Ganesh K. Venayagamoorthy
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Previous Organizations / Institutes
The organizations / institutes from the scholar's other articles in the past ten years
Ganesh K. Venayagamoorthy has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- David A. Cartes
Florida State Univ, CAPS, Tallahassee, FL 32310 USA
2 shared publications - Donald. C. Wunsch
Department of Electrical and Computer Engineering, University of Missouri at Rolla, MO 65401, USA
2 shared publications - Li Liu
Center for Advanced Power Systems (CAPS), Florida State University, Tallahassee, FL 32310, USA
2 shared publications - Mariesa Louis Crow
Electrical and Computer Engineering Department, Missouri University of Science and Technology, Rolla, MO, USA
2 shared publications - Sarangapani Jagannathan
Missouri University of Science and Technology
2 shared publications - Wenxin Liu
Member, IEEE, Center for Advanced Power Systems (CAPS), Florida State University, Tallahassee, 32310, USA
2 shared publications
Latest Publications
- 1 October 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2007 IEEE International Conference on Control Applications
- No. 10851992,p. 922-927
Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer
Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design.
- 1 October 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE) in 2007 IEEE International Conference on Control Applications
- No. 10851992,p. 922-927
Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer
Power system stabilizers are widely used to damp out the low frequency oscillations in power systems. In power system control literature, there is a lack of stability analysis for proposed controller designs. This paper proposes a Neural Network (NN) based stabilizing controller design based on a sixth order single machine infinite bus power system model. The NN is used to compensate the complex nonlinear dynamics of power system. To speed up the learning process, an adaptive signal is introduced to the NN's weights updating rule. The NN can be directly used online without offline training process. Magnitude constraint of the activators is modeled as saturation nonlinearities and is included in the stability analysis. The proposed controller design is compared with Conventional Power System Stabilizers whose parameters are optimized by Particle Swarm Optimization. Simulation results demonstrate the effectiveness of the proposed controller design.
- 1 January 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21589860,p. 676-681
Neural Network Based Decentralized Controls of Large Scale Power Systems
This paper presents a suite of neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.
- 1 January 2007
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 21589860,p. 676-681
Neural Network Based Decentralized Controls of Large Scale Power Systems
This paper presents a suite of neural network (NN) based decentralized controller designs for large scale power systems' generators, one is for the excitation control and the other is for the steam valve control. Though the control inputs are calculated using local signals, the transient and overall system stability can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system dynamics and the inter-connection terms, thus the requirements for exact system parameters are relaxed. Simulation studies with a three-machine power system demonstrate the effectiveness of the proposed controller designs.