2019
DOI: 10.1016/j.ijepes.2018.08.005
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Improved traditional directional protection by using the stationary wavelet transform

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Cited by 20 publications
(10 citation statements)
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“…Further, an improved approach using PSO is described in Reference 111 to strengthen the WT approach with better operational efficiency. A protection algorithm is proposed in Reference 112 based on stationary WT to detect the fault occurring time much faster with a less computational burden. A comparative study of WT‐based SVM and independent component analysis‐based SVM is presented in Reference 113.…”
Section: Other Miscellaneous Schemesmentioning
confidence: 99%
“…Further, an improved approach using PSO is described in Reference 111 to strengthen the WT approach with better operational efficiency. A protection algorithm is proposed in Reference 112 based on stationary WT to detect the fault occurring time much faster with a less computational burden. A comparative study of WT‐based SVM and independent component analysis‐based SVM is presented in Reference 113.…”
Section: Other Miscellaneous Schemesmentioning
confidence: 99%
“…To overcome this issue, later, a waveletbased multiresolution approach is described in Shekar et al 79 Here, the transient current is utilized to develop the algorithm by using the coefficient of Mother Biorthogonal 1.5 wavelets. Further, to enhance the performance, a robust method inclusive of all conventional methods along with wavelet transform is described in Leal et al 80 Here, real-time stationary wavelet transform is implemented in sequence components for the detection of three-phase fault with the help of negative sequence components even in severe voltage sag conditions. The major issues related to the wavelet transformbased protection schemes are the (a) dependency of system performance upon the noise in the power signals and (b) increased computational burden.…”
Section: Wavelet Transform-based Protection Strategymentioning
confidence: 99%
“…Several studies have contributed to this field of research. Traditional fault diagnostic methods can be divided into impedance-based methods [4], [5], voltage sag based methods [6], traveling wave based methods [7], [8], and machine learning based methods [9]. For example, [4] proposes an impedance-based method to locate faults in distribution networks using an impedance matrix.…”
Section: Introductionmentioning
confidence: 99%