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Co-ordinative control of excitation, fast valving and resistant braking using artificial neural network

Co-ordinative control of excitation, fast valving and resist...

引用

Proceedings of the 2nd International Conference on advances in Power System control, Operation & Management

作者： Yong, Min Lin-cheng, Xiu Li, Zhang Qi-rong, Jiang Tsinghua Univ Beijing China

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 neural networks (ANN) is proposed. Both the steady-state stability limits and the transient stability limits of the power system have been improved greatly.

关键词： neural networks

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Learning in neural networks and stochastic approximation methods with averaging

Learning in neural networks and stochastic approximation met...

引用

iee colloquium on advances in neural networks for control and systems

作者： P.S. Shcherbakov S.N. Tikhonov J.D. Mason K. Warwick Institute of Control Sciences RAS Moscow Russia Department of Cybernetics Reading University Reading UK

The problem of adjusting the weights (learning) in multilayer feedforward neural networks (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.< >

关键词： Approximation methods Feedforward neural networks Learning systems

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Data analysis by means of Kohonen feature maps for load forecast in power systems

Data analysis by means of Kohonen feature maps for load fore...

引用

iee colloquium on advances in neural networks for control and systems

作者： S. Heine I. Neumann Fachbereich Elektrotechnik Technische Hochschule Leipzig Leipzig Germany

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 neural networks (NN).< >

关键词： Feedforward neural networks Load forecasting Self-organizing feature maps

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Field programmable gate array implementation of a neural network accelerator

Field programmable gate array implementation of a neural net...

引用

Computing and control Division colloquium on Hardware Implementation of neural networks and Fuzzy Logic

作者： Reay, D.S. Green, T.C. Williams, B.W. Heriot-Watt Univ Edinburgh United Kingdom

The use of a neural network to learn the nonlinear current profiles required to minimise torque ripple in a switched reluctance motor (SRM), at low to medium speeds, has been demonstrated using a digital signal processor (DSP). However, the DSP (Texas Instruments TMS320C25) implementation of a neural network in this application is a limiting factor on motor speed (if maximum current profile integrity is to be maintained). Fortunately, the neural network architecture used (cerebellar model articulation controller (CMAC)) is amenable to hardware implementation a point noted by Albus in one of his original papers and evidenced by at least one previous field programmable gate array (FPGA) implementation. Guided by the requirements of the switched reluctance motor application, a prototype accelerator based on a Xilinx XC4000 FPGA implementation of the neural network has been constructed that operates an order of magnitude faster than the DSP implementation.

关键词： neural networks

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Constructive learning-industrial perspectives

Constructive learning-industrial perspectives

引用

iee colloquium on advances in neural networks for control and systems

作者： R. Murray-Smith K. Hunt F. Lohnert Systems Technology Research Daimler Benz Aerospace Berlin Germany

The learning algorithms and structures which have become popular in the neural network community over the last few years have been successfully applied to various challenging modelling problems. In contrast to the linear modelling methods, there is still no clearly defined engineering process from which a model can reliably be created from measurements taken from a physical system. Problems for the practical application of neural networks involve the interpretation of the trained models, and the explicit introduction of a priori models into the learning system, as well as the use, in many cases, only basic ad hoc validation and experiment design techniques. This talk will discuss several methods which can improve the engineering aspects of model identification with neural nets.< >

关键词： Learning systems neural networks

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Adaptive neurofuzzy systems for difficult modelling and control problems

Adaptive neurofuzzy systems for difficult modelling and cont...

引用

iee colloquium on advances in neural networks for control and systems

作者： M. Brown C.J. Harris Advanced Systems Research Group Department of Aeronautics & Astronautics University of Southampton Southampton UK

The ability to learn is the cornerstone of current neural technology. It was responsible for their decline in the late sixties when it was shown that the perceptron training algorithm could not be easily extended to multilayered networks, and also the revival of interest in these techniques was initiated by the discovery of a multilayer network adaptation rule. Artificial neural networks attempt to mimic a human's information processing capabilities by building neuronally inspired systems which learn to interact with their environment in a desirable fashion. There are many engineering problems which could benefit from the use such of such systems, although there are also problems with applying these adaptive networks. Most artificial neural networks can be regarded as "black box" learning systems; they are difficult initialise as knowledge is stored in an opaque fashion and validation can only performed using input/output data; their internal structure provides little information to an engineer. In addition, the generalisation, modelling and learning abilities of these networks are generally poorly understood, although such results are necessary when these adaptive systems are applied online in safety critical situations.< >

关键词： Adaptive systems Fuzzy neural networks Learning systems

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Adaptive neurocontrol of MIMO systems based on stability theory

Adaptive neurocontrol of MIMO systems based on stability the...

