Engineering Commons^™

I2Mtc 2021 Special Issue In Ieee Transactions On Instrumentation And Measurement, Pawel Niewczas, Kristen M. Donnell, Melanie Ooi

Electrical and Computer Engineering Faculty Research & Creative Works

No abstract provided.

Random Subspace Projection For Predicting Biogeographical Ancestry, Tanjin Toma, Tayo Olufemi-Ajayi, Jeremy Dawson, Donald Adjeroh

Electrical and Computer Engineering Faculty Research & Creative Works

Human biogeographical ancestry estimation using genomic information is an important problem with applications in population stratification, admixture mapping, forensic ancestry inference, and in healthcare. Various studies have proposed panels of ancestry informative single nucleotide polymorphisms (SNPs) for distinguishing between widely separated continental populations. There has been limited investigation on identifying SNP panels for sub-continental ancestry prediction, especially given the difficult challenge of identifying SNP markers to distinguish closely associated sub-populations, for instance, within a continent. In this study, we propose an ancestry informative SNP selection algorithm exploiting the concept of random subspace projection using supervised learning. The proposed approach identifies …

Deep Learning Nuclei Detection In Digitized Histology Images By Superpixels, Sudhir Sornapudi, R. Joe Stanley, William V. Stoecker, Haidar Almubarak, Rodney Long, Sameer Antani, George Thoma, Rosemary Zuna, Shelliane R. Frazier

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Advances in image analysis and computational techniques have facilitated automatic detection of critical features in histopathology images. Detection of nuclei is critical for squamous epithelium cervical intraepithelial neoplasia (CIN) classification into normal, CIN1, CIN2, and CIN3 grades.

Methods: In this study, a deep learning (DL)-based nuclei segmentation approach is investigated based on gathering localized information through the generation of superpixels using a simple linear iterative clustering algorithm and training with a convolutional neural network.

Results: The proposed approach was evaluated on a dataset of 133 digitized histology images and achieved an overall nuclei detection (object-based) accuracy of 95.97%, with …

Wide Area Monitoring Of Rotor Angle Based On Synchrophasor Measurements, P. K. Olulope, Ganesh K. Venayagamoorthy, K. A. Folly

Electrical and Computer Engineering Faculty Research & Creative Works

The concept of phasor is designed for steady state and phasor measurement unit (PMU) is based on this concept. Measuring rotor angle therefore during transient has become a challenging task. This paper investigates the possibility of using data from PMU to estimate the internal variables of synchronous generator such as rotor angle of large power system during transient disturbance. the rotor angle is estimated from synchro phasor such as phasor voltage and current as well as their angles. the proposed method is implemented on IEEE 68 bus system. © 2012 IEEE.

Linear Precoding Of Finite Alphabet Signals In Multi-Antenna Broadcast Channels, Yongpeng Wu, Mingxi Wang, Chengshan Xiao, Zhi Ding, Xiqi Gao

Electrical and Computer Engineering Faculty Research & Creative Works

We investigate the design of linear transmit precoding for multiple-input multiple-output (MIMO) broadcast channels (BC) with finite alphabet input signals. We derive an explicit expression for the achievable rate region of the MIMO BC with discrete constellation inputs, which is generally applicable to cases involving arbitrary user number and arbitrary antenna configurations. for the case where all the users employ the same modulation scheme, we further present a weighted sum rate upper-bound of the MIMO BC with identical transmit precoding matrices. the resulting bound demonstrates a serious performance loss due to multi-user interference for MIMO BC with finite alphabet inputs …

Ais-Based Coordinated And Adaptive Control Of Generator Excitation Systems For An Electric Ship, Chuan Yan, Ganesh Kumar Venayagamoorthy, Keith Corzine

Electrical and Computer Engineering Faculty Research & Creative Works

An artificial immune system (AIS)-Based control of generator excitation systems for the U.S. Navy's electric ship is presented in this paper to solve power quality problems caused by high-energy loads such as direct energy weapons. the coordinated development of the AIS controllers mainly consists of two parts innate immunity (optimal) and adaptive immunity. the parameters of the controllers for the former, to provide optimal performance, are determined simultaneously using particle swarm optimization. for dramatic changes in the ship's power system, adaptive control based on the immune system feedback law is developed. the feedback law adapts the controllers' parameters only during …

