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Full-Text Articles in Engineering
Change In Voltage Distortion Predictions At The Pcc Due To Changing Nonlinear Load Current Profile Using Plant Startup Data, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley
Change In Voltage Distortion Predictions At The Pcc Due To Changing Nonlinear Load Current Profile Using Plant Startup Data, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Customer loads connected to electricity supply systems may be broadly categorized as either linear or nonlinear. Nonlinear loads inject harmonics in a power distribution network. The interaction of the nonlinear load harmonics with the network impedances creates voltage distortions at the point of common coupling (PCC) which in turn affects other loads connected to the same PCC. When several nonlinear loads are connected to the PCC, it is difficult to predict mathematically how each nonlinear load is affecting the voltage distortion level at the PCC. Typically, customers with nonlinear loads apply harmonic filtering techniques to clean up their current and …
Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes
Two Neural Network Based Decentralized Controller Designs For Large Scale Power Systems, Wenxin Liu, Jagannathan Sarangapani, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Mariesa Crow, David A. Cartes
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents two 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 signals are calculated using local signals only, the transient and overall system stabilities can be guaranteed. NNs are used to approximate the unknown and/or imprecise dynamics of the local power system and the interconnection terms, thus the requirements for exact system parameters are released. Simulation studies with a three machine power system demonstrate the effectiveness of the proposed controller designs.
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Comparison Of Nonuniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Ganesh K. Venayagamoorthy, Wenwei Zha
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding nonuniform optimal scalar quantizer for speech coding. The quantizer is designed using two neural networks to perform the nonlinear transformation. These neural networks are used in the front and back ends of a uniform quantizer. Two approaches are presented in this paper namely adaptive critic designs and particle swarm optimization, aiming to maximize the signal-to-noise ratio. The comparison of these optimal quantizer designs over a bit-rate range of 3-6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union's Perceptual Evaluation of Speech Quality standard
Demodulation Of Fiber-Optic Sensors For Frequency Response Measurement, Abdeq M. Abdi, Steve Eugene Watkins
Demodulation Of Fiber-Optic Sensors For Frequency Response Measurement, Abdeq M. Abdi, Steve Eugene Watkins
Electrical and Computer Engineering Faculty Research & Creative Works
The neural-network-based processing of extrinsic Fabry-Perot interferometric (EFPI) strain sensors was investigated for the special case of sinusoidal strain. The application area is modal or cyclic testing of structures in which the frequency response to periodic actuation must be demodulated. The nonlinear modulation characteristic of EFPI sensors produces well-defined harmonics of the actuation frequency. Relationships between peak strain and harmonic content were analyzed theoretically. A two-stage demodulator was implemented with a Fourier series neural network to separate the harmonic components of an EFPI signal and a backpropagation neural network to predict the peak-to-peak strain from the harmonics. The system performance …