The paper proposes a novel adaptive three-phase autoreclosure technique for double circuit systems using a neural network approach. Based on the investigation of digital simulation of various types of fault on such sy...
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The paper proposes a novel adaptive three-phase autoreclosure technique for double circuit systems using a neural network approach. Based on the investigation of digital simulation of various types of fault on such systems, some salient features are summarized and extracted which are then used as the inputs of neuralnetworks. A three-layer neural network is constructed, trained and tested. The results indicate that the proposed approach is very reliable.
Quick-Response Excitation (QRE), Dynamic Resistance Braking (DRB) and Fast Valving (FV) are all the important measures to improve the stability of power systems. All the time it is a difficult problem to coordinate th...
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Quick-Response Excitation (QRE), Dynamic Resistance Braking (DRB) and Fast Valving (FV) are all the important measures to improve the stability of power systems. All the time it is a difficult problem to coordinate the three kinds of nonlinear controllers. In this paper, a cooperative control of QRE, DRB and FV based on artificial neuralnetworks (ANN) is proposed. Both the steady-state stability limits and the transient stability limits of the power system have been improved greatly.
This paper describes a neural network and its simulation results for fault diagnosis in HVDC systems. Fault diagnosis is carried out by mapping input data patterns, which represent the behaviour of the system, to one ...
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This paper describes a neural network and its simulation results for fault diagnosis in HVDC systems. Fault diagnosis is carried out by mapping input data patterns, which represent the behaviour of the system, to one or more fault conditions. The behaviour of the converters is described in terms of the time varying patterns of conducting thyristors and ac & dc fault characteristics. A three-layer neural network consisting of 20 input nodes, 12 hidden nodes and 2 output nodes is used. This paper will describe the performance of the network for ac and dc faults due to changes in number of hidden layers, number of neurons in the layer, learning rate and momentum. Dynamic characteristics of networks for different configurations are studied too. The time performance of the network is also included. neuralnetworks provide an effective way for fault diagnosis and identification. Simulation data obtained from the EMTP will be used to test the performance of this network. The comparison will be given and the result is promising.
The paper deals with new developments of interpolating memories as the basic element of learning control and with their possible application. It discusses learning control, interpolating memories, characteristic manif...
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The paper deals with new developments of interpolating memories as the basic element of learning control and with their possible application. It discusses learning control, interpolating memories, characteristic manifolds for automotive control, and possible future developments.< >
Hypersonic aircraft require a high degree of system integration. Design tools are needed that can provide rapid, accurate calculations of complex fluid flow. Existing methods are slow. The goal of this project was to ...
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Hypersonic aircraft require a high degree of system integration. Design tools are needed that can provide rapid, accurate calculations of complex fluid flow. Existing methods are slow. The goal of this project was to apply neuralnetworks to the calculation of fluid flow and heat transfer in a heat exchanger panel for the National AeroSpace Plane (NASP).< >
In this contribution, neural concepts and methods for control an their use in industrial applications are discussed and illustrated. Even though there exist numerous conventional approaches for solving control tasks, ...
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In this contribution, neural concepts and methods for control an their use in industrial applications are discussed and illustrated. Even though there exist numerous conventional approaches for solving control tasks, their realization in practice frequently proves to be very difficult. The author pursues several approaches to using neuralnetworks in the context of nonlinear control tasks. In identification, networks are trained to model the dynamics of an unknown nonlinear plant. The model provides the basis for controller design or system diagnosis. In robot control, networks are trained to model the inverse dynamics of the robot. The inverse model is used to linearize the system which is then accessible for the well-established tools of linear control theory. In another context, a network is used as a nonlinear trainable controller.< >
The training speed of batch backpropagation using steepest descent, conjugate gradient and quasi-Newton algorithm for a feedforward neural network are compared. Results illustrating the advantages of the Hessian based...
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The training speed of batch backpropagation using steepest descent, conjugate gradient and quasi-Newton algorithm for a feedforward neural network are compared. Results illustrating the advantages of the Hessian based techniques are given and issues affecting speed discussed.< >
The problem of adjusting the weights (learning) in multilayer feedforward neuralnetworks (NN) is known to be of a high importance when utilizing NN techniques in various practical applications. The learning procedure...
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The problem of adjusting the weights (learning) in multilayer feedforward neuralnetworks (NN) is known to be of a high importance when utilizing NN techniques in various practical applications. The learning procedure is to be performed as fast as possible and in a simple computational fashion, the two requirements which are usually not satisfied practically by the methods developed so far. Moreover, the presence of random inaccuracies are usually not taken into account. In view of these three issues, an alternative stochastic approximation approach discussed in the paper, seems to be very promising.< >
Because of the clustering and dimensionality reduction abilities the Kohonen feature map (KFM) can be the preferable tool for deriving knowledge about dependencies of the load consumption in electrical energy systems ...
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Because of the clustering and dimensionality reduction abilities the Kohonen feature map (KFM) can be the preferable tool for deriving knowledge about dependencies of the load consumption in electrical energy systems (EES). This paper describes the application of the KFM for analysing and splitting extensive load databases. The objective is to get separate clusters of load shapes for making short term load forecast (STLF) models with a high accuracy. The building of the forecast models is based on feedforward neuralnetworks (NN).< >
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