Resource Scheduling Under Uncertainty In A Smart Grid With Renewables And Plug-In Vehicles, Ahmed Yousuf Saber, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The power system and transportation sector are our planet's main sources of greenhouse gas emissions. Renewable energy sources (RESs), mainly wind and solar, can reduce emissions from the electric energy sector; however, they are very intermittent. Likewise, next generation plug-in vehicles, which include plug-in hybrid electric vehicles and electric vehicles with vehicletogrid capability, referred to as grid able vehicles (GVs) by the authors, can reduce emissions from the transportation sector. GVs can be used as loads, energy sources (small portable power plants) and energy storage units in a smart grid integrated with renewable energy sources. However, uncertainty surrounds the controllability …

On-Line Voltage Stability Load Index Estimation Based On Pmu Measurements, K. Joseph Makasa, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

A new method for estimation of Voltage Stability Load Index (VSLI) for a power system using data from optimally located phasor measurement units (PMU) is presented in this paper. Optimal PMU placement was carried out considering islanding operating conditions. vs.LI is estimated using a kind of recurrent neural network known as the Echo State Network (ESN). the development ESN is computational efficient and provides accurate estimation. Results of optimal PMU placement for normal and islanded operating conditions, and ESN estimation of vs.LI on the IEEE 14 bus system is presented. © 2011 IEEE.

Innovative Smart Grid Control Technologies, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The smart electric power grid will evolve into a very complex adaptive system under semi-autonomous distributed control. It will be spatially and temporally complex, non-convex, non-linear and non-stationary, with lots of variability and uncertainties beyond what the traditional power system experiences today. the distributed integration of intermittent sources of energy and energy storage to a power grid further adds complexity and challenges to modeling, control and optimization of the grid. Innovative control technologies are needed to handle the growing complexity of a smart grid, maximize the penetration of renewable energy, and provide maximum utilization of available energy storage, especially plug-in …

Intelligent Sense-Making For Smart Grid Stability, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The power systems of the future - smart grids - will see an increase in both renewable energy sources and load demand, thus increasing the need for fast dynamic reconfiguration of system parameters to handle energy and load dispatches. Power systems will need to be monitored continuously to maintain stability under normal and abnormal operating conditions and when facing disturbances. a combination of system state prediction, dynamic power flow, system optimization, and solution stability checking will allow for a more reliable, affordable, efficient and clean power grid. the optimization and control systems on a smart grid will require a computational …

Computational Intelligence For Control Of Wind Turbine Generators, Wei Qiao, Jiaqi Liang, Ganesh K. Venayagamoorthy, Ronald Harley

Electrical and Computer Engineering Faculty Research & Creative Works

This paper summarizes past and ongoing research in the area of the application of computational intelligence (CI) for control of wind turbine generators (WTGs). Several intelligent design approaches and control strategies, including optimal design of WTG controllers using particle swarm optimization (PSO) and mean-variance optimization (MVO) algorithms and adaptive critic design-Based coordinated optimal adaptive control for wind plants and shunt FACTS devices, are presented for dynamic performance and fault ride-through enhancement of WTGs and the associated power grid. the effectiveness of these intelligent design approaches and control strategies are demonstrated by nonreal- and real-time simulations in PSCAD/EMTDC and RSCAD/RTDS, respectively. …

Linear Precoding For Mimo Multiple Access Channels With Discrete-Constellation Inputs, Mingxi Wang, Weiliang Zeng, Chengshan Xiao

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, we study linear precoding for multiple-input multiple-output (MIMO) multiple access channels (MAC) with discrete-constellation inputs. We derive the constellation-constrained capacity region for the MIMO MAC with an arbitrary number of users. Due to the non-concavity of the objective function, we obtain the necessary conditions for the weighted sum rate (WSR) maximization problem through Karush-Kuhn-Tucker (KKT) analysis. to find the optimal precoding matrices, we propose an iterative algorithm utilizing alternating optimization strategy and gradient descent update. Numerical results show that when inputs are digital modulated signals and the signal-to-noise ratio (SNR) is in the medium range, our proposed …

