Abstract
This paper intends to present an adaptive algorithm for estimating the frequency, amplitude, and phase of a sinusoid under non stationary condition present in time-varying power signals. The proposed algorithm estimates precisely the frequency variation, phase variation, and the amplitude and shows accuracy in estimation even in the presence of harmonic and inter harmonic as noise. This method uses Taylor series expansion of the signal to cope with the sudden changes. Then a modified ADALINE is used because of its low computational complexity, for which it can be implemented in real time. The performance of the proposed algorithm has been extensively tested and demonstrated.
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References
Yong-mei, S., Xiao-xia, D., Wei, Z., Yuan-yuan, K.: Novel variable step-size adaptive LMS time delay estimation algorithm with nonlinear preprocess In: 4th International Congress on Image and Signal Processing, pp. 2767–2770 (2011)
So, H.C.: A comparative study of three recursive least squares algorithms for single-tone frequency tracking. Signal Process. 83(9), 2059–2062 (2003)
Chen, C.I., Chang, G.W., Hong, R.C., Lee, H.M.: Extended real model of Kalman filter for time varying harmonics estimation. IEEE Trans. Power Deliv. 25(1), 17–26 (2010)
Niedzwickei, M., Kaczmarek, P.: Tracking analysis of a generalized Notch filters. IEEE Trans. Signal Process. 54(1), 304–314 (2006)
Karimi-Ghartemani, M., Iravani, M.R.: Measurement of harmonics /inter- harmonics of time varying frequencies. IEEE, Trans-Power Del. 20(1), 23–31 (2005)
Yu, C.S.: A discrete Fourier transform based adaptive mimic phasor estimator for distance relaying application. IEEE Trans. Power Deliv. 21(4), 1836–1846 (2006)
Phadke, A.G., Thorp, J., Adamiak, M.: A new measurement technique for tracking voltage phasors, local system frequency and rate of change of frequency. IEEE Trans. Power App. Syst. PAS-102(5), 1025–1038 (1983)
Bose, B.K.: Neural network applications in power electronics and motor drives—an introduction and perspective. IEEE Trans. Ind. Electron. 54(1), 14–33 (2007)
Bertoluzzo, M., Buja, G.S., Castellan, S., Fiorentin, P.: Neural network technique for the joint time–frequency analysis of distorted signal. IEEE Trans. Ind. Electron. 50(6), 1109–1115 (2003)
Ren, J., Kezunovic, M.: An improved fourier method for power system frequency estimation. In: NorthAmerican Power Symposium (NAPS), Boston, MA, pp.1–6, 4−6 August 2011
Li, T.-T., Shi, M., Yi, Q.-M.: An improved variable step-size LMS algorithm. In: 7th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM), Wuhan, pp. 1–4, 23−25 September 2011
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Nanda, S., Hasan, S., Swain, B.K., Dash, P.K. (2015). Improved ADALINE Based Algorithm for Power System Frequency Estimation. In: Panigrahi, B., Suganthan, P., Das, S. (eds) Swarm, Evolutionary, and Memetic Computing. SEMCCO 2014. Lecture Notes in Computer Science(), vol 8947. Springer, Cham. https://doi.org/10.1007/978-3-319-20294-5_74
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DOI: https://doi.org/10.1007/978-3-319-20294-5_74
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