An EKF‐SVM machine learning‐based approach for fault detection and classification in three‐phase power transformers
Author:
Affiliation:
1. Department of Control and Power Engineering Shiraz University Shiraz Iran
2. Department of Communication and Electronic Engineering Shiraz University Shiraz Iran
Publisher
Institution of Engineering and Technology (IET)
Subject
Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics
Link
https://onlinelibrary.wiley.com/doi/pdf/10.1049/smt2.12015
Reference33 articles.
1. Feature extraction of power transformer vibration signals based on empirical wavelet transform and multiscale entropy;Zhao M.;IET Sci. Meas. Technol.,2017
2. Power transformer differential protection based on least squares algorithm with extended kernel;Naseri F.;IET Sci. Meas. Technol.,2019
3. Fast detection and compensation of current transformer saturation using extended Kalman filter;Naseri F.;IEEE Trans. Power Delivery,2019
4. A wavelet-based technique for discrimination between faults and magnetizing inrush currents in transformers
5. Hyperbolic S-transform-based method for classification of external faults, incipient faults, inrush currents and internal faults in power transformers
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