Application of Poincaré analogous time-split signal-based statistical correlation for transmission line fault classification

A transmission line fault classification scheme is proposed in this article using Poincaré-based correlation analysis of three-phase fault currents. The method segments each fault signal into two equal time-split components and computes correlation coefficient between these two time-split signals. The fault current signals of the directly affected line(s) observe an abrupt monotonic rise, compared to the indirectly affected phases. This sudden rise in magnitude is expressed with correlation coefficients between the two almost consecutive time-split components of signal, time shifted by delay index. This method emphasizes this monotonic nature of increment of the fault current, enabling prompt fault detection. Further analysis of three phases of fault signals independently yields a set of correlation coefficients for ten different fault prototypes, which are used to develop fault classifier signatures for direct classification. The proposed method yields high classification accuracy of 99.76% using only (1/6)th of the post-fault noisy signal with fault resistance varying from 0.01 to 100Ω. Besides, analysis of only one end single discards the requirement of time synchronous signal acquisition from both ends. Finally, use of simple analysis reduces the computational burden compared to several contemporary methods.