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Full-Text Articles in Engineering
Building Marginal Pattern Library With Unbiased Training Dataset For Enhancing Model-Free Load-Ed Mapping, Qiwei Zhang, Fangxing Li, Wei Feng, Xiaofei Wang, Linquan Bai, Rui Bo
Building Marginal Pattern Library With Unbiased Training Dataset For Enhancing Model-Free Load-Ed Mapping, Qiwei Zhang, Fangxing Li, Wei Feng, Xiaofei Wang, Linquan Bai, Rui Bo
Electrical and Computer Engineering Faculty Research & Creative Works
Input-output mapping for a given power system problem, such as loads versus economic dispatch (ED) results, has been demonstrated to be learnable through artificial intelligence (AI) techniques, including neural networks. However, the process of identifying and constructing a comprehensive dataset for the training of such input-output mapping remains a challenge to be solved. Conventionally, load samples are generated by a pre-defined distribution, and then ED is solved based on those load samples to form training datasets, but this paper demonstrates that such dataset generation is biased regarding load-ED mapping. The marginal unit and line congestion (i.e., marginal pattern) exhibit a …
Solar Concentrators Manufacture And Automation, Ernst Kussul, Tetyana Baydyk, Alberto Escalante Estrada, Maria Tersa Rodriguez Gonzalez, Donald C. Wunsch
Solar Concentrators Manufacture And Automation, Ernst Kussul, Tetyana Baydyk, Alberto Escalante Estrada, Maria Tersa Rodriguez Gonzalez, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Solar energy is one of the most promising types of renewable energy. Flat facet solar concentrators were proposed to decrease the cost of materials needed for production. They used small flat mirrors for approximation of parabolic dish surface. The first prototype of flat facet solar concentrators was made in Australia in 1982. Later various prototypes of flat facet solar concentrators were proposed. It was shown that the cost of materials for these prototypes is much lower than the material cost of conventional parabolic dish solar concentrators. To obtain the overall low cost of flat facet concentrators it is necessary to …
Modeling And Simulation Of Microgrid, Ahmad Alzahrani, Mehdi Ferdowsi, Pourya Shamsi, Cihan H. Dagli
Modeling And Simulation Of Microgrid, Ahmad Alzahrani, Mehdi Ferdowsi, Pourya Shamsi, Cihan H. Dagli
Electrical and Computer Engineering Faculty Research & Creative Works
Complex computer systems and electric power grids share many properties of how they behave and how they are structured. A microgrid is a smaller electric grid that contains several homes, energy storage units, and distributed generators. The main idea behind microgrids is the ability to work even if the main grid is not supplying power. That is, the energy storage unit and distributed generation will supply power in that case, and if there is excess in power production from renewable energy sources, it will go to the energy storage unit. Therefore, the electric grid becomes decentralized in terms of control …
Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi
Solar Irradiance Forecasting Using Deep Neural Networks, Ahmad Alzahrani, Pourya Shamsi, Cihan H. Dagli, Mehdi Ferdowsi
Electrical and Computer Engineering Faculty Research & Creative Works
Predicting solar irradiance has been an important topic in renewable energy generation. Prediction improves the planning and operation of photovoltaic systems and yields many economic advantages for electric utilities. The irradiance can be predicted using statistical methods such as artificial neural networks (ANN), support vector machines (SVM), or autoregressive moving average (ARMA). However, they either lack accuracy because they cannot capture long-term dependency or cannot be used with big data because of the scalability. This paper presents a method to predict the solar irradiance using deep neural networks. Deep recurrent neural networks (DRNNs) add complexity to the model without specifying …
Novel Dynamic Representation And Control Of Power Systems With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Novel Dynamic Representation And Control Of Power Systems With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
FACTS devices have been shown to be useful in damping power system oscillations. However, in large power systems, the FACTS control design is complex due to the combination of differential and algebraic equations required to model the power system. In this paper, a new method to generate a nonlinear dynamic representation of the power network is introduced to enable more sophisticated control design. Once the new representation is obtained, a back stepping methodology for the UPFC is utilized to mitigate the generator oscillations. Finally, the neural network approximation property is utilized to relax the need for knowledge of the power …
Comparison Of Feedforward And Feedback Neural Network Architectures For Short Term Wind Speed Prediction, Ganesh K. Venayagamoorthy, Richard L. Welch, Stephen M. Ruffing
Comparison Of Feedforward And Feedback Neural Network Architectures For Short Term Wind Speed Prediction, Ganesh K. Venayagamoorthy, Richard L. Welch, Stephen M. Ruffing
