AI in arcing‐HIF detection: a brief review

Bai, Hongliang

doi:10.1049/iet-stg.2019.0091

Cited by 15 publications

(10 citation statements)

References 65 publications

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“…Other methods utilize various signal processing-based techniques for HIF detection. These techniques are classified based on domain analysis into the time, frequency, time scale, and time-frequency domains [6,10]. Time-domain methods that employ behavior analysis, magnitudes, and sequences as detecting factors have been presented as an alternative to conventional mechanical procedures.…”

Section: Literature Reviewmentioning

confidence: 99%

“…This fault, usually accompanied by an electric arc, causes massive fires, leading to costly damages and potential loss of life. Despite the severity of HIFs, diagnosing these faults remains challenging due to their peculiar characteristics and low current draw, typically less than 75A [5,6]. According to Electrical safety reports, only 17.5% of HIFs were detected and eliminated by traditional protection techniques such as over-current relays.…”

Section: Introductionmentioning

confidence: 99%

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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: Literature Reviewmentioning

confidence: 99%

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 1949, the Power Systems Relaying Committee Working Group concluded that it was impossible at that time to develop techniques for detecting HIF [9]. However, especially in the last four decades, many researchers have been working to develop techniques that can detect, classify, and/or locate this type of fault [3], [6], [10], [11]. Despite the several studies presented in the literature, this research problem still lacks a definitive solution that presents high reliability and security in terms of detection and differentiation between the occurrence of HIF and other events common to distribution systems, such as switching loads or capacitors bank [6], [12], [13].…”

Section: Introductionmentioning

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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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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“…Hence, a comprehensive understanding of such faults is a necessity for many engineers in order to innovate practical solutions. The authors of [1,2] introduced an HIF detection-oriented review. Their research defined the HIF problem as a pattern classification task that can be encountered using neural network classifiers trained via features extracted from measurements (i.e., current, voltage, and magnetic field intensity).…”

Section: Introductionmentioning

confidence: 99%

“…. The evaluation criteria of such papers can depend on the following[2,31]:1. Accuracy is used to measure the performance of proposed techniques against the expected results.…”

mentioning

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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AI in arcing‐HIF detection: a brief review

Cited by 15 publications

References 65 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

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

High-Impedance Fault Diagnosis: A Review

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