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Full-Text Articles in Engineering
Implementation Of A Pso Based Online Design Of An Optimal Excitation Controller, Chuan Yan, Ganesh K. Venayagamoorthy, Keith Corzine
Implementation Of A Pso Based Online Design Of An Optimal Excitation Controller, Chuan Yan, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
The Navypsilas future electric ships will contain a number of pulsed power loads for high-energy applications such as radar, railguns, and advanced weapons. This pulse energy demand has to be provided by the ship energy sources, while not impacting the operation of the rest of the system. It is clear from studies carried out earlier that disturbances are created at the generator ac bus. This paper describes an online design and laboratory hardware implementation of an optimal excitation controller using particle swarm optimization (PSO) to minimize the effects of pulsed loads. The PSO algorithm has been implemented on a digital …
Optimal Svm Switching For A Multilevel Multi-Phase Machine Using Modified Discrete Pso, Chris M. Hutson, Ganesh K. Venayagamoorthy, Keith Corzine
Optimal Svm Switching For A Multilevel Multi-Phase Machine Using Modified Discrete Pso, Chris M. Hutson, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
This paper searches for the best possible switching sequence in a multilevel multi-phase inverter that gives the lowest amount of voltage harmonics. A modified discrete particle swarm (MDPSO) algorithm is used in an attempt to find the optimal space vector modulation switching sequence that results in the lowest voltage THD. As with typical PSO cognitive and social parameters are used to guide the search, but an additional mutation term is added to broaden the amount of area searched. The search space is the feasible solutions for the predetermined vectors at a given modulation index. Comparison of the MDPSO algorithm to …
Human Swarm Interaction For Radiation Source Search And Localization, Shishir Bashyal, Ganesh K. Venayagamoorthy
Human Swarm Interaction For Radiation Source Search And Localization, Shishir Bashyal, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This study shows that appropriate human interaction can benefit a swarm of robots to achieve goals more efficiently. A set of desirable features for human swarm interaction is identified based on the principles of swarm robotics. Human swarm interaction architecture is then proposed that has all of the desirable features. A swarm simulation environment is created that allows simulating a swarm behavior in an indoor environment. The swarm behavior and the results of user interaction are studied by considering radiation source search and localization application of the swarm. Particle swarm optimization algorithm is slightly modified to enable the swarm to …
Empirical Study Of A Hybrid Algorithm Based On Clonal Selection And Small Population Based Pso., Pinaki Mitra, Ganesh K. Venayagamoorthy
Empirical Study Of A Hybrid Algorithm Based On Clonal Selection And Small Population Based Pso., Pinaki Mitra, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a hybrid algorithm, based on clonal selection algorithm (CSA) and small population based particle swarm optimization (SPPSO) is introduced. The performance of this new algorithm (CS2P2SO) is observed for four well known benchmark functions. The SPPSO is a variant of conventional PSO (CPSO), introduced by the second author of this paper, where a very small number of initial particles are used and after a few iterations, the best particle is kept and the rest are replaced by the same number of regenerated particles. On the other hand, CSA belongs to the family of artificial immune system (AIS). …
Particle Swarm Optimization With Quantum Infusion For The Design Of Digital Filters, Bipul Luitel, Ganesh K. Venayagamoorthy
Particle Swarm Optimization With Quantum Infusion For The Design Of Digital Filters, Bipul Luitel, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, particle swarm optimization with quantum infusion (PSO-QI) has been applied for the design of digital filters. In PSO-QI, Global best (gbest) particle (in PSO star topology) obtained from particle swarm optimization is enhanced by doing a tournament with an offspring produced by quantum behaved PSO, and selecting the winner as the new gbest. Filters are designed based on the best approximation to the ideal response by minimizing the maximum ripples in passband and stopband of the filter response. PSO-QI, as is shown in the paper, converges to a better fitness. This new algorithm is implemented in the …
Swarm Intelligence And Evolutionary Approaches For Reactive Power And Voltage Control, Ganesh K. Venayagamoorthy, G. Krost, G. A. Bakare, Lisa L. Grant
Swarm Intelligence And Evolutionary Approaches For Reactive Power And Voltage Control, Ganesh K. Venayagamoorthy, G. Krost, G. A. Bakare, Lisa L. Grant
