NDLI: A combinatorial approach based on wavelet transform and Hidden Markov Models in differential relaying of power transformers

Content Provider	IEEE Xplore Digital Library
Author	Jazebi, S. Vahidi, B. Hosseinian, S.H. Shafiee, M. Naghizadeh, R.A.
Copyright Year	2008
Description	Author affiliation: Amirkabir Univ. of Technol., Tehran (Jazebi, S.; Vahidi, B.; Hosseinian, S.H.; Shafiee, M.; Naghizadeh, R.A.)
Abstract	This paper proposed a new classification method based on hidden Markov models (HMM) to discriminate between magnetizing inrush current and internal faults in transformers. HMM is a pattern recognizer which can classify signals based on waveform and characteristics. Training data-set is achieved by application of k-means clustering approach. First some principle characteristic of the signals is extracted to reduce the computational time of HMM training procedure and enhance discrimination accuracy, and then two HMM blocks is trained for these two kinds of signals. This procedure makes the scheme insensitive to irrelevant disturbances such as CT saturation. Based on the proposed algorithm a high speed differential relaying could be performed in a quarter of a cycle. Small computation requirement is the advantage of this method which can be applied for real time applications. The suitable performance of this method is demonstrated by simulation of different faults and switching conditions on a power transformer. Since the discrimination method is done with probabilistic characteristics of signals without application of any deterministic index, more reliable and accurate classification is achieved. It provides a high operating sensitivity for internal faults and remains stable for inrush currents of the power transformers.
Starting Page	1
Ending Page	7
File Size	276174
Page Count	7
File Format	PDF
ISBN	9781424432943
DOI	10.1109/UPEC.2008.4651503
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2008-09-01
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	University of Padova
Subject Keyword	Training Computational modeling Hidden Markov models Transforms Feature extraction Power transformers Circuit faults
Content Type	Text
Resource Type	Article

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