A real-time algorithm for the harmonic estimation and frequency tracking of dominant components in fusion plasma magnetic diagnostics.

The real-time tracking of instantaneous quantities such as frequency, amplitude, and phase of components immerse in noisy signals has been a common problem in many scientific and engineering fields such as power systems and delivery, telecommunications, and acoustics for the past decades. In magnetically confined fusion research, extracting this sort of information from magnetic signals can be of valuable assistance in, for instance, feedback control of detrimental magnetohydrodynamic modes and disruption avoidance mechanisms by monitoring instability growth or anticipating mode-locking events. This work is focused on nonlinear Kalman filter based methods for tackling this problem. Similar methods have already proven their merits and have been successfully employed in this scientific domain in applications such as amplitude demodulation for the motional Stark effect diagnostic. In the course of this work, three approaches are described, compared, and discussed using magnetic signals from the Joint European Torus tokamak plasma discharges for benchmarking purposes.

[1]  G. T. Heydt,et al.  Dynamic state estimation of power system harmonics using Kalman filter methodology , 1991 .

[2]  N Hawkes,et al.  Real-time data processing and magnetic field pitch angle estimation of the JET motional Stark effect diagnostic based on Kalman filtering. , 2009, The Review of scientific instruments.

[3]  D. A. Humphreys,et al.  Model-based dynamic resistive wall mode identification and feedback control in the DIII-D tokamak , 2006 .

[4]  Michael Athans,et al.  A comparison of three non-linear filters , 1969, Autom..

[5]  A. C. A. Figueiredo,et al.  Time–frequency analysis of non-stationary signals in fusion plasmas using the Choi–Williams distribution , 2004 .

[6]  Garry A. Einicke,et al.  Robust extended Kalman filtering , 1999, IEEE Trans. Signal Process..

[7]  R J Buttery,et al.  Complete stabilization of neoclassical tearing modes with lower hybrid current drive on COMPASS-D. RF teams. , 2000, Physical review letters.

[8]  Haihong Wang,et al.  Power system harmonic signal estimation and retrieval for active power filter applications , 1994 .

[9]  D. Alves,et al.  Real-time estimation of the poloidal wavenumber of ISTTOK tokamak magnetic fluctuations. , 2008, The Review of scientific instruments.

[10]  V. M. Moreno Saiz,et al.  Application of Kalman filtering for continuous real-time tracking of power system harmonics , 1997 .

[11]  Eugenio Schuster,et al.  Equilibrium reconstruction improvement via Kalman-filter-based vessel current estimation at DIII-D , 2007 .

[12]  Guanrong Chen,et al.  Kalman Filtering with Real-time Applications , 1987 .

[13]  Barbara F. La Scala,et al.  Design of an extended Kalman filter frequency tracker , 1996, IEEE Trans. Signal Process..

[14]  Dan Simon,et al.  Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches , 2006 .

[15]  A Murari,et al.  A Real-Time Synchronous Detector for the TAE Antenna Diagnostic at JET , 2010, IEEE Transactions on Nuclear Science.

[16]  R. Coelho,et al.  An Adaptive Algorithm for Real-Time Multi-Tone Estimation and Frequency Tracking of Non-Stationary Signals , 2011, IEEE Transactions on Nuclear Science.

[17]  Rudolph van der Merwe,et al.  The unscented Kalman filter for nonlinear estimation , 2000, Proceedings of the IEEE 2000 Adaptive Systems for Signal Processing, Communications, and Control Symposium (Cat. No.00EX373).

[18]  N. Commaux,et al.  Disruption-Mitigation-Technology Concepts and Implications for ITER , 2009, IEEE Transactions on Plasma Science.

[19]  R. Sartori,et al.  Edge localized modes: recent experimental findings and related issues , 2007 .

[20]  G. Gantenbein,et al.  Control of MHD instabilities by ECCD: ASDEX Upgrade results and implications for ITER , 2007 .

[21]  Fernando D. Nunes,et al.  Recursive algorithm for fast evaluation of the Abel inversion integral in broadband reflectometry , 1999 .

[22]  K. Xiong,et al.  Adaptive robust extended Kalman filter for nonlinear stochastic systems , 2008 .

[23]  James V. Candy,et al.  Adaptive and Learning Systems for Signal Processing, Communications, and Control , 2006 .

[24]  Sergio M. Savaresi,et al.  On the parametrization and design of an extended Kalman filter frequency tracker , 2000, IEEE Trans. Autom. Control..

[25]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[26]  Joris De Schutter,et al.  Kalman filters for nonlinear systems , 2002 .

[27]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[28]  R. Coelho,et al.  Real-Time Lock-In Amplifier Implementation Using a Kalman Filter for Quasi-Periodic Signal Processing in Fusion Plasma Diagnostics , 2009, IEEE Transactions on Plasma Science.

[29]  James V. Candy,et al.  Bayesian Signal Processing , 2009 .

[30]  Jet Efda Contributors,et al.  Study of the spectral properties of ELM precursors by means of wavelets , 2008 .

[31]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.