Recognition method of voltage sag causes based on two‐dimensional transform and deep learning hybrid model
Author:
Affiliation:
1. The School of Control and Computer EngineeringNorth China Electric Power UniversityBeijing102206People's Republic of China
2. The Electric Power Research InstituteSMEPCShanghai200437People's Republic of China
Funder
State Grid Corporation of China
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/iet-pel.2019.0593
Reference37 articles.
1. IEEE Std 1564‐2014: ‘IEEE guide for voltage sag indices’ 2014
2. Power Quality Control of Smart Hybrid AC/DC Microgrids: An Overview
3. Research on classification of voltage sag sources based on recorded events;Wei P.F.;CIRED Open Access Proc. J.,2017
4. Interference source identification of voltage sag in distribution system based on wavelet transform;Wang K.X.;Proc. CSEE,2003
5. Power quality detection with classification enhancible wavelet‐probabilistic network in a power system;Lin C.;IEE Proc. Gener. Transm. Distrib.,2005
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