Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/61786
Title: An adaptive neural network approach for the estimation of power system frequency
Authors: Dash, P.K.
Swain, D.P.
Routray, A.
Liew, A.C. 
Keywords: Adaline
Linear combiner
LMS algorithm
Power system frequency
Issue Date: Jun-1997
Source: Dash, P.K.,Swain, D.P.,Routray, A.,Liew, A.C. (1997-06). An adaptive neural network approach for the estimation of power system frequency. Electric Power Systems Research 41 (3) : 203-210. ScholarBank@NUS Repository.
Abstract: A new approach to the estimation of power system frequency using an adaptive neural network is presented in this paper. This approach uses a linear adaptive neuron or an adaptive linear combiner called 'Adaline' to identify the parameters of a discrete signal model of the power system voltage. Here, the learning parameters are adjusted to force the error between the actual and the computed signal samples to satisfy a stable difference error equation, rather than to minimize an error function. The proposed algorithm shows a high degree of robustness and estimation accuracy over a wide range of frequency changes. The technique is shown to be capable of tracking power system conditions and is immune to the effects of harmonics and random noise. © 1997 Elsevier Science S.A.
Source Title: Electric Power Systems Research
URI: http://scholarbank.nus.edu.sg/handle/10635/61786
ISSN: 03787796
Appears in Collections:Staff Publications

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