An arc fault diagnostic method for low voltage lines using the difference of wavelet coefficients

Duan, Ping; Xu, Liping; Ding, Xi; Ning, Chenguang; Duan, Chenxu

doi:10.1109/iciea.2014.6931196

Cited by 10 publications

(7 citation statements)

References 4 publications

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“…For detection methods based on time-frequency domain feature of electrical measurements, aspects such as computation complexity, universality, real-time performance and hardware implementation are contrasted. Compared with other algorithms in Duan et al (2014) and Koziy et al (2013), which need several layers of wavelet decomposition or require different identifications for different loads, the CAWTC method -extracting first-layer details -has a smaller amount of calculation and is available to more impedance types of electrical appliance.…”

Section: 24mentioning

confidence: 99%

“…The mother wavelet selection is of great significance to proper and effective signal analysis. In general, the evaluation for applicability of mother wavelet could be classified into two sets: mathematical parameters generated from wavelet decomposition (Duan et al, 2014) and wavelet's application results in engineering tests. Above all, a suitable mother wavelet should be chosen for CAWTC algorithm application.…”

Section: Novel Diagnostic Algorithmmentioning

confidence: 99%

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A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet

Wang

Zhang

et al. 2017

COMPEL

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Purpose A multi-load available, response reliable and product-friendly method is in urgent need to diagnose the signs of incipient arcing. This paper aims to propose a novel algorithm that originates the application of correlativity analysis of wavelet high-frequency component in state discrimination and further in arcing detection. Design/methodology/approach The proposed method calculates the correlation coefficient between the extraction by wavelet transform of arcing series current and that of normal, compares it with a predefined threshold and outputs a trip signal when eight qualified arcing half cycles within a period of 0.5 s are detected. Findings Typical appliances are selected in laboratory for arc detection to test the method which carries on independently of impedance type. The algorithm could be optimized to identify arcing for different kinds of loads, including resistive, inductive, capacitive and switching power supply loads, with a same correlation coefficient threshold. Practical implications The arithmetic operations of the method are addition and multiplication, which contribute to efficient data computation and transmission for micro-processor to undertake. The reference optimal sampling rate recommended for the algorithm helps to reduce the processed data volume and shows its promising prospect for portable product development. Originality/value This proposed correlativity analysis of wavelet transform component algorithm could classify the tested signal into two categories, which benefits the discrimination of normal and fault states in condition monitoring. Laboratory tests prove that it works effectively in arc detection for the commonly used impedance types of loads and needs no offline self-learning or training of samples.

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Section: 24mentioning

confidence: 99%

Section: Novel Diagnostic Algorithmmentioning

confidence: 99%

A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet

Wang

Zhang

et al. 2017

COMPEL

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“…Nikola L Georgijevic detected arc fault by calculating the modified Tsallis entropy of current [5]. Peiyong Duan used the fast wavelet transform to construct characteristic parameters of the series arc fault [6]. Joshua E Siegel used Fourier coefficients, Mel frequency cepstrum coefficients, and wavelet coefficients as feature quantities to train a neural network for arc fault recognition [7].…”

Section: Introductionmentioning

confidence: 99%

Series Arc Fault Diagnosis and Line Selection Method Based on Recurrent Neural Network

Liu

et al. 2020

IEEE Access

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Series arc fault is a common phenomenon in the power system, it will directly affect the working reliability, but there is no mature method to detect it due to its concealment and chaos. Common detection methods that build on the arc fault eigenvectors obtained by manual analysis are subjective and incomprehensive. A series arc fault diagnosis and line selection method based on recurrent neural network (RNN) for a multi-load system was proposed in this paper. Firstly, a series arc fault experiment under a multi-load system was carried out, the training set and test set were built by using the data obtained from the experiment. Then, the RNN model was built, trained, and tested through the training set and test set. Finally, the fast-continuous detection method and the probability-based classification result correction method were proposed, and the detection speed and accuracy were improved much further. The results show that the proposed method is effective for diagnosing series arc fault and line selection under a multi-load system, without analysis of arc fault characteristics. INDEX TERMS Series arc fault, deep learning, recurrent neural network, RNN, fault diagnosis, fault line selection.

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“…Current wavelet transform processing methods can be summarized as follows. In a half period or a full period, the mean value, forward difference, standard deviation, effective value and energy value of approximate or detailed signal after current wavelet transform are used as quantitative indicators to identify arc faults [8][9][10][11][12][13][14][15][16][17]. Reference [8] proposed a detection method that takes the average value of reconstructed current signal after wavelet decomposition and the high frequency coefficient of the difference as the indicator.…”

Section: Introductionmentioning

confidence: 99%

“…In a half period or a full period, the mean value, forward difference, standard deviation, effective value and energy value of approximate or detailed signal after current wavelet transform are used as quantitative indicators to identify arc faults [8][9][10][11][12][13][14][15][16][17]. Reference [8] proposed a detection method that takes the average value of reconstructed current signal after wavelet decomposition and the high frequency coefficient of the difference as the indicator. Reference [12] analyzed the energy characteristics of the current frequency band using the db3 wavelet and determined whether an arc fault occurred by comparing the energy ratio of each frequency band.…”

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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An arc fault diagnostic method for low voltage lines using the difference of wavelet coefficients

Cited by 10 publications

References 4 publications

A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet

A novel diagnostic algorithm for AC series arcing based on correlation analysis of high-frequency component of wavelet

Series Arc Fault Diagnosis and Line Selection Method Based on Recurrent Neural Network

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

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