Improved ADALINE Based Algorithm for Power System Frequency Estimation

Nanda, Sarita; Hasan, Shazia; Swain, Biswaranjan; Dash, P.K.

doi:10.1007/978-3-319-20294-5_74

Cited by 1 publication

(1 citation statement)

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“…But these methods fail when the signal is corrupted by noise. Many methods based on Fourier transform like Discrete Fourier Transform(DFT), Fast Fourier Transform(FFT) [3][4][5] could overcome this problem but show leakage, aliasing and picket fence effects Several other techniques like Gauss Newton algorithm [6][7], Least error squares [8][9][10][11], Adalines [12][13], Kalman Filters [14][15] have been used but even they suffer from large computational cost and show inefficiency in low SNR conditions. .Methods based on intelligent techniques like genetic algorithm [16] and neural network [17] have been also applied in this area.…”

Section: Introductionmentioning

confidence: 99%

A fast hybrid adaptive filter for parameter estimation of non stationary sinusoid under noise

Nanda

Hasan

Pujari

et al. 2015

2015 IEEE Power, Communication and Information Technology Conference (PCITC)

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This paper presents a modified combined approach using Taylor series expansion and Extended Kalman filter for accurate estimation of parameters and harmonic components of a time-varying signal embedded in noise with low signal-to-noise ratio. The signal is modelled using a dynamic model with time varying parameters. The power signal with a changing envelope has been expressed using a second order Taylor expansion. EKF algorithm is used for computing the parameters of such signal model. The algorithm using a linear model approach is presented which also reduces the computational complexity. Moreover this approach is also immune to noise, harmonic contaminations and also shows better convergence properties.

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