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a comparison of swarm intelligence and evolutionary techniques based approaches for minimization of system losses and improvement of voltage profiles in a power network. Efficient distribution of reactive power in an electric network can be achieved by adjusting the excitation on generators, the on-load tap changer positions of transformers, and proper switching of discrete portions of inductors or capacitors. This is a mixed integer non-linear optimization problem where metaheuristics techniques have proven suitable for providing optimal solutions. Four algorithms explored in this paper include differential evolution (DE), particle swarm optimization (PSO), a hybrid combination of DE and …
Comparison Of De And Pso For Generator Maintenance Scheduling, Yusuf Yare, Ganesh K. Venayagamoorthy
Comparison Of De And Pso For Generator Maintenance Scheduling, Yusuf Yare, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a comparison of a differential evolution (DE) algorithm and a modified discrete particle swarm optimization (MDPSO) algorithm for generating optimal preventive maintenance schedules for economical and reliable operation of a power system, while satisfying system load demand and crew constraints. The DE, an evolutionary technique and an optimization algorithm utilizes the differential information to guide its further search, and can handle mixed integer discrete continuous optimization problems. Discrete particle swarm optimization (DPSO) is known to effectively solve large scale multi-objective optimization problems and has been widely applied in power systems. Both the DE and MDPSO are applied …
Implementation Of Neuroidentifiers Trained By Pso On A Plc Platform For A Multimachine Power System, Curtis Alan Parrott, Ganesh K. Venayagamoorthy
Implementation Of Neuroidentifiers Trained By Pso On A Plc Platform For A Multimachine Power System, Curtis Alan Parrott, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Power systems are nonlinear with fast changing dynamics. In order to design a nonlinear adaptive controller for damping power system oscillations, it becomes necessary to identify the dynamics of the system. This paper demonstrates the implementation of a neural network based system identifier, referred to as a neuroidentifier, on a programmable logic controller (PLC) platform. Two separate neuroidentifiers are trained using the particle swarm optimization (PSO) algorithm to identify the dynamics in a two-area four machine power system, one neuroidentifier for Area 1 and the other for Area 2. The power system is simulated in real time on the Real …
Dsp-Based Pso Implementation For Online Optimization Of Power System Stabilizers, Parviz Palangpour, Pinaki Mitra, Swakshar Ray, Ganesh K. Venayagamoorthy
Dsp-Based Pso Implementation For Online Optimization Of Power System Stabilizers, Parviz Palangpour, Pinaki Mitra, Swakshar Ray, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Real-time implementations of controllers require optimization algorithms which can be performed quickly. In this paper, a digital signal processor (DSP) implementation of particle swarm optimization (PSO) is presented. PSO is used to optimize the parameters of two stabilizers used in a power system. The controllers and PSO are both implemented on a single DSP in a hardware-in-loop configuration. Results showing the performance and feasibility for real-time implementations of PSO are presented.
Artificial Immune System Based Dstatcom Control For An Electric Ship Power System, Pinaki Mitra, Ganesh K. Venayagamoorthy
Artificial Immune System Based Dstatcom Control For 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 which is generally used to solve power quality problems in distribution systems. In an all-electric ship power system, these power quality problems mainly arise due to the pulsed loads, which causes the degradation of the entire system performance. This paper presents the application of DSTATCOM to improve the power quality in a ship power system during and after pulsed 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 …
An Estimation Of Distribution Improved Particle Swarm Optimization Algorithm, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy
An Estimation Of Distribution Improved Particle Swarm Optimization Algorithm, Raghavendra V. Kulkarni, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
PSO is a powerful evolutionary algorithm used for finding global solution to a multidimensional problem. Particles in PSO tend to re-explore already visited bad solution regions of search space because they do not learn as a whole. This is avoided by restricting particles into promising regions through probabilistic modeling of the archive of best solutions. This paper presents hybrids of estimation of distribution algorithm and two PSO variants. These algorithms are tested on benchmark functions having high dimensionalities. Results indicate that the methods strengthen the global optimization abilities of PSO and therefore, serve as attractive choices to determine solutions to …
Optimal Scheduling Of Generator Maintenance Using Modified Discrete Particle Swarm Optimization, Yusuf Yare, Ganesh K. Venayagamoorthy