Smartpark As A Virtual Statcom, Pinaki Mitra, Ganesh K. Venayagamoorthy, Keith Corzine

Electrical and Computer Engineering Faculty Research & Creative Works

Power electronic-Based FACTS devices such as STATCOMs are sometimes essential for voltage support in transmission networks. They can also be used for continuous operation of doubly fed induction generator-Based wind turbines during faults. However, these devices are quite expensive and therefore cannot be used extensively. This paper explores the potential of a low-cost solution that utilizes the reactive power and voltage support capabilities of plug-in vehicles parked in charging stations (SmartParks) so that they can behave as virtual STATCOMs. for this solution, a 12-bus multimachine power system is considered wherein one of the conventional units is replaced by a 400 …

Reducing Dimensionality Of Hyperspectral Data With Diffusion Maps And Clustering With K-Means And Fuzzy Art, Louis Du Plessis, Rui Xu, Steven Damelin, Michael Sears, Donald C. Wunsch

Electrical and Computer Engineering Faculty Research & Creative Works

It is very difficult to analyse large amounts of hyperspectral data. Here we present a method based on reducing the dimensionality of the data and clustering the result in moving toward classification of the data. Dimensionality reduction is done with diffusion maps, which interpret the eigenfunctions of Markov matrices as a system of coordinates on the original dataset in order to obtain an efficient representation of data geometric descriptions. Clustering is done using k-means and a neural network clustering theory, Fuzzy ART (FA). The process is done on a subset of core data from AngloGold Ashanti, and compared to results …

Voltage Prediction Using A Cellular Network, Lisa L. Grant, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Better identification tools are needed for power system voltage profile prediction. the power systems of the future will see an increase in both renewable energy sources and load demand increasing the need for quick estimation of bus voltages and line power flows for system security and contingency analysis. a Cellular Simultaneous Recurrent Neural Network (CSRN) to identify and predict bus voltage dynamics is presented in this paper. the benefit of using a cellular structure over traditional neural network architectures is that the network can represent a direct mapping of any power system allowing for easier scalability to large power systems. …

Gms Considering Uncertainty In Wind Power In A Wind-Hydrothermal Power System, Y. Yare, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

An optimal preventive generator maintenance scheduling (GMS) in a smart grid environment comprising wind-hydrothermal energy resources is presented in this paper. GMS problem is solved with the aim of maximizing economic benefits subject to satisfying system constraints. This GMS formulation becomes a challenging problem because of the variability and intermittency of wind speed and the incorporation of uncertainty in wind generation. the objective is to perform preventive GMS in such a manner that the annual generation cost is minimized, the annual cost saving is increased while all operating constraints are satisfied in the presence of uncertainty in wind generation. Discrete …

Optimal Tuning Of System Stabilizer Parameters Using Pbil With Adaptive Learning Rate, K. A. Folly, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents an optimal tuning of Power System Stabilizers (PSS) for a multi-machine power system using Population-Based Incremental Learning (PBIL) algorithm with adaptive learning rate. the proposed (APBIL) algorithm can adjust the learning rate automatically according to the degree of evolution of the search. the objective of the design is to achieve adequate stability over a wide range of operating conditions. the proposed controller is compared with traditional PBIL with fixed learning rate (PBIL) and tested under various operating conditions. Simulation results show that the APBIL based PSS provides a more efficient search capability and gives a better damping …

Bio-Inspired Algorithms For Autonomous Deployment And Localization Of Sensor Nodes, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Optimal deployment and accurate localization of sensor nodes have a strong influence on the performance of a wireless sensor network (WSN). This paper considers real-time autonomous deployment of sensor nodes from an unmanned aerial vehicle (UAV). Such a deployment has importance, particularly in ad hoc WSNs, for emergency applications, such as disaster monitoring and battlefield surveillance. the objective is to deploy the nodes only in the terrains of interest, which are identified by segmentation of the images captured by a camera on board the UAV. Bioinspired algorithms, particle swarm optimization (PSO) and bacterial foraging algorithm (BFA), are presented in this …

