Aspects of arc-flash protection and prediction

Kumpulainen, Lauri; Hussain, G. Amjad; Rival, Marc; Lehtonen, Matti; Kauhaniemi, Kimmo

doi:10.1016/j.epsr.2014.05.011

Cited by 18 publications

(10 citation statements)

References 16 publications

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“…At this stage, it is necessary to use sensors to detect an electrical isolation breakdown to prevent further development of an accident. A document [8] provides a technological review of arc protection in distributive devices with an air-insulator. It covers the entire range from design aspects of distributive devices to the ultra-quick removal of an arc.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Designing a combined device for determining the place of arc discharge

Parkhomenko

Aniskov

Tsibulevsky³

et al. 2018

EEJET

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Section: Literature Review and Problem Statementmentioning

confidence: 99%

Designing a combined device for determining the place of arc discharge

Parkhomenko

Aniskov

Tsibulevsky³

et al. 2018

EEJET

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“…Generally speaking, taking relay settings down is the best feasible solution to reduce the risk of IE exposure but is limited by the range of available settings that will still achieve the requirement of discriminating relay operation [32,33]. Full comprehensive review regarding aspects of the AF issues could be found in [34]. The IE at the specific working distance and with the devices clearing time [27] is defined in the following equation.…”

Section: Overview Of the Arc-flash Event And The Calculation Of The Imentioning

confidence: 99%

Optimal directional digital overcurrent relays coordination and arc-flash hazard assessments in meshed networks

El‐Fergany

2015

Int. Trans. Electr. Energ. Syst.

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Summary The article addresses the arc‐flash hazard assessments combining the optimal coordination of digital overcurrent relays. A novel objective function is adopted to concurrently reduce the arc‐flash hazards while adapting the optimal settings of directional overcurrent relays subjects to set of coordination constraints. The flower pollination algorithm (FPA) is applied to attain the target. The proposed FPA‐based technique is employed to define three main parameters for the relay settings. The performance and value of the proposed FPA‐based methodology are demonstrated on the 8‐bus and 15‐bus meshed networks with various fault scenarios. Comparisons with the other competitive algorithms and subsequent discussions validate its good performance. Efficacious attributes of the novel objective function are compared and highlighted over the traditional objective function commonly used by the other researchers reported in the literature. Copyright © 2015 John Wiley & Sons, Ltd.

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“…When the arc fault occurs, the current signals of lines with different loads have various time-frequency characteristics [5,6]: The randomness and fluctuation of the current signal increase and the periodicity in the strict definition is lost. There will be a "zero off" phenomenon at the zero crossing point (namely, where the current waveform shows an obvious shoulder phenomenon); in addition, multiple pulse currents may occur within a period, leading to a significant increase in the high-frequency components of current signals.…”

Section: Introductionmentioning

confidence: 99%

Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

Han

Huang

et al. 2020

Electronics

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The influence of a series arc on line current is different with different loads, which makes it difficult to accurately extract arc fault characteristics suitable for all loads according to line current signal. To improve the accuracy of arc fault detection, a series arc fault detection method based on category recognition and an artificial neural network is proposed on the basis of analyzing the current characteristics of arc faults under different loads. According to the waveform of current and voltage, the load is divided into three types: Resistive category (Re), resistive-inductive category (RI), and rectifying circuit with a capacitive filter category (RCCF). Based on the wavelet transform, the characteristics of line current in the time domain and frequency domain when the series arc occurs under different types of loads are analyzed, and then the time and frequency indicators are taken as the inputs of the artificial neural network to establish three-layer neural networks corresponding to three types of loads to realize the detection of the series arc fault of lines under different categories of loads. To avoid the neural network falling into a local optimum, the initial weight and threshold of the neural network are optimized by a genetic algorithm, which further improves the accuracy of the neural network in arc identification. The experimental results show that the proposed arc detection method has the advantages of high recognition rate and a simple neural network model.

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Aspects of arc-flash protection and prediction

Cited by 18 publications

References 16 publications

Designing a combined device for determining the place of arc discharge

Designing a combined device for determining the place of arc discharge

Optimal directional digital overcurrent relays coordination and arc-flash hazard assessments in meshed networks

Series Arc Fault Detection Method Based on Category Recognition and Artificial Neural Network

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