Arc Fault Model for Low-Voltage AC Systems

Wu, Haowei; Li, Xiaohui; Stade, D.; Schau, H.

doi:10.1109/tpwrd.2005.844231

Cited by 28 publications

(11 citation statements)

References 2 publications

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“…It is noted that there are many other models, such as the model proposed in [23] which new model derived by probability method according to the characteristics of three-phase fault arcs. Ref.…”

Section: Othersmentioning

confidence: 99%

Review of Arc Models in Distribution Networks

Yin

Ding

2016

MATEC Web of Conferences

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Abstract. The incipient fault in underground cable is recognized as arc fault. Then the arc model selection is very important for the incipient fault detection. The arc features and some typical models have been introduced in detail, including traditional thermal based models, arc models in low voltage and models of arc in long free air. At last, the Kizilcay's model is recommended to analyze the incipient fault in underground cable for its accuracy and widely utilized. Finally, some conclusions are summarized.

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Section: Othersmentioning

confidence: 99%

Review of Arc Models in Distribution Networks

Yin

Ding

2016

MATEC Web of Conferences

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“…This model could predict arc fault currents because arc voltage was a current-dependent function. Another similar arc fault model for alternating current (AC) systems was derived by the probability method [7]. Parise provided a simplified model which could be used to analyze and determine basic parameters such as arc resistance and ignition voltage [8].…”

Section: Introductionmentioning

confidence: 99%

Series Arc Fault Detection Algorithm Based on Autoregressive Bispectrum Analysis

et al. 2015

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Arc fault is one of the most critical reasons for electrical fires. Due to the diversity, randomness and concealment of arc faults in low-voltage circuits, it is difficult for general methods to protect all loads from series arc faults. From the analysis of many series arc faults, a large number of high frequency signals generated in circuits are found. These signals are easily affected by Gaussian noise which is difficult to be eliminated as a result of frequency aliasing. Thus, a novel detection algorithm is developed to accurately detect series arc faults in this paper. Initially, an autoregressive model of the mixed high frequency signals is modelled. Then, autoregressive bispectrum analysis is introduced to analyze common series arc fault features. The phase information of arc fault signal is preserved using this method. The influence of Gaussian noise is restrained effectively. Afterwards, several features including characteristic frequency, fluctuation of phase angles, diffused distribution and incremental numbers of bispectrum peaks are extracted for recognizing arc faults. Finally, least squares support vector machine is used to accurately identify series arc faults from the load states based on these frequency features of bispectrum. The validity of the algorithm is experimentally verified obtaining arc fault detection rate above 97%. OPEN ACCESSAlgorithms 2015, 8 930

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“…Reference [1] depicts fault arc voltages and introduces an arc fault model. Reference [2] used the business software ANSYS Fluent to simulate the multi-physical fields from a cutting arc.…”

Section: Introductionmentioning

confidence: 99%