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Neural networks based non-uniform scalar quantizer design with particle swarm optimization

Neural networks based non-uniform scalar quantizer design wi...

引用

2005 IEEE Swarm Intelligence Symposium, SIS 2005

作者： Zha, Wenwei Venayagamoorthy, Ganesh K. Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States

ISBN: (纸本)0780389166

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. © 2005 IEEE.

关键词： Neural networks

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Wide area power system protection using a learning vector quantization network

Wide area power system protection using a learning vector qu...

引用

13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05

作者： Venayagamoorthy, Ganesh K. Zarghami, Mahyar IEEE Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla MO 65409 United States

ISBN: (纸本)1599750287

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 wide area network to the other areas of the system. The scheme has been applied to a two-area power system in this paper and the LVQ results show that it is a promising scheme for system protection against partial or total blackouts or brown outs. © 2005 ISAP.

关键词： Wide area networks

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A neural network based optimal wide area control scheme for a power system

A neural network based optimal wide area control scheme for ...

引用

2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting

作者： Venayagamoorthy, Ganesh K. Ray, Swakshar IEEE Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States

ISBN: (纸本)0780392086

With deregulation of the power industry, many tie lines between control areas are driven to operate near their maximum capacity, especially those serving heavy load centers. Wide area control systems (WACSs) 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. This paper presents the design and the DSP implementation of a nonlinear optimal wide area controller based on adaptive critic designs and neural networks for a power system on the real-time Digital Simulator (RTDS®). The performance of the WACS as a power system stability agent is studied using the Kundur's two area power system example. The WACS provides better damping of power system oscillations under small and large disturbances even with the inclusion of local power system stabilizers. © 2005 IEEE.

关键词： Neural networks

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Comparison of non-uniform optimal quantizer designs for speech coding with adaptive critics and particle swarm

Comparison of non-uniform optimal quantizer designs for spee...

引用

2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting

作者： Zha, Wenwei Venayagamoorthy, Ganesh K. IEEE Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States

ISBN: (纸本)0780392086

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 Evaluation of Speech Quality (PESQ) standard. © 2005 IEEE.

关键词： Speech coding

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Priority refinement for dependent tasks in large embedded real-time software

Priority refinement for dependent tasks in large embedded re...

引用

IEEE Symposium on real-time and Embedded Technology and Applications

作者： J.R. Merrick S. Wang K.G. Shin Department of Electrical Engineering and Computer Science Real-Time Computing Laboratory The University of Michigan Ann Arbor MI

In a large embedded real-time system, priority assignment can greatly affect the timing behavior - which can consequently affect the overall behavior - of the system. Thus, it is crucial for model-based design of a large embedded real-time system to be able to intelligently assign priorities such that tasks can meet their deadlines. In this paper, we propose a priority-refinement method for dependent tasks distributed throughout a heterogeneous multiprocessor environment. In this method, we refine an initial priority assignment iteratively using the simulated annealing technique with tasks' latest completion times (LCT). Our evaluations, based on randomly-generated models, have shown that the refinement method outperforms other priority-assignment schemes and scales well for large, complex, real-time systems. This method has been implemented in the Automatic Integration of Reusable Embedded Software (AIRES) toolkit and has been successfully applied to a vehicle system control application.

关键词： Embedded software real time systems Timing Simulated annealing Vehicles Control systems Application software Programming Software architecture Embedded computing

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Hardware implementation of a Mamdani fuzzy logic controller for a static compensator in a multimachine power system

Hardware implementation of a Mamdani fuzzy logic controller ...

引用

2005 IEEE Industry Applications Conference, 40th IAS Annual Meeting

作者： Mohagheghi, Salman Venayagamoorthy, Ganesh K. Rajagopalan, Satish Harley, Donald G. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla Rolla MO 65409-0249 United States

ISBN: (纸本)0780392086

A Mamdani based fuzzy logic controller is designed and implemented for controlling a STATCOM, which is connected to a 10 bus multimachine power system. Such a controller does not need any prior knowledge of the plant to be controlled and can efficiently provide control signals for the STATCOM during different disturbances in the network. The proposed controller is implemented using the M67 DSP board and is interfaced to the multimachine power system simulated on a real-time Digital Simulator (RTDS). Experimental results are provided, showing that the proposed controller provides more effective damping than the conventional PI controller in a typical large scale disturbance, i.e., a three phase short circuit. © 2005 IEEE.

关键词： Fuzzy control

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A comparison of PSO and backpropagation for training RBF neural networks for identification of a power system with statcom

A comparison of PSO and backpropagation for training RBF neu...

引用

2005 IEEE Swarm Intelligence Symposium, SIS 2005

作者： Mohaghegi, Salman Del Valle, Yamille Venayagamoorthy, Ganesh K. Harley, Ronald G. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States University of KwaZulu Natal South Africa

ISBN: (纸本)0780389166

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. © 2005 IEEE.

关键词： Radial basis function networks

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An adaptive mamdani fuzzy logic based controller for a static compensator in a multimachine power system

An adaptive mamdani fuzzy logic based controller for a stati...

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13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05

作者： Mohagheghi, S. Venayagamoorthy, G.K. Harley, R.G. IEEE School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri-Rolla MO 65409-0249 United States University of KwaZulu-Natal South Africa

ISBN: (纸本)1599750287

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 PI controller during dynamic and transient disturbances. © 2005 ISAP.

关键词： Programmable logic controllers

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real-time dual heuristic program-ming-based neurocontroller for a turbogenerator in a multimachine power system

Real-time dual heuristic program-ming-based neurocontroller ...

引用

作者： Venayagamoorthy, Ganesh K. Harle, Ronald G. Wunsch, Donald C. Real-Time Power and Intelligent Systems Laboratory University of Missouri Rolla MO 65409 United States Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409 United States School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States

Based on Dual Heuristic Programming (DHP), a real-time implementation of a neurocontroller for excitation and turbine control of a turbogenerator in a multimachine power system is presented. The feedback variables are completely based on local measurements. Simulation and real-time hardware implementation on a three-machine system demonstrate that the DHP neurocontroller is much more effective than conventional PID controllers, the automatic voltage regulator, power system stabilizer and the governor, for improving dynamic performance and stability under small and large disturbances. © 2005 CRL Publishing Ltd.

关键词： Heuristic programming

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A dynamic recurrent neural network for wide area identification of a multimachine power system with a FACTS device

A dynamic recurrent neural network for wide area identificat...

引用

13th International Conference on Intelligent Systems Application to Power Systems, ISAP'05

作者： Mohagheghi, S. Venayagamoorthy, G.K. Harley, R.G. IEEE School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332-0250 United States Real-Time Power and Intelligent Systems Laboratory Department of Electrical and Computer Engineering University of Missouri Rolla MO 65409-0249 United States University of KwaZulu-Natal South Africa

ISBN: (纸本)1599750287

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 for supervisory level identification of a multimachine power system with a FACTS device. Simulation results are provided to show that the neuroidentifier can track the system dynamics with sufficient accuracy. © 2005 ISAP.

关键词： Electric power systems

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