论文信息 - Application of fractals to PD signal recognition

Application of fractals to PD signal recognition

A fractal random model namely the fractional Brownian motion (FBM) is established to describe a single PD pulse. The fractal dimension ruler variable (FDRV) and fractal dimension (FD), as two features derived through the least-square fitting, are introduced to extract characteristics from the single PD pulse. Analysis of experimental results indicates that the fractal dimension ruler variable has a higher sensitivity compared with fractal dimension, and therefore can he used as an important practical feature to recognize PD signals.

E. Kuffel | Yuchang Qiu | Zhongyuan Zhao

[1] E. M. Lalitha,et al. Wavelet analysis for classification of multi-source PD patterns , 2000 .

[2] Graham Williams,et al. Conference on Electrical Insulation and Dielectric Phenomena , 1982, IEEE Transactions on Electrical Insulation.

[3] Alan S. Willsky,et al. The Modeling and Estimation of Statistically Self-Similar Processes in a Multiresolution Framework , 1999, IEEE Trans. Inf. Theory.

[4] Nicola Femia,et al. Fractal characteristics of electrical discharges : experiments and simulation , 1993 .

[5] G. Mazzanti,et al. Identification and separation of two concurrent partial discharge phenomena , 1996, Proceedings of Conference on Electrical Insulation and Dielectric Phenomena - CEIDP '96.

[6] Nelson Luis Saldanha da Fonseca,et al. Statistical multiplexing of self-similar sources , 2000, Globecom '00 - IEEE. Global Telecommunications Conference. Conference Record (Cat. No.00CH37137).

[7] E. Gulski,et al. Computer-aided recognition of discharge sources , 1992 .

[8] G. Montanari,et al. PD source recognition by Weibull processing of pulse height distributions , 2000 .

[9] E. M. Lalitha,et al. Fractal image compression for classification of PD sources , 1998 .