Abstract
This paper describes a wavelet entropy-based simple method for classification of transmission line faults using wavelet entropy analysis of sending end fault current waveforms of one cycle post-fault duration. The fault transients are scaled with respect to the peak value under no-fault condition for respective phases. These three phase scaled current signals are fed to the wavelet classifier model to extract fault features in terms of wavelet entropy values. The variation in the three phase entropy for ten fault classes provides enough features for distinct differentiation among different fault conditions. Two threshold values are identified on detail analysis of the fault class entropies, which helps to develop fault classifier rule base, and in turn, fault signatures. The unknown class is identified by direct comparison of the three phase test entropies with that of fault class signatures. The proposed classifier produces 99.2857% accuracy in classification with one cycle post-fault data.
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Mukherjee, A., Kundu, P.K., Das, A. (2021). A Wavelet Entropy-Based Power System Fault Classification for Long Transmission Lines. In: Hassanien, A.E., Bhattacharyya, S., Chakrabati, S., Bhattacharya, A., Dutta, S. (eds) Emerging Technologies in Data Mining and Information Security. Advances in Intelligent Systems and Computing, vol 1300. Springer, Singapore. https://doi.org/10.1007/978-981-33-4367-2_13
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DOI: https://doi.org/10.1007/978-981-33-4367-2_13
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