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Fast estimation of the non-stationary amplitude of a harmonically distorted signal using a Kalman filter

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EN
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EN
In this paper we introduce a self-tuning Kalman filter for fast time-domain amplitude estimation of noisy harmonic signals with non-stationary amplitude and harmonic distortion, which is the problem of a contactvoltage measurement to which we apply the proposed method. The research method is based on the self-tuning of the Kalman filter's dropping-off behavior. The optimal performance (in terms of accuracy and fast response) is achieved by detecting the jump of the amplitude based on statistical tests of the innovation vector of the Kalman filter and reacting to this jump by adjusting the values of the covariance matrix of the state vector. The method's optimal configuration of the parameters was chosen using a statistical power analysis. Experimental results show that the proposed method outperforms competing methods in terms of speed and accuracy of the jump detection and amplitude estimation.
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27--42
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. Bibliogr. 23 poz., rys., tab., wykr.
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Bibliografia
  • [1] Metrel (2008). Guide for testing and verification of low voltage installations.
  • [2] Lušin, T., Agrež, D. (2011). Estimation of the Amplitude Square Using the Interpolated Discrete Fourier Transform. Metrol. Meas. Syst., 18(4), 583-596.
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  • [6] Stoica, P., Li, H., Li, J. (2000). Amplitude estimation of sinusoidal signals: survey, new results, and an application. IEEE Transactions on Signal Processing, 48, 338-352.
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  • [8] Caciotta, M., Carbone, P. (1996). Estimation of non-stationary sinewave amplitude via competitive Kalman filtering. Instrumentation and Measurement Technology Conference, IMTC-96. Conference Proc., Quality Measurements: The Indispensable Bridge between Theory and Reality, IEEE.
  • [9] Dash, P., Jena, R., Panda, G., Routray, A. (2000). An extended complex Kalman filter for frequency measurement of distorted signals. IEEE Transactions on Instrumentation and Measurement, 49, 746-753.
  • [10] Chen, C., Chang, G., Hong, R., Li, H. (2000). Extended real model of Kalman filter for time-varying harmonics estimation. IEEE Transactions on Power Delivery, 25, 17-26.
  • [11] Liu, S. (1998). An adaptive Kalman filter for dynamic estimation of harmonic signals. In Proc. of 8th International Conference on Harmonics And Quality of Power, 2, 636-640.
  • [12] Metrel (2006). Measurements on electric installations in theory and practice.
  • [13] Electrical safety in low voltage distribution systems up to 1000 V a.c. and 1500 V d.c. - Equipment for testing, measuring or monitoring of protective measures - Part 3: Loop impedance (2007). IEC Std. 61557-3.
  • [14] Chen, C.I., Chang, G.W., Hong, R.C., Li., H.M. (2010). Extended real model of Kalman filter for time-varying harmonics estimation. IEEE Transactions on Power Delivery, 25(1), 17-26.
  • [15] Yu, K., Watson, N., Arrillaga, J. (2005). An adaptive Kalman filter for dynamic harmonic state estimation and harmonic injection tracking. IEEE Transactions on Power Delivery, 20, 1577-1584.
  • [16] Macias, J., Exposito, A. (2006). Self-tuning of Kalman filters for harmonic computation. IEEE Transactions on Power Delivery, 21, 501-503.
  • [17] Wang, J. (2008). Test Statistics in Kalman Filtering. Journal of Global Positioning Systems, 7, 81-90.
  • [18] Miller, R.G.J. (1981). Simultaneous Statistical Inference. 2nd ed., Springer Series in Statistics. Springer.
  • [19] Cohen, J. (1988). Statistical Power Analysis for the Behavioral Sciences. 2nd ed., Routledge Academic.
  • [20] Lehmann, E.L., Romano, J.P. (2010). Testing Statistical Hypotheses. Springer Texts in Statistics, Springer.
  • [21] Soong, T. (2004). Fundamentals of probability and statistics for engineers. Wiley.
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  • [23] Offelli, C., Petri, D. (1992). The influence of windowing on the accuracy of multifrequency signal parameter estimation. IEEE Transactions on Instrumentation and Measurement, 41, 256-261.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-BSW1-0113-0003
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