A signal processing adaptive algorithm for nonstationary power signal parameter estimation

SUMMARY This paper presents the design and analysis of an adaptive algorithm for tracking the amplitude, phase and frequency of the fundamental, harmonics and interharmonics present in time-varying power sinusoid in white noise. If frequency, amplitude and phase of the multiple sinusoids become nonstationary, they are estimated as an unconstrained optimization problem using robust and low complexity multi-objective Gauss–Newton algorithm. The presented algorithm deals with frequency drift and can accurately estimate frequency variation, amplitude and phase variation, as well as harmonic amplitude and phase variations. Further, the learning parameters in the proposed algorithm are tuned iteratively to provide faster convergence and better accuracy. The excellent tracking capability of proposed multi-objective Gauss–Newton algorithm is shown through simulation and experimental results in a nonstationary environment. Copyright © 2012 John Wiley & Sons, Ltd.

[1]  Darlan A. Fernandes,et al.  Harmonic Analysis Based on Kalman Filtering and Prony's Method , 2007, 2007 International Conference on Power Engineering, Energy and Electrical Drives.

[2]  H. Lin.  Intelligent Neural Network-Based Fast Power System Harmonic Detection , 2007, IEEE Transactions on Industrial Electronics.

[3]  A. Bakhshai,et al.  Processing of Harmonics and Interharmonics Using an Adaptive Notch Filter , 2010, IEEE Transactions on Power Delivery.

[4]  Francisco A. S. Neves,et al.  A Method for Extracting the Fundamental-Frequency Positive-Sequence Voltage Vector Based on Simple Mathematical Transformations , 2008, IEEE Transactions on Industrial Electronics.

[5]  Vladimir V. Terzija,et al.  Improved recursive Newton-type algorithm for frequency and spectra estimation in power systems , 2003, IEEE Trans. Instrum. Meas..

[6]  Om P. Malik,et al.  Detection and classification of power quality disturbances in noisy conditions , 2003 .

[7]  A. Testa,et al.  Desynchronized Processing technique for Harmonic and interharmonic analysis , 2004, IEEE Transactions on Power Delivery.

[8]  K. C. Ho,et al.  Linear prediction approach for efficient frequency estimation of multiple real sinusoids: algorithms and analyses , 2005, IEEE Transactions on Signal Processing.

[9]  Davood Yazdani,et al.  A Real-Time Extraction of Harmonic and Reactive Current in a Nonlinear Load for Grid-Connected Converters , 2009, IEEE Transactions on Industrial Electronics.

[10]  Adly Girgis,et al.  A Quantitative Study of Pitfalls in the FFT , 1980, IEEE Transactions on Aerospace and Electronic Systems.

[11]  A. Testa,et al.  On the Processing of harmonics and interharmonics: using Hanning window in standard framework , 2004, IEEE Transactions on Power Delivery.

[12]  Aurobinda Routray,et al.  A novel Kalman filter for frequency estimation of distorted signals in power systems , 2002, IEEE Trans. Instrum. Meas..

[13]  Simon X. Yang,et al.  Power system frequency estimation using supervised Gauss–Newton algorithm , 2009 .

[14]  Jun Zheng,et al.  Two Simplified Recursive Gauss–Newton Algorithms for Direct Amplitude and Phase Tracking of a Real Sinusoid , 2007, IEEE Signal Processing Letters.

[15]  Gary W. Chang,et al.  A two-stage ADALINE for harmonics and interharmonics measurement , 2010, 2010 5th IEEE Conference on Industrial Electronics and Applications.

[16]  Maciej Niedzwiecki,et al.  Tracking analysis of a generalized adaptive notch filter , 2006, IEEE Transactions on Signal Processing.

[17]  Sukumar Mishra,et al.  Hybrid least-square adaptive bacterial foraging strategy for harmonic estimation , 2005 .

[18]  R. Zhao,et al.  Interharmonics Analysis Based on Interpolating Windowed FFT Algorithm , 2007, IEEE Transactions on Power Delivery.

[19]  Hsiung-Cheng Lin.  Fast Tracking of Time-Varying Power System Frequency and Harmonics Using Iterative-Loop Approaching Algorithm , 2007, IEEE Transactions on Industrial Electronics.

[20]  Mahmood Joorabian,et al.  Harmonic estimation in a power system using a novel hybrid Least Squares-Adaline algorithm , 2009 .

[21]  Jian Yang,et al.  Robust Modified Newton Algorithm for Adaptive Frequency Estimation , 2007, IEEE Signal Processing Letters.