Optimal Scheduling Of Generator Maintenance Using Modified Discrete Particle Swarm Optimization, Yusuf Yare, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a modified discrete particle swarm optimization (PSO) based technique for generating optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system while satisfying system load demand and crew constraints. While GA and other analytical methods might suffer from premature convergence and the curse of dimensionality, heuristics based swarm intelligence can be an efficient alternative. PSO is known to effectively solve large scale multi-objective optimization problems. Here, a modified discrete PSO approach is proposed for the GMS optimization problem in order to overcome the limitations of the conventional methods and come up …
Dhp-Based Wide-Area Coordinating Control Of A Power System With A Large Wind Farm And Multiple Facts Devices, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Dhp-Based Wide-Area Coordinating Control Of A Power System With A Large Wind Farm And Multiple Facts Devices, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Wide-area coordinating control is becoming an important issue and a challenging problem in the power industry. This paper proposes a novel optimal wide-area monitor and wide-area coordinating neurocontroller (WACNC), based on wide-area measurements, for a power system with power system stabilizers, a large wind farm, and multiple flexible ac transmission system (FACTS) devices. The wide-area monitor is a radial basis function neural network (RBFNN) that identifies the input-output dynamics of the nonlinear power system. Its parameters are optimized through a particle swarm optimization (PSO) based method. The WACNC is designed by using the dual heuristic programming (DHP) method and RBFNNs. …
Identification Of Induction Machines Stator Currents With Generalized Neurons, Jing Huang, Ganesh K. Venayagamoorthy, Keith Corzine
Identification Of Induction Machines Stator Currents With Generalized Neurons, Jing Huang, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
A new approach to identify the nonlinear model of an induction machine using two generalized neurons (GNs) is presented in this paper. Compared to the multilayer perceptron feedforward neural network, a GN has simpler structure and lesser requirement in terms of memory storage which is makes it attractive for hardware implementation. This method shows that with less number of weights, GN is able to learn the dynamics of an induction machine. The proposed model is made by two coupled networks. A modified particle swarm optimization algorithm is designed to solve this distinctive GN training problem. A pseudo-random binary sequence signal …
Combined Training Of Recurrent Neural Networks With Particle Swarm Optimization And Backpropagation Algorithms For Impedance Identification, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Combined Training Of Recurrent Neural Networks With Particle Swarm Optimization And Backpropagation Algorithms For Impedance Identification, Peng Xiao, Ganesh K. Venayagamoorthy, Keith Corzine
Electrical and Computer Engineering Faculty Research & Creative Works
A recurrent neural network (RNN) trained with a combination of particle swarm optimization (PSO) and backpropagation (BP) algorithms is proposed in this paper. The network is used as a dynamic system modeling tool to identify the frequency-dependent impedances of power electronic systems such as rectifiers, inverters, and DC-DC converters. As a category of supervised learning methods, the various backpropagation training algorithms developed for recurrent neural networks use gradient descent information to guide their search for optimal weights solutions that minimize the output errors. While they prove to be very robust and effective in training many types of network structures, they …
A Fuzzy-Pso Based Controller For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
A Fuzzy-Pso Based Controller For A Grid Independent Photovoltaic System, Richard L. Welch, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents a particle swarm optimization (PSO) method for optimizing a fuzzy logic controller (FLC) for a photovoltaic (PV) grid independent system consisting of a PV collector array, a storage battery, and loads (critical and non-critical loads). PSO is used to optimize both the membership functions and the rule set in the design of the FLC. Optimizing the PV system controller yields improved performance, allowing the system to meet more of the loads and keep a higher average state of battery charge. Potential benefits of an optimized controller include lower costs through smaller system sizing and a longer battery …
Gene Expression Data For Dlbcl Cancer Survival Prediction With A Combination Of Machine Learning Technologies, Rui Xu, Xindi Cai, Donald C. Wunsch
Gene Expression Data For Dlbcl Cancer Survival Prediction With A Combination Of Machine Learning Technologies, Rui Xu, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
Gene expression profiles have become an important and promising way for cancer prognosis and treatment. In addition to their application in cancer class prediction and discovery, gene expression data can be used for the prediction of patient survival. Here, we use particle swarm optimization (PSO) to address one of the major challenges in gene expression data analysis, the curse of dimensionality, in order to discriminate high risk patients from low risk patients. A discrete binary version of PSO is used for gene selection and dimensionality reduction, and a probabilistic neural network (PNN) is implemented as the classifier. The experimental results …
