Open Access. Powered by Scholars. Published by Universities.®
- Keyword
-
- Neurocontrollers (27)
- Turbogenerators (18)
- Power System Control (16)
- Multimachine Power System (15)
- Optimal Control (15)
-
- Machine Control (12)
- Learning (Artificial Intelligence) (10)
- Adaptive Critic Designs (9)
- Neural Networks (9)
- Power System Stability (9)
- Adaptive Control (8)
- Backpropagation (8)
- Control System Analysis (8)
- Control Simulation (7)
- Control System Synthesis (7)
- Heuristic Programming (7)
- Machine Theory (7)
- Multilayer Perceptrons (7)
- Neural Nets (7)
- Nonlinear Control Systems (7)
- Static VAr Compensators (7)
- Voltage Control (7)
- Automatic Voltage Regulator (6)
- Heuristic Dynamic Programming (6)
- Particle Swarm Optimization (6)
- Power System (6)
- Turbine Governor (6)
- Turbogenerator (6)
- Dynamic Programming (5)
- Feedforward Neural Nets (5)
Articles 61 - 90 of 125
Full-Text Articles in Engineering
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 …
Effects Of Facts Devices On A Power System Which Includes A Large Wind Farm, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Effects Of Facts Devices On A Power System Which Includes A Large Wind Farm, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
No abstract provided.
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 …
Fast Time-Varying Dispersive Channel Estimation And Equalization For 8-Psk Cellular System, Sang-Yick Leong, Jingxian Wu, Chengshan Xiao, Jan C. Olivier
Fast Time-Varying Dispersive Channel Estimation And Equalization For 8-Psk Cellular System, Sang-Yick Leong, Jingxian Wu, Chengshan Xiao, Jan C. Olivier
Electrical and Computer Engineering Faculty Research & Creative Works
In this paper, a novel channel-estimation scheme for an 8-PSK enhanced data rates for GSM evolution (EDGE) system with fast time-varying and frequency-selective fading channels is presented. via a mathematical derivation and simulation results, the channel impulse response (CIR) of the fast fading channel is modeled as a linear function of time during a radio burst in the EDGE system. Therefore, a least-squares-based method is proposed along with the modified burst structure for time-varying channel estimation. Given that the pilot-symbol blocks are located at the front and the end of the data block, the LS-based method is able to estimate …
Adaptive Critic Design Based Neuro-Fuzzy Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Design Based Neuro-Fuzzy 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
This paper presents a novel nonlinear optimal controller for a static compensator (STATCOM) connected to a power system, using artificial neural networks and fuzzy logic. The action dependent heuristic dynamic programming, a member of the adaptive Critic designs family, is used for the design of the STATCOM neuro-fuzzy controller. This neuro-fuzzy controller provides optimal control based on reinforcement learning and approximate dynamic programming. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system. Two multimachine systems are considered in this study: a 10-bus system …
A Fault-Tolerant P-Q Decoupled Control Scheme For Static Synchronous Series Compensator, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
A Fault-Tolerant P-Q Decoupled Control Scheme For Static Synchronous Series Compensator, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
Control of nonlinear devices in power systems relies on the availability and the quality of sensor measurements. Measurements can be corrupted or interrupted due to sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software (referred to as missing sensor measurements in this paper). This paper proposes a fault-tolerant control scheme (FTCS) for a static synchronous series compensator (SSSC). This FTCS consists of a sensor evaluation and (missing sensor) restoration scheme (SERS) cascaded with a P-Q decoupled control scheme (PQDC). It is able to provide effective control to the SSSC when single or multiple crucial …
Adaptive Critic Designs Based Coupled Neurocontrollers For A Static Compensator, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Adaptive Critic Designs Based Coupled Neurocontrollers For A Static Compensator, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
A novel nonlinear optimal neurocontroller for a static compensator (STATCOM) connected to a power system, using artificial neural networks, is presented in this paper. The heuristic dynamic programming (HDP) method, a member of the adaptive critic designs (ACD) family, is used for the design of the STATCOM neurocontroller. The proposed controller is a nonlinear optimal controller that provides coupled control for the line voltage and the dc link voltage regulation loops of the STATCOM. An action dependent approach is used, in which the controller is independent of a model of the network. Moreover, a proportional-integrator approach allows the neurocontroller to …
An Interval Type-Ii Robust Fuzzy Logic Controller For A Static Compensator In A Multimachine Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Ronald G. Harley
An Interval Type-Ii Robust Fuzzy Logic 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
This paper presents a novel fuzzy logic based controller for a Static Compensator (STATCOM) connected to a power system. Type-II fuzzy systems are selected that enable the controller to deal with design uncertainties and the noise associated with the measurements in the power system. Interval type-II fuzzy is computationally more effective than the ordinary type-II fuzzy systems and is more suitable for the power network with fast changing dynamics. Using a proportional-integrator approach the proposed controller is capable of dealing with actual rather than deviation signals. The STATCOM is connected to a multimachine power system in order to provide extra …
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.
Multiple Statcom Allocation And Sizing Using Particle Swarm Optimization, Ganesh K. Venayagamoorthy, J. C. Hernandez, Yamille Del Valle, Ronald G. Harley
Multiple Statcom Allocation And Sizing Using Particle Swarm Optimization, Ganesh K. Venayagamoorthy, J. C. Hernandez, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
No abstract provided.
Fault-Tolerant Control For Sssc Using Neural Networks And Pso, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Fault-Tolerant Control For Sssc Using Neural Networks And Pso, Wei Qiao, Ganesh K. Venayagamoorthy, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
No abstract provided.
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 …
Hierarchical Control Scheme For Two Static Compensators In The Brazilian 45-Bus Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Hierarchical Control Scheme For Two Static Compensators In The Brazilian 45-Bus Power System, Salman Mohagheghi, Ganesh K. Venayagamoorthy, Yamille Del Valle, Ronald G. Harley
Electrical and Computer Engineering Faculty Research & Creative Works
No abstract provided.
Recurrent Neural Network Based Predictions Of Elephant Migration In A South African Game Reserve, Parviz Palangpour, Ganesh K. Venayagamoorthy, Kevin Duffy
Recurrent Neural Network Based Predictions Of Elephant Migration In A South African Game Reserve, Parviz Palangpour, Ganesh K. Venayagamoorthy, Kevin Duffy
Electrical and Computer Engineering Faculty Research & Creative Works
A large portion of South Africa''s elephant population can be found on small wildlife reserves. When confined to enclosed reserves the elephant densities are much higher than observed in the wild. The large nutritional demands and destructive foraging behavior of elephants threaten rare species of vegetation. If conservation management is to protect threatened species of vegetation, knowing how long elephants will stay in one area of the reserve as well as which area they will move to next is essential. The goal of this study is to train a recurrent neural network (RNN) to continuously predict an elephant herd''s next …
Mlp/Rbf Neural-Networks-Based Online Global Model Identification Of Synchronous Generator, Jung-Wook Park, Ganesh K. Venayagamoorthy, Ronald G. Harley
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 …
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
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
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
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
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
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 …
Two Separate Continually Online-Trained Neurocontrollers For A Unified Power Flow Controller, Ganesh K. Venayagamoorthy, Radha P. Kalyani
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
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
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
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
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
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
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
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
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
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.