Opportunistic Cooperation For Multi-Antenna Multi-Relay Networks, Weiliang Zeng, Chengshan Xiao, Youzheng Wang, Jianhua Lu

Electrical and Computer Engineering Faculty Research & Creative Works

A low-complexity, near-optimal transmit antenna selection algorithm is proposed for multi-relay networks where all nodes are equipped with multiple antennas. We first establish a system model and a unified capacity maximization framework for a two-hop opportunistic relaying scheme where the source node (S) transmits signals to multiple relay nodes (R) in the first time slot, and the selected relay antennas and their corresponding relay nodes receive, decode and forward the messages to the destination (D) in the second time slot. based on the system model, we develop a transmit antenna selection algorithm that maximizes the network capacity assuming that the …

Efficient Utilization Of Renewable Energy Sources By Gridable Vehicles In Cyber-Physical Energy Systems, Ahmed Yousuf Saber, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The main sources of emission today are from the electric power and transportation sectors. One of the main goals of a cyber-physical energy system (CPES) is the integration of renewable energy sources and grid able vehicles (GVs) to maximize emission reduction. GVs can be used as loads, sources and energy storages in CPES. a large CPES is very complex considering all conventional and green distributed energy resources, dynamic data from sensors, and smart operations (e.g., charging/discharging, control, etc.) from/to the grid to reduce both cost and emission. If large number of GVs are connected to the electric grid randomly, peak …

Recurrent Neural Networks Based Impedance Measurement Technique For Power Electronic Systems, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine, Jing Huang

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, and it is often necessary to measure system impedance through experiments. Traditional injection-based impedance measurement techniques require multiple online testing that lead to many disadvantages, including prolonged test time, operating point variations, and impedance values at limited frequency points. The impedance identification method proposed in this paper greatly reduces online testing time by modeling the system with recurrent neural …

Dynamic System Eigenvalue Extraction Using A Linear Echo State Network For Small-Signal Stability Analysis - A Novel Application, Jiaqi Liang, Jing Dai, Ganesh K. Venayagamoorthy, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

A large nonlinear dynamic system usually has complex dynamic modes corresponding to the system's eigenvalues. These eigenvalues govern the system's local behavior and thus are critical information for designing system operation and control strategies. Without the availability of the system's analytical model, which is often the case for large nonlinear systems, the system's eigenvalues need to be estimated. a linear echo state network (ESN) based method for extracting observable eigenvalues of a dynamic system together with the participation factors of these eigenvalues in the accessible system states is presented in this paper. a linear ESN is first trained to track …

An Adaptive Control Strategy For Dstatcom Applications In An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

Distribution static compensator (DSTATCOM) is a shunt compensation device that is generally used to solve power quality problems in distribution systems. In an all-electric ship power system, power quality issues arise due to high-energy demand loads such as pulse loads. This paper presents the application of a DSTATCOM to improve the power quality in a ship power system during and after pulse loads. The control strategy of the DSTATCOM plays an important role in maintaining the voltage at the point of common coupling. A novel adaptive control strategy for the DSTATCOM based on artificial immune system (AIS) is presented in …

Missing-Sensor-Fault-Tolerant Control For Sssc Facts Device With Real-Time Implementation, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald Harley

Electrical and Computer Engineering Faculty Research & Creative Works

Control of power systems relies on the availability and quality of sensor measurements. However, measurements are inevitably subjected to faults caused by sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software. These faults in turn may cause the failure of power system controllers and consequently severe contingencies in the power system. to avoid such contingencies, this paper presents a sensor evaluation and (missing sensor) restoration scheme (SERS) by using auto-associative neural networks (auto-encoders) and particle swarm optimization (PSO). based on the SERS, a missing-sensor-fault-tolerant control (MSFTC) is developed for controlling a static synchronous series …

