Engineering Commons^™

Spatial Resolution And Processing Tradeoffs For Hydros: Application Of Reconstruction And Resolution Enhancement Techniques, David G. Long, Michael W. Spencer, Eni G. Njoku

Faculty Publications

Recent developments in reconstruction and resolution enhancement for microwave instruments suggest a possible tradeoff between computation, resolution, and downlink data rate based on postcollection reconstruction/resolution enhancement processing. The Hydrospheric State (HYDROS) mission is designed to measure global soil moisture and freeze/thaw state in support of weather and climate prediction, water, energy, and carbon cycle studies, and natural hazards monitoring. It will use an active and passive L-band microwave system that optimizes measurement accuracy, spatial resolution, and coverage. The active channels use synthetic aperture radar-type processing to achieve fine spatial resolution, requiring a relatively high downlink data rate and ground processor …

Mlp/Rbf Neural-Networks-Based Online Global Model Identification Of Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper compares the performances of a multilayer perceptron neural network (MLPN) and a radial basis function neural network (RBFN) for online identification of the nonlinear dynamics of a synchronous generator in a power system. The computational requirement to process the data during the online training, local convergence, and online global convergence properties are investigated by time-domain simulations. The performances of the identifiers as a global model, which are trained at different stable operating conditions, are compared using the actual signals as well as the deviation signals for the inputs of the identifiers. Such an online-trained identifier with fixed optimal …

Two Separate Continually Online-Trained Neurocontrollers For A Unified Power Flow Controller, Ganesh K. Venayagamoorthy, Radha P. Kalyani

Electrical and Computer Engineering Faculty Research & Creative Works

The crucial factor affecting the modern power systems today is load flow control. The Unified Power Flow Controller (UPFC) provides an effective means for controlling the power flow and improving the transient stability in a power network. The UPFC has fast complex dynamics and its conventional control is based on a linearized model of the power system. This paper presents the design of neurocontrollers to provide better damping during transient and dynamic control. Two separate neurocontrollers are used for controlling the UPFC, one neurocontroller for the shunt inverter and the other for the series inverter. Simulation studies carried out in …

Genetic Algorithms Based Economic Dispatch With Application To Coordination Of Nigerian Thermal Power Plants, Ganesh K. Venayagamoorthy, G. A. Bakare, U. O. Aliyu, Y. K. Shu'aibu

Electrical and Computer Engineering Faculty Research & Creative Works

The main focus of this paper is on the application of genetic algorithm (GA) to search for an optimal solution to a realistically formulated economic dispatch (ED) problem. GA is a global search technique based on principles inspired from the genetic and evolution mechanism observed in natural biological systems. A major drawback of the conventional GA (CGA) approach is that it can be time consuming. The micro-GA (µGA) approach has been proposed as a better time efficient alternative for some engineering problems. The effectiveness of CGA and µGA. to solving ED problem is initially verified on an IEEE 3-generating unit, …

A Novel Method Based On Neural Networks To Distinguish Between Load Harmonics And Source Harmonics In A Power System, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

Wide Area Power System Protection Using A Learning Vector Quantization Network, Ganesh K. Venayagamoorthy, Mahyar Zarghami

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents a wide area monitoring and protection technique based on a learning vector quantization (LVQ) neural network. Phasor measurements of the power network buses are monitored continuously by a LVQ network in order to alert the control room operators of possible faults. The proposed scheme could be used in a wide area monitored network to provide remedial action when primary local protection schemes for transmission lines fail to function. This technique could also be extended to the actuation of the secondary protection schemes, hence, preserving the integrity of the power network especially when the faults are spreading over …

A Heuristic-Dynamic-Programming-Based Power System Stabilizer For A Turbogenerator In A Single-Machine Power System, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Power system stabilizers (PSSs) are used to generate supplementary control signals for the excitation system in order to damp the low-frequency power system oscillations. To overcome the drawbacks of a conventional PSS (CPSS), numerous techniques have been proposed in the literature. Based on the analysis of existing techniques, a novel design based on heuristic dynamic programming (HDP) is presented in this paper. HDP, combining the concepts of dynamic programming and reinforcement learning, is used in the design of a nonlinear optimal power system stabilizer. Results show the effectiveness of this new technique. The performance of the HDP-based PSS is compared …

