Deep learning techniques for transmission line fault classification – A comparative study
Author:
Publisher
Elsevier BV
Subject
General Engineering
Reference36 articles.
1. An intelligent fault detection and classification scheme for distribution lines integrated with distributed generators;Chaitanya;Comput Electr Eng,2018
2. Manohar M, Koley E. SVM based protection scheme for microgrid. In: 2017 int conf intelligent computing instrumentation and control technologies ICICICT 2017; 2018.
3. Tokel HA, Al HR, Alirezaei G, Mathar R. A new approach for machine learning-based fault detection and classification in power systems. In: 2018 IEEE power energy society of innovative smart grid technology conference ISGT 2018; 2018.
4. A new approach for fault classification in microgrids using optimal wavelet functions matching pursuit;Abdelgayed;IEEE Trans Smart Grid,2018
5. Measurement and assessment of power quality characteristics for photovoltaic systems: harmonics, flicker, unbalance, and slow voltage variations;Ortega;Electrical Power Syst Res,2013
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