High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet

Bai, Hongliang; Tang, Bingnan; Cheng, Tianyu; Liu, Hong‐Wen

doi:10.1016/j.egyr.2022.05.199

Cited by 14 publications

(2 citation statements)

References 9 publications

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“…The Working Group on HIF Detection technique, operating under the purview of the IEEE Power System Relay Committee, defines HIFs as faults that produce inadequate fault current to be detected by conventional overcurrent protective devices, such as relays or fuses [1,2]. These faults occur when a power line comes into contact with highresistive surfaces such as asphalt, grass, sand, and tree branches or when a line breaks and touches the nonzero-resistance surfaces, resulting in a significant hazard to people and public safety [3][4][5]. This fault, usually accompanied by an electric arc, causes massive fires, leading to costly damages and potential loss of life.…”

Section: Introductionmentioning

confidence: 99%

High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load

Allawi,

Hussain,

Wali

et al. 2024

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High Impedance Fault (HIF) detection in distribution networks is challenging for protection engineers, mainly because HIFs possess unique characteristics, including non-linearity, asymmetry, randomness, and relatively low fault current levels compared to the feeder load current. In this regard, the study proposes an approach to detect HIFs in a radial distribution feeder based on the spectrum analysis of current signals at the substation bus. The proposed method comprises two stages: signal decomposition and feature extraction. Fast Fourier Transform (FFT) is utilized for signal decomposition, followed by feature extraction. These features are subsequently used as input to an artificial neural network (ANN) to distinguish HIF from non-HIF events, such as linear and non-linear load switching, capacitor bank switching, and transformer energization. The proposed method's efficacy is rigorously evaluated under various dynamic conditions, demonstrating that the method can detect and differentiate HIFs from non-fault events with a high detection rate and high accuracy of 99.3%, irrespective of the HIF location and fault resistance.

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Section: Introductionmentioning

confidence: 99%

High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load

Allawi,

Hussain,

Wali

et al. 2024

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“…In the literature solutions can be found based on wavelet analysis, fuzzy logic, travelling wave, neural networks, TDR method in the underground LV distribution network, combination of TDR and FDR methods, on improved Emanuel model and DenseNet, broadband impedance spectroscopy (BIS), models with distributed parameters, and measurements of voltage, current, or expert systems [17][18][19][20][21][22][23][24][25][26]. VOLUME XX, 2022…”

Section: Introductionmentioning

confidence: 99%

Impedance-Based Short Circuit—Short Circuit Fault Location Method in Coaxial Cables

Bugajska,

Desaniuk

2023

IEEE Access

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The paper presents the results of the assessment and verification of a non-destructive method of fault location in coaxial cables with a local weakening of insulation. The essence of the method is to measure the input impedance of the cable in a short circuit at both ends and, based on obtained values, to determine the transverse impedance of the fault and the distance to the fault location. For the RG59 Cable with a length of 50.0 m, with the assumed distance to the fault location of 5.0 m, for the short circuit-short circuit method, the error in determining the fault location is from 0.0 m to 1.8 m, for open circuit-open circuit from 0.1 m to 0.2 m and open circuit-short circuit to 0.2 m. For the RG59 Cable at the actual distance to the fault location of 10.0 m, for the short circuit-short circuit method, the error in determining the fault location is from 0.0 m to 0.7 m, for open circuit-open circuit from 0.0 m to 0.1 m and open circuit-short circuit from 0.2 m to 0.7 m. For the RF-7 Cable with a length of 100.0 m, with the assumed distance to the fault location of 10.0 m, for the short circuit-short circuit method, the error in determining the fault location is from 0.7 m to 1.8 m, for open circuit-open circuit from 0.0 m to 0.7 m and open circuit-short circuit 0.2 m to 2.3 m. For the RF-7 Cable with the assumed distance to the fault location of 20.0 m, for the short circuit-short circuit method, the error in determining the fault location is from 0.1 m to 1.2 m, for open circuit-open circuit method from 0.1 m to 0.3 m and open circuit-short circuit method from 0.1 m to 0.7 m.

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A data-driven approach to microgrid fault detection and classification using Taguchi-optimized CNNs and wavelet transform

Arévalo,

Cano,

Fedoseienko

et al. 2025

Applied Soft Computing

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High impedance fault detection method in distribution network based on improved Emanuel model and DenseNet

Cited by 14 publications

References 9 publications

High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load

High Impedance Fault Detection in Distribution Feeder Based on Spectrum Analysis and ANN with Non-Linear Load

Impedance-Based Short Circuit—Short Circuit Fault Location Method in Coaxial Cables

A data-driven approach to microgrid fault detection and classification using Taguchi-optimized CNNs and wavelet transform

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