Symbolic dynamic filtering for online power quality anomaly detection

Saxena, K and Gurrala, G and Joseph, FC and Teja, BR (2020) Symbolic dynamic filtering for online power quality anomaly detection. In: IEEE Power and Energy Society General Meeting, 2-6 Aug. 2020, Montreal, QC, Canada.

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Abstract

A methodology for anomaly detection using Symbolic Dynamic Filtering (SDF) is proposed for detection of power quality events in this paper. The methodology overcomes the limitation of bulk data processing by compressing the signature feature information. It improves the sensitivity of early evolving anomaly detection in case of power quality events, which might not be evident by manual inspection. SDF is constructed based on the knowledge of symbolic encoding and finite state automata that generates signature patterns of histograms. Quantification of anomalous condition is done by comparing it with reference conditions, which depicts the deviation from normalcy. The proposed approach is validated on simulated data of various power quality events. The proposed SDF approach is found to be promising for online power quality event detection. © 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:	Data handling; Power quality, Feature information; Manual inspection; Power quality event; Reference condition; Symbolic Dynamic Filtering, Anomaly detection
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	03 Feb 2021 10:51
Last Modified:	03 Feb 2021 10:51
URI:	http://eprints.iisc.ac.in/id/eprint/67843

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