Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks

Pan, Qi; Jovanovic, Slavisa; Lezama, Jinmi; Schweitzer, Patrick

doi:10.1016/j.epsr.2016.10.008

Cited by 65 publications

(28 citation statements)

References 14 publications

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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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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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“…e time domain feature is mainly based on the zero-rest phenomenon of arc fault current [10]. e frequency domain feature extraction is mainly based on Fourier transform [11][12][13] and wavelet transform [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

PSO–SOM Neural Network Algorithm for Series Arc Fault Detection

Chen

Zuo

et al. 2020

Advances in Mathematical Physics

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Self-organizing feature map (SOM) neural network is a kind of competitive neural network with unsupervised learning. It has the strong abilities of self-organization and self-learning. However, the classification accuracy of SOM neural network may decrease when the features of tested object are not obvious. In this paper, the particle swarm optimization (PSO) algorithm is used to optimize the weight values of SOM network. Three indexes, i.e., intra-class density, standard deviation and sample difference, are used to judge the weight value, which can improve the classification accuracy of the SOM network. PSO–SOM network is applied to the detection of series arc fault in electrical circuits and compared with conventional SOM network and learning vector quantization (LVQ) network. The detection accuracy of the PSO–SOM network is 95%, which is higher than conventional SOM network and LVQ network.

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“…Time-frequency methods are able to analyze non-stationary transient signals produced by arcing faults using essentially the Wavelet packet transform [11][12][13][14] and recently the Hilbert-Huang transform [15]. The performance of these methods is strongly influenced by the sampling frequency, the level of signal decomposition and type of load connected to the power network [16].…”

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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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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Discrete wavelet transform optimal parameters estimation for arc fault detection in low-voltage residential power networks

Cited by 65 publications

References 14 publications

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

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

PSO–SOM Neural Network Algorithm for Series Arc Fault Detection

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

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