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Fault Classification and Localization Scheme for Power Distribution Network
by
Moloi, Katleho
, Davidson, Innocent E.
, Ndlela, Nomihla Wandile
in
Algorithms
/ Approximation
/ Classification
/ Electricity
/ fault classification
/ fault location
/ Fuzzy logic
/ Neural networks
/ Optimization techniques
/ Power
/ power distribution system
/ Signal processing
/ Simulation
/ Support vector machines
/ wavelet packet decomposition
/ Wavelet transforms
2022
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Fault Classification and Localization Scheme for Power Distribution Network
by
Moloi, Katleho
, Davidson, Innocent E.
, Ndlela, Nomihla Wandile
in
Algorithms
/ Approximation
/ Classification
/ Electricity
/ fault classification
/ fault location
/ Fuzzy logic
/ Neural networks
/ Optimization techniques
/ Power
/ power distribution system
/ Signal processing
/ Simulation
/ Support vector machines
/ wavelet packet decomposition
/ Wavelet transforms
2022
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Do you wish to request the book?
Fault Classification and Localization Scheme for Power Distribution Network
by
Moloi, Katleho
, Davidson, Innocent E.
, Ndlela, Nomihla Wandile
in
Algorithms
/ Approximation
/ Classification
/ Electricity
/ fault classification
/ fault location
/ Fuzzy logic
/ Neural networks
/ Optimization techniques
/ Power
/ power distribution system
/ Signal processing
/ Simulation
/ Support vector machines
/ wavelet packet decomposition
/ Wavelet transforms
2022
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Fault Classification and Localization Scheme for Power Distribution Network
Journal Article
Fault Classification and Localization Scheme for Power Distribution Network
2022
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Overview
In this paper, a fault protection diagnostic scheme for a power distribution system is proposed. The scheme comprises a wavelet packet decomposition (WPD) for signal processing and analysis and a support vector machine (SMV) for fault classification and location. The scheme is tested on a reduced Eskom 132 kV power line. The WPD is used to extract fault signatures of interest and the SVM is subsequently used for fault classification and locating various fault conditions. Furthermore, we investigate the effectiveness of the SVM scheme using different samples of the cycles for fault classification and location. The results show that the fault classification and location on a distribution line can be determined rapidly and efficiently irrespective of the fault impedance and incipient angle with minimum estimation error. Lastly, the proposed scheme is tested on a grid-integrated system with renewable energy sources.
Publisher
MDPI AG
Subject
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