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In a Bayesian selectivity technique has been introduced to identify the faulty feeder in compensated medium voltage (MV) networks. The proposed technique has been based on a conditional probabilistic method applied on transient features extracted from the residual currents only using the Discrete Wavelet Transform (DWT). In this paper, the performance of this selectivity technique is evaluated when...
In this paper, a Bayesian selectivity technique is introduced to identify the faulty feeder in compensated medium voltage (MV) networks. The proposed technique is based on a conditional probabilistic method applied on features extracted from the residual currents only using the discrete wavelet transform (DWT). DWT enhances to localize initial transients generated in the network due to the fault event...
In this paper, the windowed standard deviation (WSTD) is used to extract the initial transients created in residual current waveforms generated due to earth faults in medium voltage (MV) compensated networks. The sliding window width used for computing the standard deviation is evaluated from the frequency filter point of view. The fault cases occurring in a compensated 20 kV network are simulated...
A novel selectivity technique to identify the faulty feeder in unearthed Medium Voltage (MV) networks is introduced. The proposed technique is based on a simplified probabilistic method applied to transient features extracted from the residual currents only using the discrete wavelet transform (DWT). DWT will enhance the accuracy of localising fault events with different fault resistances and the...
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