Optimal Design Of Power System Stabilizers Using A Small Population Based Pso, Ganesh K. Venayagamoorthy, Tridib Kumar Das
Optimal Design Of Power System Stabilizers Using A Small Population Based Pso, Ganesh K. Venayagamoorthy, Tridib Kumar Das
Electrical and Computer Engineering Faculty Research & Creative Works
Power system stabilizers (PSSs) are used to generate supplementary control signals to excitation systems in order to damp out local and inter-area oscillations. In this paper, a modified particle swarm optimization (PSO) algorithm with a small population is presented for the design of optimal PSSs. The small population based PSO (SPPSO) is used to determine the optimal parameters of several PSSs simultaneously in a multi-machine power system. In order to maintain a dynamic search process, the idea of particle regeneration in the population is also proposed. Optimal PSS parameters are determined for the power system subjected to small and large …
Fuzzy Pso: A Generalization Of Particle Swarm Optimization, S. Abdelshahid, Donald C. Wunsch, Ashraf M. Abdelbar
Fuzzy Pso: A Generalization Of Particle Swarm Optimization, S. Abdelshahid, Donald C. Wunsch, Ashraf M. Abdelbar
Electrical and Computer Engineering Faculty Research & Creative Works
In standard particle swarm optimization (PSO), the best particle in each neighborhood exerts its influence over other particles in the neighborhood. In this paper, we propose fuzzy PSO, a generalization which differs from standard PSO in the following respect: charisma is defined to be a fuzzy variable, and more than one particle in each neighborhood can have a non-zero degree of charisma, and, consequently, is allowed to influence others to a degree that depends on its charisma. We evaluate our model on the weighted maximum satisfiability (maxsat) problem, comparing performance to standard PSO and to Walk-Sat.
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Engine Data Classification With Simultaneous Recurrent Network Using A Hybrid Pso-Ea Algorithm, Xindi Cai, Donald C. Wunsch
Electrical and Computer Engineering Faculty Research & Creative Works
We applied an architecture which automates the design of simultaneous recurrent network (SRN) using a new evolutionary learning algorithm. This new evolutionary learning algorithm is based on a hybrid of particle swarm optimization (PSO) and evolutionary algorithm (EA). By combining the searching abilities of these two global optimization methods, the evolution of individuals is no longer restricted to be in the same generation, and better performed individuals may produce offspring to replace those with poor performance. The novel algorithm is then applied to the simultaneous recurrent network for the engine data classification. The experimental results show that our approach gives …
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Neural Networks Based Non-Uniform Scalar Quantizer Design With Particle Swarm Optimization, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
Quantization is a crucial link in the process of digital speech communication. Non-uniform quantizer such as the logarithm quantizers are commonly used in practice. In this paper, a companding non-uniform quantizer is designed using two neural networks to perform the nonlinear transformation. Particle swarm optimization is applied to find the weights of neural networks such that the signal to noise ratio (SNR) is maximized. Simulation results on different speech samples are presented and the proposed quantizer design is compared with the logarithm quantizer for bit rates ranging from 3 to 8.
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Comparison Of Non-Uniform Optimal Quantizer Designs For Speech Coding With Adaptive Critics And Particle Swarm, Wenwei Zha, Ganesh K. Venayagamoorthy
Electrical and Computer Engineering Faculty Research & Creative Works
This paper presents the design of a companding non-uniform 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 (ACD) and particle swarm optimization (PSO), aiming to maximize the signal to noise ratio (SNR). The comparison of these optimal quantizer designs over bit rate range of 3 to 6 is presented. The perceptual quality of the coding is evaluated by the International Telecommunication Union''s Perceptual …
Improving The Performance Of Particle Swarm Optimization Using Adaptive Critics Designs, Ganesh K. Venayagamoorthy, Sheetal Doctor
Improving The Performance Of Particle Swarm Optimization Using Adaptive Critics Designs, Ganesh K. Venayagamoorthy, Sheetal Doctor
Electrical and Computer Engineering Faculty Research & Creative Works
Swarm intelligence algorithms are based on natural behaviors. Particle swarm optimization (PSO) is a stochastic search and optimization tool. Changes in the PSO parameters, namely the inertia weight and the cognitive and social acceleration constants, affect the performance of the search process. This paper presents a novel method to dynamically change the values of these parameters during the search. Adaptive critic design (ACD) has been applied for dynamically changing the values of the PSO parameters.