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iee colloquium on advances in neural networks for control and systems

作者： J.-M. Renders M. Saerens H. Bersini France Laboraioire lridia Université Libre de Bruxelles Brussels Belgium Lab. IRIDIA Univ. Libre de Bruxelles Belgium

In this paper we prove the stability of a certain class of nonlinear discrete MIMO systems controlled by a multilayer neural net with a simple weight adaptation strategy. The proof is based on the Lyapunov formalism. The stability statement is, however, only valid if the initial weight values are not too far from their optimal values that allow perfect model matching. We therefore propose to initialize the weights with values that solve the linear problem. This extends our previous work (Renders, 1993; Saerens, Renders and Bersini, 1993), where SISO systems were considered.< >

关键词： Adaptive control Lyapunov methods Multilayer perceptrons Multivariable systems Neurocontrollers Nonlinear systems Stability

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Fault identification in HVDC systems with neural networks

Fault identification in HVDC systems with neural networks

引用

Proceedings of the 2nd International Conference on advances in Power System control, Operation & Management

作者： Lai, L.L. Ndeh-Che, F. Chari, Tejedo City Univ United Kingdom

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. neural networks 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.

关键词： Electric fault location

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Exothermic heat estimation using fuzzy neural nets for a batch reactor temperature control system

Exothermic heat estimation using fuzzy neural nets for a bat...

引用

iee colloquium on advances in neural networks for control and systems

作者： E.S. Cubliar J.L. Coronado C.G. Moreno J. Manuel C. Izquierdo Dept. de Ingenieria de Sistemas y Autom. Valladolid Univ. Spain Department of Systems Engineering and Control School of Industrial Engineering University of Valladolid Valladolid Spain

Batch processes may constantly be changing, sometimes in unpredictable ways. For this reason, the operators need to keep a close watch on things. Batch-plant control stations have traditionally been located very close to the process, because operators can do a better job of sensing abnormalities if they can see, hear and smell what is going on. The reaction process can be considered as unstable, the instability being understood as the characteristic making the self abandoned system evolve towards unpermitted temperature values with the associated danger of accidents. The temperature also plays a decisive role in the final product quality, each time the reactor changes from the desired temperature the quality diminishes, the appearance of undesired species increases, and the overall performance of the reaction decreases which directly affects the economic performance.< >

关键词： Chemical industry Fuzzy neural networks Stability Temperature control

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Adaptive neural network control of the temperature in an oven

Adaptive neural network control of the temperature in an ove...

引用

iee colloquium on advances in neural networks for control and systems

作者： O. Dubois J. Nicolas A. Billat UFR Sciences Exactes et Naturelles L. A. M Reims France

Many authors have shown by simulated studies, that a great number of non-linear dynamical systems could be identified and controlled by using neural network models. The authors applied these results to a real process : an oven with two inputs, one for heating and one for cooling. The output to be controlled is the temperature inside the oven. The choice of the control strategy on the one hand and the choice of the neural network architecture for the oven identification on the other hand had to satisfy two main objectives. First, the control strategy should be quite insensible to random disturbances (air leaks, door openings, ...) and the neural model should be able to fit any modification of the plant dynamics during its lifetime (modification of the internal load alteration of the heating system ...). The authors chose to use the internal model control as their control strategy. They also used a radial basis function network to identify the plant. The process identification is composed of two phases, an off-line one and an online one. The off-line part consists first in training the network while determining its internal structure using an initial training set. The on-line phase is the adaptive part of the control scheme.< >

关键词： Adaptive control Feedforward neural networks Identification neural network architecture Process control Temperature control

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