 6834
Ashwin M. Khambadkone is an academic researcher from National University of Singapore. The author has contributed to research in topics: Vector control & Inverter. The author has an hindex of 38, co-authored 127 publications receiving 6108 citations. Previous affiliations of Ashwin M. Khambadkone include Agency for Science, Technology and Research & University of Queensland.
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Proceedings ArticleDOI
Unit commitment risk evaluation of power systems with PV and energy storage
TL;DR: In this paper, an area risk based method that can take into account the PV intermittence and ESS operations when evaluating the unit commitment risk (UCR) was proposed to quantify the effects of different ESS capacities on the system's load carrying capability.
Proceedings ArticleDOI
Input voltage switched dc-dc converter with improved transient performance
R.P. Singh,Ashwin M. Khambadkone +1 more
TL;DR: In this article, a scheme for increasing the slew rate without reducing the inductance value was proposed, and the scheme operates only when there is a demand in the surge rate and restores to the normal operating conditions during steady state operation.
Proceedings ArticleDOI
Evaluation of Workplace Car-park for Electric Vehicle charging in a dense Urban environment
TL;DR: In this article, the authors present a model of an aggregated charging facility that is located as a work-place car park, where the arrival and departure of PEVs are modeled using the office statistics.
Journal ArticleDOI
Economic analysis of annual load loss due to voltage sags in industrial distribution networks with distributed PVs
TL;DR: In this paper , the authors proposed a method for load disconnection indices that relies on dynamic simulation to calculate load disconnections, which allows the integration of the voltage tolerance curve of loads and controls associated with PVs making the scenarios more realistic.
Proceedings ArticleDOI
Application of Matrix pencil method in sub-cycle voltage dip classification
TL;DR: The results show that MPM is able to estimate the fundamental frequency space vector even in highly distorted signals, and differentiated between two highly similar two-phase voltage dips using only a quarter-cycle of data.