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

Li, Wenchu; Liu, Yanli; Li, Ying; Guo, Fengyi

doi:10.1109/access.2020.3027002

Cited by 38 publications

(24 citation statements)

References 17 publications

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“…Nevertheless, it would take a lot of time to train neural networks with raw data. Wenchu Li et al proposed a recurrent neural network (RNN) method for series arc fault detection in multi-load systems, and further improved the detection speed and accuracy through fast continuous monitoring and probability classification results [32]. Furthermore, Yao Wang et al proposed an arc recognition model based on current raw data and convolutional neural network (CNN), and implemented the model on Raspberry Pi 3B to achieve good results [33].…”

Section: Introductionmentioning

confidence: 99%

Detection of AC Arc Faults of Aviation Cables Based on H-I-W Three-Dimensional Features and CNN-LSTM Neural Network

et al. 2022

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Aiming at the difficulty in identifying subtle AC arcs in aviation cables, this paper proposes an arc fault detection method based on the combination of three-dimensional features and convolutional neural network-long short term memory (CNN-LSTM). Firstly, based on the SAE AS5692A standard, the vibration series test, cutting parallel test, and wet arc trajectory parallel test were respectively conducted and the arc current signals under four types of loads were collected to analyze the arc faults under different incentives. Then, the three-dimensional features of arc current including Hurst exponent, inter-harmonic variance, and wavelet energy entropy (H-I-W) were extracted with an improved algorithm so as to enhance the fault identification capability and overcome the limitation of single-dimensional feature detection. Finally, a grid search algorithm was used to find out the optimal parameters, and a three-dimensional reference input CNN-LSTM neural network was designed to detect arc faults. The experimental results showed that the average detection accuracy of the proposed method for the three AC arc faults respectively reached 98.52%, 99.23%, and 98.51%. The real-time performance of the proposed method was better than the comparison methods, proving the feasibility and effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Detection of AC Arc Faults of Aviation Cables Based on H-I-W Three-Dimensional Features and CNN-LSTM Neural Network

et al. 2022

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“…Fallen trees can cause a high or medium voltage to arc over and cause intermittent earth leakage. Continuing on to the low voltage systems, arc fault detection is a hot topic in household mains [6][7][8][9][10][11][12][13][14][15] protection, photovoltaic arrays, and DC microgrids [16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]. To ensure safety in that regard, several standards have been developed, including UL 1699 for household mains arc fault detection devices and UL 1699B for DC arc detection in photovoltaic systems [32].…”

Section: Introductionmentioning

confidence: 99%

“…For both AC and DC systems, among others, the following signal processing algorithms have been taken into consideration: Wavelet transform and Fast Fourier Transform (FFT) [1,[3][4][5]28,29,58,59]; Short-Time Fourier Transform (STFT) [38,42,48]; Finite Impulse Response (FIR) filtration and derivative [51,[60][61][62]; Wigner-Ville Distribution (WVD) [11]; Signal-to-Noise Ratio (SNR) [27]; statistics [26]; and mathematical morphology [30]. Neural networks/machine learning have been used to extract the arc features [12][13][14][15]41,49,63], as well as image processing algorithms [64,65]. The signal processing methods mentioned above are able to distinguish between waveforms with and without an arc very efficiently, but they also have the drawback of a high computational complexity or the need to learn and thus long calculation times.…”

Section: Introductionmentioning

confidence: 99%

A Novel Algorithm for Fast DC Electric Arc Detection

Dołęgowski

Szmajda

2021

Energies

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Electric arcing is a common problem in DC power systems. To overcome this problem, the electric arc detection algorithm has been developed as a faster alternative to existing algorithms. The following issues are addressed in this paper: The calculation of the proposed algorithm of incremental decomposition of the signal over time; the computational complexity of Fast Fourier Transform (FFT) and the incremental decomposition; the test bench used to measure electric arcs at given parameters; the analysis of measurements using FFT; and the analysis of measurements using incremental decomposition. The parameters are the DC voltage, electric load, and width of the gap between electrodes. The results showed that the proposed algorithm allows for a faster calculation—about seven times faster than FFT—and cheaper implementation in electric arc detection devices than FFT.

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“…In the last several years, AI algorithms, e.g. support vector machine (SVM), neural network (NN), etc., have been widely investigated for fault diagnosis due to their powerful learning capacity [14][15][16]. Reference [17] has proposed to extract twelve kinds of current features to train SVM for distinguishing between arcing and non-arcing conditions.…”

mentioning

confidence: 99%

“…The latter one refers to the end-to-end frameworks, e.g. convolutional NN and recurrent NN, without manual feature extraction [16]. However, there are still many challenging issues in AC series arc fault detection.…”

mentioning

confidence: 99%

Machine Learning Approach to Detect Arc Faults Based on Regular Coupling Features

Jiang

Bao

Hong

et al. 2023

IEEE Trans. Ind. Inf.

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During AC series arc faults (SAFs), arcing current features can change significantly or vanish rapidly under different load-combination modes and fault inception points. The phenomena make it very challenging for feature-extracting algorithms to detect SAFs. To address the issues, this paper presents a detection model based on regular coupling features (RCFs). After the model is only trained by the samples in single-load circuits, it can detect SAFs under unknown multi-load circuits. To extract the RCFs, asymmetric magnetic flux is coupled by passing the live line and the neutral line through the current transformer. The coupling signals are not influenced by the multi-load circuits. According to the unique signals, two time-domain features and one frequency-domain feature are extracted to represent the RCFs, including impulsefactor analysis (IFA), covariance-matrix analysis (CMA) and multiple frequency-band analysis (MFA). Then, the impulse factor and its threshold are used to preprocess the signals and decrease analysis complexity for the classifier. Finally, the experimental results show that the proposed method has significantly improved generalization ability and detection accuracy in SAF detection.

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

Cited by 38 publications

References 17 publications

Detection of AC Arc Faults of Aviation Cables Based on H-I-W Three-Dimensional Features and CNN-LSTM Neural Network

Detection of AC Arc Faults of Aviation Cables Based on H-I-W Three-Dimensional Features and CNN-LSTM Neural Network

A Novel Algorithm for Fast DC Electric Arc Detection

Machine Learning Approach to Detect Arc Faults Based on Regular Coupling Features

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