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Detection And Identification Of Vehicles Based On Their Unintended Electromagnetic Emissions, Xiaopeng Dong, Haixiao Weng, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch, Michael Noll, Huseyin Goksu, Benjamin Moss
Detection And Identification Of Vehicles Based On Their Unintended Electromagnetic Emissions, Xiaopeng Dong, Haixiao Weng, Daryl G. Beetner, Todd H. Hubing, Donald C. Wunsch, Michael Noll, Huseyin Goksu, Benjamin Moss
Electrical and Computer Engineering Faculty Research & Creative Works
When running, vehicles with internal combustion engines radiate electromagnetic emissions that are characteristic of the vehicle. Emissions depend on the electronics, harness wiring, body type, and many other features. Since emissions are unique to each vehicle, these may be used for identification purposes. This paper investigates a procedure for detecting and identifying vehicles based on their RF emissions. Parameters like the average magnitude or standard deviation of magnitude within a frequency band were extracted from measured emission data. These parameters were used as inputs to an artificial neural network (ANN) that was trained to identify the vehicle that produced the …
Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Adaptive Critic Neural Network Force Controller For Atomic Force Microscope-Based Nanomanipulation, Qinmin Yang, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
Automating the task of nanomanipulation is extremely important since it is tedious for humans. This paper proposes an atomic force microscope (AFM) based force controller to push nano particles on the substrates. A block phase correlation-based algorithm is embedded into the controller for the compensation of the thermal drift which is considered as the main external uncertainty during nanomanipulation. Then, the interactive forces and dynamics between the tip and the particle, particle and the substrate are modeled and analyzed. Further, an adaptive critic NN controller based on adaptive dynamic programming algorithm is designed and the task of pushing nano particles …
Nonlinear Modified Pi Control Of Multi-Module Gcscs In A Large Power System, Swakshar Ray, Ganesh K. Venayagamoorthy
Nonlinear Modified Pi Control Of Multi-Module Gcscs In A Large Power System, Swakshar Ray, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a new control strategy for gate-controlled series compensators (GCSCs). GCSCs are new FACTS devices which can provide active power flow control on a transmission line. Proper placement of GCSCs in proximity to generators can also provide damping to system oscillations. This paper has investigated the effectiveness of multiple multi-module gate controlled series compensators (MMGCSCs) for large power systems. MMGCSCs can be less expensive devices with wide range of control of capacitive reactance in series with transmission lines. A nonlinear modified PI (NMPI) control is developed to provide power flow control and enhanced transient stability …
Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani
Neural Network Based Decentralized Excitation Control Of Large Scale Power Systems, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch, David A. Cartes, Jagannathan Sarangapani
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a neural network (NN) based decentralized excitation controller design for large scale power systems. The proposed controller design considers not only the dynamics of generators but also the algebraic constraints of the power flow equations. The control signals are calculated using only local signals. The transient stability and the coordination of the subsystem controllers can be guaranteed. NNs are used to approximate the unknown/imprecise dynamics of the local power system and the interconnections. All signals in the closed loop system are guaranteed to be uniformly ultimately bounded (UUB). Simulation results with a 3-machine power system demonstrate the …
Identification Of Svc Dynamics Using Wide Area Signals In A Power System, Ganesh K. Venayagamoorthy, Sandhya R. Jetti
Identification Of Svc Dynamics Using Wide Area Signals In A Power System, Ganesh K. Venayagamoorthy, Sandhya R. Jetti
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a wide area monitor (WAM) using remote area signals, such as speed deviations of generators in a power network, for identifying online the dynamics of a static var compensator (SVC). The design of the WAM is studied on the 12 bus FACTS benchmark system recently introduced. A predict-correct method is used to enhance the performance of the WAM during online operation. Simulation results are presented to show that WAM can correctly identify the dynamics of SVC in a power system for small and large disturbances. Such WAMs can be applied in the design of …
Intelligent Tool For Determining The True Harmonic Current Contribution Of A Customer In A Power Distribution Network, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Marty L. Page, Ronald G. Harley
Intelligent Tool For Determining The True Harmonic Current Contribution Of A Customer In A Power Distribution Network, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Marty L. Page, 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 into the power network. Harmonics in a power system are classified as either load harmonics or as supply harmonics depending on their origin. The source impedance also impacts the harmonic current flowing in the network. Hence any change in the source impedance is reflected in the harmonic spectrum of the current. This paper proposes a novel method based on Artificial Neural Networks to isolate and evaluate the impact of the source impedance change without disrupting the operation of any load, …
Intelligent Optimal Control Of Excitation And Turbine Systems In Power Networks, Ganesh K. Venayagamoorthy, Ronald G. Harley
Intelligent Optimal Control Of Excitation And Turbine Systems In Power Networks, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
The increasing complexity of the modern power grid highlights the need for advanced modeling and control techniques for effective control of excitation and turbine systems. The crucial factors affecting the modern power systems today is voltage control and system stabilization during small and large disturbances. Simulation studies and real-time laboratory experimental studies carried out are described and the results show the successful control of the power system excitation and turbine systems with adaptive and optimal neurocontrol approaches. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for small and large disturbances.
Comparison Of Two Optimal Control Strategies For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Comparison Of Two Optimal Control Strategies For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents two optimal control strategies for a grid independent photovoltaic system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). The first strategy is based on Action Dependent Heuristic Dynamic Programming (ADHDP), a model-free adaptive critic design (ACD) technique which optimizes the control performance based on a utility function. ADHDP critic network is used in a PV system simulation study to train an action neural network (optimal neurocontroller) to provide optimal control for varying PV system output energy and loadings. The second optimal control strategy is based on a fuzzy logic controller …
Hdp Based Optimal Control Of A Grid Independent Pv System, Richard L. Welch, Ganesh K. Venayagamoorthy
Hdp Based Optimal Control Of A Grid Independent Pv System, Richard L. Welch, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents an adaptive optimal control scheme for a grid independent photovoltaic (PV) system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). The optimal control algorithm is based on the model-free heuristic dynamic programming (HDP), an adaptive critic design (ACD) technique which optimizes the control performance based on a utility function. The HDP critic network is used in a PV system simulation study to train a neurocontroller to provide optimal control for varying PV system output energy and load demands. The emphasis of the optimal controller is primarily to supply the critical …