Electrical and Computer Engineering Faculty Research & Creative Works
This paper compares three types of neural networks trained using particle swarm optimization (PSO) for use in the short term prediction of wind speed. The three types of neural networks compared are the multi-layer perceptron (MLP) neural network, Elman recurrent neural network, and simultaneous recurrent neural network (SRN). Each network is trained and tested using meteorological data of one week measured at the National Renewable Energy Laboratory National Wind Technology Center near Boulder, CO. Results show that while the recurrent neural networks outperform the MLP in the best and average case with a lower overall mean squared error, the MLP …
Novel Dynamic Representation And Control Of Power Networks Embedded With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Novel Dynamic Representation And Control Of Power Networks Embedded With Facts Devices, Shahab Mehraeen, Jagannathan Sarangapani, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
FACTS devices have been shown to be powerful in damping power system oscillations caused by faults; however, in the multi machine control using FACTS, the control problem involves solving differential-algebraic equations of a power network which renders the available control schemes ineffective due to heuristic design and lack of know how to incorporate FACTS into the network. A method to generate nonlinear dynamic representation of a power system consisting of differential equations alone with universal power flow controller (UPFC) is introduced since differential equations are typically preferred for controller development. Subsequently, backstepping methodology is utilized to reduce the generator oscillations …
Intelligent Tool For Determining The True Harmonic Current Contribution Of A Customer In A Power Distribution Network, Joy Mazumdar, Ronald G. Harley, Frank C. Lambert, Ganesh K. Venayagamoorthy, Marty L. Page
Intelligent Tool For Determining The True Harmonic Current Contribution Of A Customer In A Power Distribution Network, Joy Mazumdar, Ronald G. Harley, Frank C. Lambert, Ganesh K. Venayagamoorthy, Marty L. Page
Electrical and Computer Engineering Faculty Research & Creative Works
Customer loads connected to power distribution network 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, …
Live Wire Segmentation Tool For Osteophyte Detection In Lumbar Spine X-Ray Images, Santosh Seetharaman, R. Joe Stanley, Soumya De, Sameer Antani, L. Rodney Long, George R. Thoma
Live Wire Segmentation Tool For Osteophyte Detection In Lumbar Spine X-Ray Images, Santosh Seetharaman, R. Joe Stanley, Soumya De, Sameer Antani, L. Rodney Long, George R. Thoma
Electrical and Computer Engineering Faculty Research & Creative Works
Computer-assisted vertebra segmentation in x-ray images is a challenging problem. Inter-subject variability and the generally poor contrast of digitized radiograph images contribute to the segmentation difficulty. In this paper, a semi-automated live wire approach is investigated for vertebrae segmentation. The live wire approach integrates initially selected user points with dynamic programming to generate a closed vertebra boundary. In order to assess the degree to which vertebra features are conserved using the live wire technique, convex hull-based features to characterize anterior osteophytes in lumbar vertebrae are determined for live wire and manually segmented vertebrae. Anterior osteophyte discrimination was performed over 405 …
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 …
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 …
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
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.
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 …
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 …
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 …
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 …
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 …
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 …
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
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 …
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Decentralized Neural Network Control Of A Class Of Large-Scale Systems With Unknown Interconnection, Wenxin Liu, Jagannathan Sarangapani, Donald C. Wunsch, Mariesa Crow
Electrical and Computer Engineering Faculty Research & Creative Works
A novel decentralized neural network (DNN) controller is proposed for a class of large-scale nonlinear systems with unknown interconnections. The objective is to design a DNN for a class of large-scale systems which do not satisfy the matching condition requirement. The NNs are used to approximate the unknown subsystem dynamics and the interconnections. The DNN is designed using the back stepping methodology with only local signals for feedback. All of the signals in the closed loop (system states and weights estimation errors) are guaranteed to be uniformly ultimately bounded and eventually converge to a compact set.
