High impedance fault localization: A comprehensive review

Baharozu, Eren; İlhan, Suat; Soykan, Gürkan

doi:10.1016/j.epsr.2022.108892

Cited by 10 publications

(4 citation statements)

References 60 publications

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“…The non-linear model with antiparallel diodes, widely employed in the literature, is used to analyze the HIF, considering that the single-phase short-circuit to earth is predominant, occurring in 63% of faults [7], [38]. Figure 1 shows the generic HIF model.…”

Section: A Hif Modelmentioning

confidence: 99%

“…The location, phase, time, and duration of the HIFs vary for each scenario, as well as the location and time of the capacitor bank activation. HIFs resistors are set to 10 kΩ in all cases [7], [16]. For the NPR and NF networks, the RMS current data of each simulated circuit is used generating matrices of size 198339×3.…”

Section: B Validation and Training Scenarios For Hif Detectionmentioning

confidence: 99%

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Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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High impedance faults constitute one of the biggest challenges in electrical power systems. In overhead distribution systems, faults are caused by tree branches that touch the electrical grid or by the rupture of energized conductors on low conductivity soils. They are also known as low-current faults, which are not detected by conventional protection systems, compromising the quality of the power supply and causing hazardous risks to the electrical system. This paper aims to address the problem of high impedance faults detection using Artificial Neural Networks: two Multi Layer Perceptron networks, being one Neural Pattern Recognition and another Neural Fitting, and a Convolutional Neural Network. The neural networks are trained and analyzed in scenarios based on a medium-voltage distribution grid model, located in the Basque Country, Spain. The network topologies are implemented, repeatedly trained considering multiple architectures, and validated in other scenarios with different location, time, and duration of the fault using the Matlab software. After, the criteria of accuracy, reliability, security, safety and sensitivity are evaluated. At last, a comparative analysis between them is carried out, and from the results obtained, a superior performance of the Convolutional Neural Network in compared to the Multi Layer Perceptron networks is observed.

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Section: A Hif Modelmentioning

confidence: 99%

Section: B Validation and Training Scenarios For Hif Detectionmentioning

confidence: 99%

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Diefenthäler,

Sausen,

De Campos

et al. 2023

IEEE Access

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show abstract

“…T he Electric Power System (EPS) plays a critical role in the transmission and distribution of electrical power and therefore requires efficient fault detection mechanisms [1,2]. A major challenge in EPS is the detection of faults such as high impedance faults (HIFs) and conductor breaks [3,4,5]. To overcome this challenge, ATPDraw™ software has become widely used for EPS simulation and analysis [6], enabling accurate modeling distribution systems [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

“…When a CB occurs and a conductor comes into contact with a high impedance surface such as concrete, asphalt, sand, or vegetation, it can lead to a High Impedance Fault (HIF), which is typically difficult to reliably detect and distinguish from other common events in the network. These situations pose risks to human life and property [3,4,5]. Moreover, due to the extremely low current associated with this type of fault, conventional protection schemes fail to activate, as they require a significantly higher current threshold [4].…”

Section: Introductionmentioning

confidence: 99%

Simulation tutorial in ATPDraw™ software for Lateral Branch Switching, Lumped Load, Distributed Load, Capacitor Switching in distribution systems

Pereira de Oliveira,

Senhorini,

De Freitas

et al. 2023

Acad. Journal on Comp., Eng. and Appl. Math.

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This tutorial paper provides a comprehensive guide on performing switching operations in distribution systems using ATPDraw™ software. The focus is on understanding the appropriate modeling and simulation techniques for various types of load switching events in electrical distribution systems. The tutorial covers key aspects such as lateral branch switching, lumped load, distributed load, capacitor, and non-linear load. By acquiring a thorough understanding of these switching operations, researchers and practitioners can effectively study and analyze electrical distribution systems. Having the knowledge and skills to accurately model and simulate switching events is crucial for conducting in-depth studies related to energy distribution. This tutorial serves as a valuable resource for researchers and professionals seeking to enhance their understanding and proficiency in modeling and simulating switching operations in electrical distribution systems.

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Fault location observability rules for impedance-based fault location algorithms

Hosseini Dolatabadi,

Hamedani Golshan

2023

Electric Power Systems Research

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High impedance fault localization: A comprehensive review

Cited by 10 publications

References 60 publications

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Artificial Neural Networks: Modeling and Comparison to Detect High Impedance Faults

Simulation tutorial in ATPDraw™ software for Lateral Branch Switching, Lumped Load, Distributed Load, Capacitor Switching in distribution systems

Fault location observability rules for impedance-based fault location algorithms

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