Signal-piloted processing and machine learning based efficient power quality disturbances recognitio...
Signal-piloted processing and machine learning based efficient power quality disturbances recognition
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Publisher
San Francisco: Public Library of Science
Journal title
Language
English
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Publisher
San Francisco: Public Library of Science
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Scope and Contents
Contents
Significant losses can occur for various smart grid stake holders due to the Power Quality Disturbances (PQDs). Therefore, it is necessary to correctly recognize and timely mitigate the PQDs. In this context, an emerging trend is the development of machine learning assisted PQDs management. Based on the conventional processing theory, the existing...
Alternative Titles
Full title
Signal-piloted processing and machine learning based efficient power quality disturbances recognition
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TN_cdi_plos_journals_2533695893
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2533695893
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ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0252104