Online Identification Of Turbogenerator's Dynamics Using A Neuro-Identifier, Wenxin Liu, Ganesh K. Venayagamoorthy, Donald C. Wunsch
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 …
Neural Networks Skin Tumor Diagnostic System, Zhao Zhang, William V. Stoecker, Randy Hays Moss
Neural Networks Skin Tumor Diagnostic System, Zhao Zhang, William V. Stoecker, Randy Hays Moss
Electrical and Computer Engineering Faculty Research & Creative Works
In this study, a malignant melanoma diagnostic system is designed using a straightforward neural network with the back-propagation learning algorithm. Eleven features are automatically extracted from skin tumor images. The correct diagnostic rate of this system is better than the average rate of 16 dermatologists who based their diagnosis with only the slide images.
Fdtd Data Extrapolation Using Multilayer Perceptron (Mlp), H. Goksu, David Pommerenke, Donald C. Wunsch
Fdtd Data Extrapolation Using Multilayer Perceptron (Mlp), H. Goksu, David Pommerenke, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
This work compares MLP with the matrix pencil method, a linear eigenanalysis-based extrapolator, in terms of their effectiveness in finite difference time domain (FDTD) data extrapolation. Matrix pencil method considers the signal as superposed complex exponentials while MLP considers each time step to be a nonlinear function of previous time steps.
Intelligent Control Of Turbogenerator Exciter/Turbine On The Electric Power Grid To Improve Power Generation And Stability, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Intelligent Control Of Turbogenerator Exciter/Turbine On The Electric Power Grid To Improve Power Generation And Stability, Ganesh K. Venayagamoorthy, Ronald G. Harley, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
A review of the applications of intelligent control to replace/augment the conventional excitation and/or turbine control of turbogenerators on the electric power grid is presented in the paper. The intelligent controller designs are based on neural networks and adaptive critic designs (ACDs). The feedback variables are completely based on local measurements from the generators. Simulations and some practical laboratory implementations on a single-machine-infinite-bus and a three-machine power system demonstrate that intelligent controllers are much more effective than the conventional PID control for improving dynamic performance and stability of the power grid under small and large disturbances. The safety margins on …
Using Neural Networks To Estimate Wind Turbine Power Generation, Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann
Using Neural Networks To Estimate Wind Turbine Power Generation, Shuhui Li, Donald C. Wunsch, Edgar O'Hair, Michael G. Giesselmann
Electrical and Computer Engineering Faculty Research & Creative Works
This paper uses data collected at Central and South West Services Fort Davis wind farm to develop a neural network based prediction of power produced by each turbine. The power generated by electric wind turbines changes rapidly because of the continuous fluctuation of wind speed and direction. It is important for the power industry to have the capability to perform this prediction for diagnostic purposes—lower-than-expected wind power may be an early indicator of a need for maintenance. In this paper, characteristics of wind power generation are first evaluated in order to establish the relative importance for the neural network. A …
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Abnormal Cell Detection Using The Choquet Integral, R. Joe Stanley, James M. Keller, Charles William Caldwell, Paul D. Gader
Electrical and Computer Engineering Faculty Research & Creative Works
Automated Giemsa-banded chromosome image research has been largely restricted to classification schemes associated with isolated chromosomes within metaphase spreads. In normal human metaphase spreads, there are 46 chromosomes occurring in homologous pairs for the autosomal classes 1-22 and the X chromosome for females. Many genetic abnormalities are directly linked to structural and/or numerical aberrations of chromosomes within metaphase spreads. Cells with the Philadelphia chromosome contain an abnormal chromosome for class 9 and for class 22, leaving a single normal chromosome for each class. A data-driven homologue matching technique is applied to recognizing normal chromosomes from classes 9 and 22. Homologue …