Detection of serial arc fault on low-voltage indoor power lines by using radial basis function neural network

Liu, Yuwei; Wu, Chi‐Jui; Wang, Yi-Chieh

doi:10.1016/j.ijepes.2016.04.013

Cited by 24 publications

(15 citation statements)

References 15 publications

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“…From this comparison, it can be seen that the architecture of the proposed method based on a Kalman filter has the important advantage to detect arcing faults in the transient regime considering different maskingtype configurations (including EMI tests) on a larger number of loads (simple and combined). Also, the greatest advantage of the proposed method compared to proposed approaches [4,6,7,9,14,25] is the using of an adaptive thresholding mechanism which can avoid efficiently unwanted trips in the process of fault detection without requiring complex training tasks as are used in approaches [27][28][29]. Moreover, concerning the time response, the proposed method gives satisfactory results and exceeds the requirements defined by the standard IEC 62606 (tripping time=0.12 s for line current = 32 A).…”

Section: Case 3: Parallel Disturbing Appliancementioning

confidence: 88%

“…Statistical techniques used for the selection of static thresholds presented in [26] are not sufficient to develop robust algorithms capable of avoiding false activation on circuit breakers. Another solution is to use a neural network or an SVM [27][28][29]. However, they require long learning stages and are difficult to implement in a conventional electronic circuit board.…”

Section: Introductionmentioning

confidence: 99%

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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: Case 3: Parallel Disturbing Appliancementioning

confidence: 88%

Section: Introductionmentioning

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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“…A harmonic analysis has been conducted on a specific frequency band of the line current when arc faults occur [10][11][12][13][14]. Some time-frequency analysis methods, such as a short-time Fourier transform [15], wavelet transform [11,[16][17][18][19][20][21][22], and Hilbert-Huang transform [23], have also been used for fault arc detection. In addition, some pattern recognition methods have been used to help classify the situation when an arc fault occurs, such as support vector machines [13] and neural networks [11,[20][21]23].…”

Section: Volt-ampere Characteristics Of An Arcmentioning

confidence: 99%

Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

Zhang

et al. 2019

Energies

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Owing to the shortcomings of existing series arc fault detection methods, based on a summary of arc volt–ampere characteristics, the change rule of the line current and the relationship between the voltage and current are deeply analyzed and theoretically explained under different loads when series arc faults occur. A series arc fault detection method is proposed, and the software flowchart and principles of the applied hardware implementation are given. Finally, a prototype of an arc fault detection device (AFDD) with a rated voltage of 220 V and a rated current of 40 A is developed. The prototype was tested according to experimental methods provided by the Chinese national standard, GB/T 31143-2014. The experimental results show that the proposed detection method is simple and practical, and can be implemented using a low-cost microprocessor. The proposed method will provide good theoretical guidance in promoting the research and development of an AFDD.

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“…Based on the multi-resolution feature of wavelet decomposition, Zhang [21] analyzed the components of the current signal at different frequency bands by wavelet transform (WT) and the wavelet energy was extracted as the fault feature. Chirp zeta transform (CZT) [22] and WT [23] were utilized to analyze the spectrum of the current signal and extract the time-frequency domain characteristics for arc fault detection. However, the microwave oven, computer and other nonlinear loads will produce a wealth of harmonic components under normal operation and this method is prone to misjudgment if only a time-frequency component is used as the fault characteristic.…”

Section: Introductionmentioning

confidence: 99%

A Novel Arc Fault Detection Method Integrated Random Forest, Improved Multi-scale Permutation Entropy and Wavelet Packet Transform

et al. 2019

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Arc faults are one of the important causes of electric fires. In order to solve the problem of randomness, diversity, the concealment of series arc faults and to improve the detection accuracy, a novel arc fault detection method integrated random forest (RF), improved multi-scale permutation entropy (IMPE) and wavelet packet transform (WPT) are designed. Firstly, singular value decomposition (SVD) was applied to filter the current signal and then the high-dimensional fault features were constructed by extracting IMPE, the wavelet packet energy and the wavelet packet energy-entropy. Afterward, the high-dimensional fault features were employed to train the RF to realize the arc fault detection of different load types and the experimental results verify the effectiveness of the arc fault detection method designed in this paper. Finally, the comparative experiments demonstrates that the RF shows better performance in arc fault detection compared to the back-propagation neural network (BPNN) and least squares support vector machines (LSSVM), and that the experiments of transient events indicate that RF is able to effectively avoid incorrectly detecting different load types during the start operations and stop operations.

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Detection of serial arc fault on low-voltage indoor power lines by using radial basis function neural network

Cited by 24 publications

References 15 publications

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

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

Analysis of the Effects of Arc Volt–Ampere Characteristics on Different Loads and Detection Methods of Series Arc Faults

A Novel Arc Fault Detection Method Integrated Random Forest, Improved Multi-scale Permutation Entropy and Wavelet Packet Transform

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