Potentials And Promises Of Computational Intelligence For Smart Grids, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The electric power grid is a complex adaptive system under semi-autonomous distributed control. It is spatially and temporally complex, non-convex, nonlinear and non-stationary with a lot of uncertainties. the integration of renewable energy such as wind farms, and plug-in hybrid and electric vehicles further adds complexity and challenges to the various controllers at all levels of the power grid. a lot of efforts have gone into the development of a smart grid to align the interests of the electric utilities, consumers and environmentalists. Advanced computational methods are required for planning and optimization, fast control of power system elements, processing of …

Comparative Study Of Population Based Techniques For Power System Stabilizer Design, Pinaki Mitra, Chuan Yan, Lisa Grant, Ganesh K. Venayagamoorthy, Komla Folly

Electrical and Computer Engineering Faculty Research & Creative Works

Power System Stabilizers (PSSs) are used in interconnected power systems in order to mitigate low frequency oscillations. the comparison of the performances of four advanced population-based techniques in tuning the parameters of PSS in a three-machine-nine-bus test system is presented in this paper. the algorithms considered in this paper are A) Differential Evolution based Particle Swarm Optimization (DEPSO), b) Modified Clonal Selection Algorithm (MCSA), c) Small Population based Particle Swarm Optimization (SPPSO) and d) Population based Incremental Learning (PBIL). the comparative study is focused on the frequency domain performances. It is observed that MCSA is performing better than the other …

A Real-Time Implementation Of A Pbil Based Stabilizing Controller For Synchronous Generator, Komla A. Folly, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

This paper presents the optimal tuning of power system stabilizer parameters using a newly introduced evolutionary algorithm called Population based Incremental Learning (PBIL). to robustly stabilize the system, an objective function that minimizes the infinity norm of the closed-loop system is introduced such that the parameters of a fixed structure PSS are optimally tuned, and the controller stabilizes a pre-specified set of system models. the PBIL-PSS is compared with the Conventional PSS (CPSS). the simulation results presented in this paper show that the proposed PBIL-PSS is more effective than the Conventional PSS in damping the low frequency oscillations. the performance …

Getting An Nsf Career Award And Beyond, Ganesh Kumar Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

The National Science Foundation (NSF), USA, CAREER program is a premier program that emphasizes the importance the Foundation places on the early development of academic careers solely dedicated to stimulating the discovery process in which the excitement of research enriched by inspired teaching and enthusiastic learning. This paper presents the research and education experiences gained by the principal investigator - Vinayagamoorthy and his research collaborators and students as a result of a NSF CAREER proposal been awarded by the Power, Control and Adaptive Networks (PCAN) program of the Electrical, Communications and Cyber Systems Division, effective June 1, 2004. the main …

Hardware Implementation Of A Mamdani Fuzzy Logic Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Satish Rajagopalan, Ronald G. Harley

Electrical and Computer Engineering Faculty Research & Creative Works

A Mamdani-type fuzzy logic controller is designed and implemented in hardware for controlling a static compensator (STATCOM), which is connected to a ten-bus multimachine power system. Such a controller does not need any mathematical model of the plant to be controlled and can efficiently provide control signals for the STATCOM over a wide range of operating conditions of the power system and during different disturbances. The proposed controller is implemented using the M67 digital signal processor board and is interfaced to the multimachine power system simulated on a real-time digital simulator. Experimental results are provided, showing that the proposed Mamdani …

Learning Nonlinear Functions With Mlps And Srns, R. Cleaver, Ganesh K. Venayagamoorthy

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, nonlinear functions generated by randomly initialized multilayer perceptrons (MLPs) and simultaneous recurrent neural networks (SRNs) are learned by MLPs and SRNs. Training SRNs is a challenging task and a new learning algorithm - DEPSO is introduced. DEPSO is a standard particle swarm optimization (PSO) algorithm with the addition of a differential evolution step to aid in swarm convergence. The results from DEPSO are compared with the standard backpropagation (BP) and PSO algorithms. It is further verified that functions generated by SRNs are harder to learn than those generated by MLPs but DEPSO provides better learning capabilities for …