Ganesh K. Venayagamoorthy
Clemson University
449 Papers
4.3K Citations
Ganesh K. Venayagamoorthy is an academic researcher from Clemson University. The author has contributed to research in topics: Electric power system & Particle swarm optimization. The author has an hindex of 56, co-authored 435 publications. Previous affiliations of Ganesh K. Venayagamoorthy include University of KwaZulu-Natal & ML Sultan Technikon.
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Papers
Adaptive critic design based dynamic optimal power flow controller for a smart grid
Jiaqi Liang,Ronald G. Harley,Ganesh K. Venayagamoorthy +2 more
- 11 Apr 2011
TL;DR: An adaptive critic design (ACD) based dynamic optimal power flow control (DOPFC) is proposed in this paper as a solution to the smart grid operation in a high short-term uncertainty and variability environment.
Adaptive Automatic Generation Control for Improved Stability of Power Systems with Utility-Scale Photovoltaic Plants
Rajan Ratnakumar,Ganesh K. Venayagamoorthy +1 more
- 11 Mar 2023
TL;DR: In this article , an adaptive automatic generation control (A-AGC) based on an EMO index derived from phasor measurement units is proposed to ensure the stability of the power system.
Intelligent analysis of wind turbine power curve models
Arman Goudarzi,Innocent E. Davidson,Afshin Ahmadi,Ganesh K. Venayagamoorthy +3 more
- 01 Dec 2014
TL;DR: Comparison analysis of various parametric and non-parametric techniques for modeling of wind turbine power curves, with reference to three commercial wind turbines, is presented.
Damping inter-area oscillations using virtual generator based power system stabilizer
TL;DR: In this article, the effect of time delays encountered as a result of wide area measurements and communications are considered in the studies presented, and the generator choice for VG-PSS location is determined based on the generator that has maximum controllability on dominant weakly damped inter-area modes in a power system.
Recognition of facial expressions using Gabor wavelets and learning vector quantization
TL;DR: The application of Gabor filter based feature extraction in combination with learning vector quantization (LVQ) for recognition of seven different facial expressions from still pictures of the human face proves the feasibility of computer vision based facial expression recognition for practical applications like surveillance and human computer interaction.