Real‐time recognition of power quality disturbance‐based deep belief network using embedded parallel computing platform
Author:
Affiliation:
1. School of Electric Power EngineeringSouth China University of Technology Guangzhou GuangDong 510006 China
Publisher
Wiley
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1002/tee.23084
Reference21 articles.
1. KhokharS ZinAAM MokhtarAS IsmailNAM ZareenN. Automatic classification of power quality disturbances: A review.IEEE Student Conf. Research and Developement 2013 1; 427–432.
2. IEEE Recommended Practice for Monitoring Electric Power Quality.IEEE Std 1159–2009 (Revision of IEEE Std 1159–1995).2009 01;01–81.
3. Automatic classification of power quality events and disturbances using wavelet transform and support vector machines
4. Techniques and methodologies for power quality analysis and disturbances classification in power systems: a review
5. Feature Extraction and Power Quality Disturbances Classification Using Smart Meters Signals
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