Abstract
The performance of the current sensor in power equipment may become worse affected by the environment. In this paper, based on ICA, we propose a method for on-line verification of the phase difference of the current sensor. However, not all source components are mutually independent in our application. In order to get an exact result, we have proposed a relative likelihood index to choose an optimal result from different runs. The index is based on the maximum likelihood evaluation theory and the independent subspace analysis. The feasibility of our method has been confirmed by experimental results.
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References
Hyvärinen, A., Karhunen, J., Oja, E.: Independent Component Analysis. John Wiley & Sons. Inc., New York (2001)
Cardoso, J.F.: On the performance of orthogonal source separation algorithms. In: Proc. EUSIPCO, Edinburgh, vol. 9, pp. 776–779 (1994)
Meinecke, F., Ziehe, A., Kawanabe, M., Müller, K.R.: A resampling approach to estimate the stability of one-dimensional or multidimensional independent components. IEEE Trans. Biomed. Eng. 49, 1514–1525 (2002)
Himberg, J., Hyvärinen, A.: Icasso: software for investigating the reliability of ICA estimates by clustering and visualization. In: Processing of the Workshop on Neural Networks and Signal Processing, Toulouse, France (2003)
Attias, H., Schreiner, C.E.: Blind source separation and deconvolution: The dynamic component analysis algorithm. Neural Computation 10, 1373–1424 (1998)
Cardoso, J.F.: lnfamax and maximum likelihood for source separation. IEEE Signal Processing Letters 4(4), 112–114 (1997)
Choi, S., Cichocki, A.: Correlation matching approach to source separation in the presence of spatially correlated noise. In: Proc. IEEE ISSPA., vol. 1, pp. 272–275 (2001)
Gribonval, R., Benaroya, L., Vincent, E., Févotte, C.: Proposals for performance measurement in source separation. In: 4th International Symposium on Independent Component Analysis and Blind Signal Sepration, Nara, Japan (April 2003)
Stögbauer, H., Kraskov, A., Astakhov, S.A., Grassberger, P.: Least-dependent-component analysis based on mutual information. Physical Review E 70 066123, 1–17 (2004)
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Ma, X., Lu, H. (2006). Application of ICA in On-Line Verification of the Phase Difference of the Current Sensor. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_54
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DOI: https://doi.org/10.1007/11893295_54
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
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