Towards accurate estimation of fast varying frequency in future electricity networks: The transition from model-free methods to model-based approach

Accurate estimation of fast varying fundamental frequency in the presence of harmonics and noise will be required for effective frequency regulation in future electricity networks with high penetration level of renewable energy sources. Two new algorithms for network frequency tracking are proposed. The first algorithm represents a robust modification of classical zero crossing method, which is widely used in industry. The second algorithm is a multiple model algorithm based on the systems with harmonic regressor. Algorithm allows complete reconstruction of the frequency content of the signal, using information about the upper bound of the number of harmonics only. Moreover, new family of high-order algorithms together with new stepwise splitting method are proposed for parameter calculation in systems with harmonic regressor for the accuracy improvement. Statistical methods are introduced for comparison of two new algorithms to classical zero crossing algorithm. The modified algorithm provides significant improvement compared to the classical algorithm, and the algorithm with harmonic regressor provides further improvement of the statistical performance indexes with respect to the modified algorithm.

[1]  R. Orglmeister,et al.  QRS Detection Using Zero Crossing Counts , 2003 .

[2]  Chul-Won Park,et al.  A Comparative Study on Frequency Estimation Methods , 2013 .

[3]  M. Benzi.  Preconditioning techniques for large linear systems: a survey , 2002 .

[4]  Alexander Stotsky,et al.  Frequency Determination in Control Applications: Excitation Based Approach , 2012, ROCOND.

[5]  K. Chen,et al.  Matrix preconditioning techniques and applications , 2005 .

[6]  Milenio B. Duric,et al.  Combined Fourier and Zero Crossing Technique for Frequency Measurement in Power Networks in the Presence of Harmonics , 2005 .

[7]  A. M. Walker.  On the estimation of a harmonic component in a time series with stationary independent residuals , 1971 .

[8]  Denis V. Efimov,et al.  Switched Algorithm for Frequency Estimation with Noise Rejection , 2012, IEEE Transactions on Automatic Control.

[9]  G. Amdhal,et al.  Validity of the single processor approach to achieving large scale computing capabilities , 1967, AFIPS '67 (Spring).

[10]  Barry G. Quinn,et al.  The Estimation and Tracking of Frequency , 2001 .

[11]  Rajeev Thakur,et al.  An Efficient Approach to Zero Crossing Detection Based On Opto-Coupler , 2013 .

[12]  X. Xia.  Global frequency estimation using adaptive identifiers , 2002, IEEE Trans. Autom. Control..

[13]  Alexander Stotsky,et al.  Accuracy improvement in Least-Squares estimation with harmonic regressor: New preconditioning and correction methods , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[14]  Roberto Galeazzi,et al.  Experimental verification of a global exponential stable nonlinear wave encounter frequency estimator , 2015 .

[15]  E. Jacobsen,et al.  Fast, Accurate Frequency Estimators [DSP Tips & Tricks] , 2007, IEEE Signal Processing Magazine.

[16]  Peter J. Kootsookos,et al.  Fast, Accurate Frequency Estimators , 2007 .

[17]  R. Wall.  Simple methods for detecting zero crossing , 2003, IECON'03. 29th Annual Conference of the IEEE Industrial Electronics Society (IEEE Cat. No.03CH37468).

[18]  Thomas Parisini,et al.  Robust Sinusoid Identification With Structured and Unstructured Measurement Uncertainties , 2014, IEEE Transactions on Automatic Control.

[19]  Alexander Stotsky,et al.  Combined high-order algorithms in robust least-squares estimation with harmonic regressor and strictly diagonally dominant information matrix , 2015, J. Syst. Control. Eng..

[20]  Vladimir Friedman,et al.  A zero crossing algorithm for the estimation of the frequency of a single sinusoid in white noise , 1994, IEEE Trans. Signal Process..

[21]  Ta-Hsin Li,et al.  A fast algorithm for efficient estimation of frequencies , 1998, 9th European Signal Processing Conference (EUSIPCO 1998).

[22]  L. Angrisani,et al.  An efficient extension of the zero-crossing technique to measure frequency of noisy signals , 2012, 2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings.

[23]  A. Stotsky,et al.  Recursive spline interpolation method for real time engine control applications , 2003, 2003 European Control Conference (ECC).

[24]  J. Kuntzmann,et al.  Solutions Numeriques des Equations Algebriques , 1963 .

[25]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.