High impedance fault detection in radial distribution system using wavelet transform

Mishra, Manohar; Rout, Pravat Kumar; Routray, Pallavi

doi:10.1109/indicon.2015.7443176

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…The opposite will occur at V f < V n as the current will flow back to the source, and when V n < V f < V p , no current will flow into the system. Other researchers in [47][48][49][50] expanded into Emanuel's model by experimenting with a variable resistor, as shown in Figure 6. However, this model lacks the ability to simulate the build-up and shoulder characteristics of an HIF.…”

Section: Two Antiparallel Diodesmentioning

confidence: 99%

High-Impedance Fault Diagnosis: A Review

Aljohani

Habiballah

2020

Energies

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High-impedance faults (HIFs) represent one of the biggest challenges in power distribution networks. An HIF occurs when an electrical conductor unintentionally comes into contact with a highly resistive medium, resulting in a fault current lower than 75 amperes in medium-voltage circuits. Under such condition, the fault current is relatively close in value to the normal drawn ampere from the load, resulting in a condition of blindness towards HIFs by conventional overcurrent relays. This paper intends to review the literature related to the HIF phenomenon including models and characteristics. In this work, detection, classification, and location methodologies are reviewed. In addition, diagnosis techniques are categorized, evaluated, and compared with one another. Finally, disadvantages of current approaches and a look ahead to the future of fault diagnosis are discussed.

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Section: Two Antiparallel Diodesmentioning

confidence: 99%

High-Impedance Fault Diagnosis: A Review

Aljohani

Habiballah

2020

Energies

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“…Moreover, it produces better spatial and spectral localization of signal. In recent decades, such advanced powerful tool has been used for designing the protective relays [17,[18][19][20][21][22][23][24]. In DWT, the fault current signal x (t) is decomposed into low and high frequency components such as approximation (A) and detailed coefficients (D) which is mathematically expressed in (1) and (2) for decomposed signal [25][26].…”

Section: Discrete Wavelet Transformmentioning

confidence: 99%

Transmission line fault identification and classification with integrated FACTS device using multiresolution analysis and naïve bayes classifier

Aker

Othman

Aris

et al. 2020

IJPEDS

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This paper is present a novel approach for solving the pending under-reach problem encountered by distance relay protection scheme in the 3rd zones protection coverage for a midpoint STATCOM compensated transmission lines. The propose transmission line model is develop in Matlab for analyzed feature extraction using Discrete Wavelet multiresolution analysis approach. Extracted feature from standard deviation and entropy energy contents of SLG transient faults current at location beyond the integrated STATCOM used for machine learning algorithm model building using WEKA software. The Naïve Bayes classifier model perform best with robustness prediction and detection of faults with quick convergence even with less training data. The outperformance of the proposed classifier has been 100 % for the relay algorithm modification for under-reach problem elimination in 3rd zones protection coverage.

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“…The combination of WT and ANN appears to be an acceptable approach for solving the HIF detection problem, as shown in [6,[14][15][16], where DWT was used as a pre-processing stage for feature extraction and then used as an input to ANN stage [11].…”

Section: Literature Reviewmentioning

confidence: 99%