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Feature extraction using symbolic dynamic filtering for fault analysis in distribution systems

Saxena, K and Gurrala, G (2020) Feature extraction using symbolic dynamic filtering for fault analysis in distribution systems. In: IEEE Power and Energy Society General Meeting, 2-6 August 2020, Montreal; Canada.

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Official URL: https://dx.doi.org/10.1109/PESGM41954.2020.9282090


Feature extraction for fault detection and classification using Symbolic Dynamic Filtering (SDF) is explored in this paper. It provides an edge over existing methodologies by compressing voluminous waveform data into probability histograms which describe signature features of the faults. A SDF is constructed based on the knowledge of symbolic encoding and finite state automata to generate signature histogram patterns from different fault categories. These histogram patterns are used for training various pattern classifiers to build the fault classification model. The simulation results on a test distribution network showcase the model accuracy for fault prediction with k-nearest neighbour (kNN) and support vector machines. © 2020 IEEE.

Item Type: Conference Paper
Publication: IEEE Power and Energy Society General Meeting
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 2020 IEEE Power and Energy Society General Meeting, PESGM 2020 ; Conference Date: 2 August 2020 Through 6 August 2020; Conference Code:165854
Keywords: Extraction; Fault detection; Graphic methods; Nearest neighbor search; Support vector machines, Distribution systems; Fault classification; Fault detection and classification; Fault prediction; K nearest neighbours (k-NN); Pattern classifier; Probability histograms; Symbolic Dynamic Filtering, Feature extraction
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 03 Feb 2021 10:54
Last Modified: 03 Feb 2021 10:55
URI: http://eprints.iisc.ac.in/id/eprint/67842

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