Neural networks for real-time estimation of the basic waveforms of voltages and currents encountered in power systems
Cichocki, A (Warsaw University of Technology, Poland) ; Lobos, T (Wroclaw University of Technology, Poland)
/ ||I07/1993/I-141 ||ENY-ARTICLE-2008-116|
Abstract: An electronic artificial neural network architecture is presented for estimating the parameters of a sine wave distorted by a DC exponential signal and corrupted by noise. In the proposed approach a finite set or sampled data from noisy measurements is used to implement Lp-norm. The standard least-squares (L2 -norm) criterion is considered as a special case. Mathematical algorithms are presented in detail and associated architectures of analogue artificial neural networks are proposed. Extensive computer simulations are used to demonstrate the validity and performance of the proposed algorithms and neural network realizations. The proposed methods seems to be particularly useful for real-time, high-sped and low-cost estimations of parameters of sinusoidal signals.
Keyword(s): neural network ; distorted signals ; parameter estimation ; signal processing
Note: International Journal of Electronics. 1993 vol. 74, No 2, pp. 307-318
Fulltext : http://zet10.ipee.pwr.wroc.pl/record/151/files/
Cited by: try citation search for ENY-ARTICLE-2008-116; I07/1993/I-141
Record created 2008-03-30, last modified 2008-03-31
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