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000000077 001__ 77
000000077 037__ $$aENY-ARTICLE-2008-068
000000077 041__ $$aeng
000000077 088__ $$aI07/1999/I-402
000000077 100__ $$aCichocki, A
000000077 245__ $$aNeural networks for real-time estimation of parameters of signals in power systems
000000077 260__ $$c1997-11-20
000000077 300__ $$a12p
000000077 500__ $$aInternational Journal of Engineering Intelligent Systems for Electrical Engineering and Communications. 1998 vol. 6, nr 3, s. 131-140
000000077 520__ $$aThe purpose of this paper is to present new algorithms and along with them new architectures of analogue neuron-like adaptive processors for online estimation of parameters of sinusoidal signals, which are distorted by higher harmonics and corrupted by noise. For steady-state conditions we have developed neural networks which enable us to estimate the amplitudes and the frequency of the fundamental component of signals. When estimating the basic waveform of currents during short circuits the exponential DC component distorts the results. Assuming the known frequency, we have developed adaptive neural networks which enable us to estimate the amplitudes of the basic components as well as the amplitudes and the time constant of a DC component. The problem of estimation of signal parameters is formulated as an unconstrained optimization problem and solved by using the gradient descent continuous-time method. Basing on this approach we have developed systems of nonlinear differential equations that can be implemented by analog adaptive neural networks. The solution of the optimization problem bases on some principles given by Tank and Hopfield [ 4 ] as well as by Kennedy and Chua. The developed networks contain elements which are similar to the adaptive threshold elements of the perceptron presented by Widrow.
000000077 6531_ $$aneural networks
000000077 6531_ $$aestimation
000000077 6531_ $$asignal processing
000000077 6531_ $$aelectrical power systems
000000077 700__ $$aKostyla, P
000000077 700__ $$aLobos, T
000000077 700__ $$aWaclawek, Z
000000077 8560_ $$
000000077 8564_ $$u$$zAccess to Fulltext
000000077 909CO $$$$pglobal
000000077 980__ $$aARTICLE