NDLI: The classification of power system disturbance waveforms using a neural network approach

Content Provider	IEEE Xplore Digital Library
Author	Ghosh, A.K. Lubkeman, D.L.
Copyright Year	1994
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA (Ghosh, A.K.; Lubkeman, D.L.)
Abstract	Owing to the rise in power quality problems, the use of transient recorders to monitor power systems has increased steadily. The triggering strategies used by these transient recorders to capture disturbance waveforms are usually based on the violation of a set of predetermined measurement thresholds. Unfortunately, threshold based triggering strategies are difficult to apply in situations when only waveforms corresponding to a given class of disturbances need to be recorded. This inability of the reader to automatically discriminate between waveform types tends to burden the user with the task of manually sifting and sorting through a large number of captured waveforms. This paper describes an artificial neural network methodology for the classification of waveforms that are captured, as part of a larger scheme to automate the data collection process of recorders. Two different neural network paradigms are investigated: the more common feedforward network; and a modification of that, the time-delay network, which has the ability to encode temporal relationships found in the input data and exhibits a translation shift invariance property. Comparisons of both network paradigms, based on a typical distribution circuit configuration, are also presented.
Starting Page	323
Ending Page	329
File Size	627819
Page Count	7
File Format	PDF
ISBN	0780318838
DOI	10.1109/TDC.1994.328398
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1994-04-10
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Power systems Power system transients Power system measurements Artificial neural networks Power quality Monitoring Sorting Neural networks Feedforward neural networks Circuits
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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