TY  - CONF
AV  - none
SP  - 327
PB  - IEEE Computer Society
EP  - 332
CY  - Melaka
N1  - cited By 10; Conference of 2013 3rd IEEE International Conference on Signal and Image Processing Applications, IEEE ICSIPA 2013 ; Conference Date: 8 October 2013 Through 10 October 2013; Conference Code:102487
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-84894202039&doi=10.1109%2fICSIPA.2013.6708027&partnerID=40&md5=e6312f99133f7a4cab9ef482d4fa8373
A1  - Alhaj, H.M.M.
A1  - Nor, N.M.
A1  - Asirvadam, V.S.
A1  - Abdullah, M.F.
ID  - scholars3879
Y1  - 2013///
KW  - Algorithms; Complex networks; Fast Fourier transforms; Harmonic analysis; Image processing; Power electronics; Signal to noise ratio
KW  -  Harmonic components; Harmonics component; Least mean square algorithms; Power system harmonics; Power system networks; Power system signal; Sliding window methods; Sliding window-based
KW  -  Estimation
TI  - Power system harmonics estimation using sliding window based LMS
SN  - 9781479902675
N2  - The widespread use of power electronics devises and nonlinear loads in power system grids is increasing in the last decades leads to rise of harmonic in power system signals. Great damage to power system gird can happen due to harmonics. Thus it is important to precisely estimate the harmonics components that may help to avoid its harmful effect of the electrical grid performance. The more common algorithm that has been used to estimate the harmonic component is the Fast Fourier Transform (FFT), however FFT has few limitations, furthermore, modern power system network getting complex and noisy. Therefore, fast and accurate harmonic estimation in the presence of noise is needed. Sliding window based least mean square (LMS) algorithm is introduced in this paper to estimate the harmonic components in noisy environment. The result shows that the sliding window method able to give a good estimation to the harmonic component even when the signal to noise ratio (SNR) is 0 dB. © 2013 IEEE.
ER  -