Published Article
/  I07/1999/I402  ENYARTICLE2008068 
Neural networks for realtime estimation of parameters of signals in power systems
Cichocki, A ; Kostyla, P ; Lobos, T ; Waclawek, Z
19971120
Abstract: The purpose of this paper is to present new algorithms and along with them new architectures of analogue neuronlike adaptive processors for online estimation of parameters of sinusoidal signals, which are distorted by higher harmonics and corrupted by noise. For steadystate conditions we have developed neural networks which enable us to estimate the amplitudes and the frequency of the fundamental component of signals. When estimating the basic waveform of currents during short circuits the exponential DC component distorts the results. Assuming the known frequency, we have developed adaptive neural networks which enable us to estimate the amplitudes of the basic components as well as the amplitudes and the time constant of a DC component. The problem of estimation of signal parameters is formulated as an unconstrained optimization problem and solved by using the gradient descent continuoustime method. Basing on this approach we have developed systems of nonlinear differential equations that can be implemented by analog adaptive neural networks. The solution of the optimization problem bases on some principles given by Tank and Hopfield [ 4 ] as well as by Kennedy and Chua. The developed networks contain elements which are similar to the adaptive threshold elements of the perceptron presented by Widrow.
Keyword(s): neural networks ; estimation ; signal processing ; electrical power systems
Note: International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications. 1998 vol. 6, nr 3, s. 131140
Fulltext : http://zet10.ipee.pwr.wroc.pl/record/77/files/
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Record created 20080314, last modified 20080314
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