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The Existence Of Multiple Equilibria In The Upfc Power Injection Model, Mahyar Zarghami, Mariesa Crow
The Existence Of Multiple Equilibria In The Upfc Power Injection Model, Mahyar Zarghami, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
This letter shows the existence of multiple equilibria that arise from the use of the state model of the unified power flow controller (UPFC). These multiple equilibria can arise from a common power injection model for the same terminal conditions of shunt bus voltage and series active and reactive power injections. The multiple equilibria result in two or more sets of eigenvalues, some of which may indicate an unstable operating condition. Therefore, the use of the UPFC power injection model must be used with caution to ensure stable operation of the UPFC.
Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes
Comparisons Of An Adaptive Neural Network Based Controller And An Optimized Conventional Power System Stabilizer, Wenxin Liu, Ganesh K. Venayagamoorthy, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes
Electrical and Computer Engineering Faculty Research & Creative Works
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 …
Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes
Neural Network Based Decentralized Controls Of Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, Li Liu, David A. Cartes
Electrical and Computer Engineering Faculty Research & Creative Works
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.
Parameter Optimization Of Pss Based On Estimated Hessian Matrix From Trajectory Sensitivities, Jung-Wook Park, Ganesh K. Venayagamoorthy, Seung-Mook Baek
Parameter Optimization Of Pss Based On Estimated Hessian Matrix From Trajectory Sensitivities, Jung-Wook Park, Ganesh K. Venayagamoorthy, Seung-Mook Baek
Electrical and Computer Engineering Faculty Research & Creative Works
This paper describes the optimal tuning for the output limits of the power system stabilizer (PSS), which can improve the system damping performance immediately following a large disturbance. The non-smooth nonlinear parameters such as the saturation limits of the PSS cannot be tuned by the conventional methods based on linear approaches. To implement the systematic optimal tuning for the output limits of the PSS, a feedforward neural network (FFNN) is applied to the hybrid system model based on the differential-algebraic-impulsive-switched (DAIS) structure. The FFNN is firstly designed to identify the trajectory sensitivities obtained from the DAIS structure. Thereafter, it estimates …
A Novel Impedance Measurement Technique For Power Electronic Systems, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
A Novel Impedance Measurement Technique For Power Electronic Systems, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
When designing and building power systems that contain power electronic switching sources and loads, system integrators must consider the frequency-dependent impedance characteristics at an interface to ensure system stability. Stability criteria have been developed in terms of source and load impedance for both dc and ac systems and it is often necessary to measure system impedance through experiments. Traditional injection-based impedance measurement techniques require multiple online tests which lead to many disadvantages. The impedance identification method proposed in this paper greatly reduces online test time by modeling the system with recurrent neural networks. The recurrent networks are trained with measured …