Adaptive Critic Designs For Optimal Control Of Power Systems, 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, turbine and flexible AC transmission systems (FACTS). The crucial factors affecting the modern power systems today is voltage and load flow control. Simulation studies in the PSCAD/EMTDC environment and realtime laboratory experimental studies carried out are described and the results show the successful control of the power system elements and the entire power system with adaptive and optimal neurocontrol schemes. Performances of the neurocontrollers are compared with the conventional PI controllers for damping under different operating conditions for …

Optimal Dynamic Neurocontrol Of A Gate-Controlled Series Capacitor In A Multi-Machine Power System, Swakshar Ray, Ganesh K. Venayagamoorthy, Edson H. Watanabe, F. D. De Jesus

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the design of an optimal dynamic neurocontroller for a new type of FACTS device - the gate controlled series capacitor (GCSC) incorporated in a multi-machine power system. The optimal neurocontroller is developed based on the heuristic dynamic programming (HDP) approach. In addition, a dynamic identifier/model and controller structure using the recurrent neural network trained with backpropagation through time (BPTT) is employed. Simulation results are presented to show the effectiveness of the dynamic neurocontroller and its performance is compared with that of the conventional PI controller under small and large disturbances.

Power System Optimization And Coordination Of Damping Controls By Series Facts Devices, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

Reactive Power And Voltage Control Of The Nigerian Grid System Using Micro-Genetic Algorithm, Ganesh K. Venayagamoorthy, G. A. Bakare, U. O. Aliyu

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, a micro-genetic based approach to the optimization of reactive power and voltage profiles improvement and real power loss minimization is presented. The reactive power control devices such as generators, tap positions of on-load tap changer of transformers, shunt reactors are used to correct voltage limits violations while simultaneously reducing the system real power losses. Genetic algorithms (GAs) are well-known global search techniques anchored on the mechanisms of natural selection and genetics. Because of the time intensive nature of the conventional GA, the micro-GA is proposed as a more time efficient alternative. The feasibility and effectiveness of the …

Effects Of A Statcom, A Scrc And A Upfc On The Dynamic Behavior Of A 45 Bus Section Of The Brazilian Power System, Ganesh K. Venayagamoorthy, Salman Mohagheghi, Wei Qiao, Swakshar Ray, Ronald G. Harley, Djalma M. Falcao, Glauco N. Taranto, Tatiana M. L. Assis, Yamille Del Valle

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

Optimal Control Parameters For A Upfc In A Multimachine Using Pso, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The crucial factor affecting the modern power systems today is load flow control. The unified power flow controller (UPFC) is an effective means for controlling the power flow and can provide damping capability during transient conditions. The UPFC is controlled conventionally using PI controllers. The optimal design of the PI controllers for a UPFC is a challenging task and time consuming using the conventional techniques. This paper presents an approach using particle swarm optimization (PSO) for the design of optimal conventional controllers for a UPFC in a multimachine power system. Simulation results are presented to show the effectiveness of the …

A Comparison Of Pso And Backpropagation For Training Rbf Neural Networks For Identification Of A Power System With Statcom, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Backpropagation algorithm is the most commonly used algorithm for training artificial neural networks. While being a straightforward procedure, it suffers from extensive computations, relatively slow convergence speed and possible divergence for certain conditions. The efficiency of this method as the training algorithm of a radial basis function neural network (RBFN) is compared with that of particle swarm optimization, for neural network based identification of a small power system with a static compensator. The comparison of the two methods is based on the convergence speed and robustness of each method.

