Frequency phenomenon and algorithms for arc detection

Phipps, K.O.; Cooke, Thomas A.; Dorr, D.; Keebler, Philip F.

doi:10.1109/isemc.2010.5711268

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…A derivative type filtering method for arcing fault detection is presented in [35]. According to the authors, this method is based on the third derivative of the arc current which produces large logarithms gains preserving the low and high frequency spectral data.…”

Section: The Third Order Differencementioning

confidence: 99%

Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

Calderon-Mendoza

Schweitzer

Weber

2019

International Journal of Electrical Power & Energy Systems

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This paper presents a method for detecting series arcing faults in AC home electrical networks. The proposed algorithm is based on both a Kalman filter, used for identifying fault symptoms and a decision block, which confirms the presence of a series arc fault to activate a tripping signal. The current measured at one end of the power line is estimated using a model of two steady-state variables (X1 and X2). Firstly, residuals and the third order difference of state X2 are used as input parameters of a Fuzzy logic processor for detecting fault symptoms. Secondly, the fault symptoms are processed by a detection logic block, which confirms the presence of an electrical arcing fault. The algorithm is tested on a variety of loads in single or masking load configurations chosen accordingly to the requirements of the UL 1699 and IEC 62606 standards. The algorithm is also tested in the steady state or at load start (transient state). This method's performance is studied and discussed in the final part. Experimental results show that the method we propose can detect arcing faults efficiently, avoiding false tripping, whilst taking into account a high degree of diagnosis accuracy and average detection time.

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Section: The Third Order Differencementioning

confidence: 99%

Kalman filter and a fuzzy logic processor for series arcing fault detection in a home electrical network

Calderon-Mendoza

Schweitzer

Weber

2019

International Journal of Electrical Power & Energy Systems

View full text Add to dashboard Cite

show abstract

“…In general the frequency range is selected to avoid interference caused by the load. Other solutions operate with a time domain feature approach-rapid changes in arcing currents (di/dt) [12], autocorrelation [13], crest factor or inter-harmonic distortion [14,15]. Some methods are based on the implementation of time-frequency analysis or wavelet packet transform [16], which require long and empirical settings which negatively affect their uses on a large scale.…”

Section: Introductionmentioning

confidence: 99%