Using A Neural Network To Distinguish Between The Contributions To Harmonic Pollution Of Non-Linear Loads And The Rest Of The Power System, Joy Mazumdar, Frank C. Lambert, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

A Dynamic Recurrent Neural Network For Wide Area Identification Of A Multimachine Power System With A Facts Device, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Multilayer perceptron and radial basis function neural networks have been traditionally used for plant identification in power systems applications of neural networks. While being efficient in tracking the plant dynamics in a relatively small system, their performance degrades as the dimensions of the plant to be identified are increased, for example in supervisory level identification of a multimachine power system for wide area control purposes. Recurrent neural networks can deal with such a problem by modeling the system as a set of differential equations and with less order of complexity. Such a recurrent neural network identifier is designed and implemented …

A Neural Network Based Wide Area Monitor For A Power System, Xiaomeng Li, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

With the deregulation of power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area controllers (WACs) using wide-area or global signals can provide remote auxiliary control signals to local controllers such as automatic voltage regulators, power system stabilizers, etc to damp out inter-area oscillations. The power system is highly nonlinear system with fast changing dynamics. In order to have an efficient WAC, an online system monitor/predictor is required to provide inter-area information to the WAC from time to time. This paper presents the design of an …

An Adaptive Mamdani Fuzzy Logic Based Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

An adaptive Mamdani based fuzzy logic controller has been designed for controlling a static compensator (STATCOM) in a multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. A model free approach using the controller output error is applied for training purposes that adaptively changes the controller output parameters based on a gradient descent method. Moreover, shrinking span membership functions are used for a more stable and accurate control performance. Simulation results show that the proposed controller outperforms the conventional …

A New Method For Melt Detection On Antarctic Ice-Shelves And Scatterometer Calibration Verification, Lukas Brad Kunz

Theses and Dissertations

Ku-band dual-polarization radar backscatter measurements from the SeaWinds on QuikScat scatterometer and microwave radiometer measurements from the Special Sensor Microwave/Imager (SSM/I) are used to determine periods of surface melt and freeze in the Antarctic ice-shelves. The normalized radar backscatter (sigma-0) and backscatter polarization ratio (PR) are used in the maximum likelihood estimation of the ice-state. This method is used to infer the daily ice-surface conditions for 25 selected study points located on the Ronne, Ross, Larsen, Fimbul, Amery, and Shackleton Ice-shelves. The temporal and spatial variations of the radar response are also observed for various neighborhood sizes surrounding each given …

Modified Takagi-Sugeno Fuzzy Logic Based Controllers For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Takagi-Sugeno (TS) based fuzzy logic controllers have been designed for controlling a STATCOM in a multimachine power system. Such controllers do not need any prior knowledge of the plant to be controlled and can efficiently control a STATCOM during different disturbances in the network. Two different approaches for the TS fuzzy logic controller are proposed: a conventional TS fuzzy logic design and a modified TS fuzzy logic design based on shrinking span membership functions. Simulation results, along with a comparison of the conventional TS fuzzy logic controller performance with that of the proposed controller are presented.

Indirect Adaptive Control For Synchronous Generator: Comparison Of Mlp/Rbf Neural Networks Approach With Lyapunov Stability Analysis, Jung-Wook Park, Ronald G. Harley, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

This paper compares two indirect adaptive neurocontrollers, namely a multilayer perceptron neurocontroller (MLPNC) and a radial basis function neurocontroller (RBFNC) to control a synchronous generator. The different damping and transient performances of two neurocontrollers are compared with those of conventional linear controllers, and analyzed based on the Lyapunov direct method.

Neural Network Based Classification Of Road Pavement Structures, V. Venayagamoorthy, D. Allopi, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Roads have formed the basic infrastructure of commerce since flints and other tools and artifacts were first exchanged along the trade routes of prehistory. Roadways are very large, in volume, in extent, and in value. They also wear out, and their useful life is directly proportional to their initial strength and inversely proportional to the number of heavy goods vehicles using them. Therefore, the increasing complexity of road transportation needs advanced techniques for effective design of pavements. This paper proposes an intelligent technique using neural networks to classify different types of road pavement structures, which is essential in estimating bearing …

Primitives And Design Of The Intelligent Pixel Multimedia Communicator, David K. Lucas

Theses: Doctorates and Masters

Communication systems arc an ever more essential component of our modern global society. Mobile communications systems are still in a state of rapid advancement and growth. Technology is constantly evolving at a rapid pace in ever more diverse areas and the emerging mobile multimedia based communication systems offer new challenges for both current and future technologies. To realise the full potential of mobile multimedia communication systems there is a need to explore new options to solve some of the fundamental problems facing the technology. In particular, the complexity of such a system within an infrastructure framework that is inherently limited …

Spatially Varying Threshold Models For The Automated Segmentation Of Radiodense Tissue In Digitized Mammograms, Richard Edson Eckert Iii

Theses and Dissertations

The percentage of radiodense (bright) tissue in a mammogram has been correlated to an increased risk of breast cancer. This thesis presents an automated method to quantify the amount of radiodense tissue found in a digitized mammogram. The algorithm employs a radial basis function neural network in order to segment the breast tissue region from the remainder of the X-ray. A spatially varying Neyman-Pearson threshold is used to calculate the percentage of radiodense tissue and compensate for the effects of tissue compression that occurs during a mammography procedure. Results demonstrating the efficacy of the technique are demonstrated by exercising the …

A Normalization Scheme Of Bidirectional Reflectances And Normalized Vegetation Indices For Short-Term Multi-Angular Spot Satellite Images, Chien-Hui Liu

Journal of Marine Science and Technology–Taiwan

Though the view angle range of SPOT HRV is smaller as compared to those of NOAA AVHRR, EOS MODIS/MISR and ADEOS POLDER, numerous researchers had showed significant bi-directional effect of ground reflectance for decades. Currently, there's no existing normalization method of bidirectional reflectances for multitemporal SPOT images. In this study, a normalization scheme for multi-angular SPOT data is proposed. This scheme will correct both atmospheric and bidirectional effect for short-term multi-temporal SPOT data. Five SPOT images scanned within twelve days are used. The results show that the variation of trees reflectance to viewing zenith angle is very significant. The proposed …

Day 3. Wednesday, August 13, 2003: Oil Shale, University Of Colorado Boulder. Natural Resources Law Center

Energy Field Tour 2003 (August 11-16)

8 pages.

Contains references.

Online Identification Of Turbogenerator's Dynamics Using A Neuro-Identifier, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing complexity of modern power systems highlights the need for effective system identification techniques for the successful control of power system. This paper proposes a robust continually online trained neuroidentifier to predict the outputs of turbogenerator - terminal voltage and speed deviation. The inputs to the neuro-identifier are the changes of the plant's outputs and plant's inputs. It overcomes the drawback of calculating deviation signals from reference signals for different operating points in previous work. Simulation results show that the neuro-identifier can provide accurate identification under different operating conditions. Furthermore, the neuro-identifier can learn the dynamics of the system …

Real-Time Dual Heuristic Programming-Based Neurocontroller For A Turbogenerator In A Multimachine Power System, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

Based on Dual Heuristic Programming (DHP), a real-time implementation of a neurocontroller for excitation and turbine control of a turbogenerator in a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulation and real-time hardware implementation on a three-machine system demonstrate that the DHP neurocontroller is much more effective than conventional PID controllers, the automatic voltage regulator, power system stabilizer and the governor, for improving dynamic performance and stability under small and large disturbances.

Automated Distribution And Management Of Software And Hardware Configurations For Heterogeneous Wireless Devices, Grainne Foley

Theses

The continuing evolution of wireless networks and services has increased the complexity and diversity of wireless networks and devices, placing significant emphasis on the need for flexible device configuration management. Widely varying wireless technologies currently operate independently worldwide, making global wireless network access for the mobile user confusing and unwieldy. New technologies, network access methodologies and improved services further obscure the evolving environment. The focus of effort for this thesis is the development of a system to automate the distribution and management of software and hardware configurations that enable different levels of wireless device reconfiguration. The scope of investigation will …

Dual Heuristic Programming Excitation Neurocontrol For Generators In A Multimachine Power System, Ganesh K. Venayagamoorthy, Donald C. Wunsch, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

The design of nonlinear optimal neurocontrollers that replace the conventional automatic voltage regulators for excitation control of turbogenerators in a multimachine power system is presented in this paper. The neurocontroller design is based on dual heuristic programming (DHP), a powerful adaptive critic technique. The feedback variables are completely based on local measurements from the generators. Simulations on a three-machine power system demonstrate that DHP-based neurocontrol is much more effective than the conventional proportional-integral-derivative control for improving dynamic performance and stability of the power grid under small and large disturbances. This paper also shows how to design optimal multiple neurocontrollers for …