Series Arc Fault Detection Based on Random Forest and Deep Neural Network

Jiang, Jun; Li, Wei; Wang, Zhe; Bie, Yifan; Schwarz, Harald; Zhang, Chaohai

doi:10.1109/jsen.2021.3082294

Cited by 59 publications

(21 citation statements)

References 27 publications

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“…Certain energy is bound to be generated after an arc fault occurs in the line, so the energy entropy of decomposed IMFs components is selected as one of the characteristic quantities [10].…”

Section: Energy Entropy Feature Extractionmentioning

confidence: 99%

AC Series Arc Fault Diagnosis Method Based on AO-VMD Multidimensional Feature Extraction

Di,

Liu,

et al. 2023

J. Phys.: Conf. Ser.

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In view of the difficulty in determining the number of mode components K and penalty factor α in VMD, which leads to poor signal decomposition effect and low diagnosis and recognition rate due to insufficient feature extraction in AC arc fault, an arc fault diagnosis method based on the Aquila algorithm was proposed to optimize the multivariate feature extraction of variational mode decomposition. First of all, the arc fault test platform was set up to consider the resistive, inductive, capacitive, and other household loads were considered to obtain the normal and fault current data. Second, the optimal K and α parameters were obtained by AO-VMD optimization and then decomposed into intrinsic mode functions (IMFs) by substituting them into VMD. Then, the time domain characteristics of the IMF2 component, the Energy Entropy of IMFs, and the Fuzzy Entropy of the kurtosis maximum IMFk component were extracted respectively, and the multidimensional fault characteristic matrix was constructed. Finally, the random forest (RF) model was used to accurately identify arc faults. The experiment shows that the average fault recognition rate of each type of load is above 99%, which has an excellent diagnostic effect.

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Section: Energy Entropy Feature Extractionmentioning

confidence: 99%

AC Series Arc Fault Diagnosis Method Based on AO-VMD Multidimensional Feature Extraction

Di,

Liu,

et al. 2023

J. Phys.: Conf. Ser.

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“…Arc intelligent recognition algorithms include BP neural network (BPNN), SVM [26], DNN [29], RNN [32], CNN [33], LSTM [36], and CNN-LSTM. Among them, CNN-LSTM combines the advantages of CNN and LSTM with space invariance and time invariance, so it shows more advantages in dealing with time series and complex data.…”

Section: Arc Fault Detection Based On Cnn-lstmmentioning

confidence: 99%

“…Wang et al proposed to extract signal features with sparse matrix, and input them into fully connected neural network (FCNN) for residential AC arc identification [28]. Jiang et al carried out series arc fault identification based on random forest (RF) and deep neural network (DNN) [29]. Ali Amiri et al proposed a method for series arc fault detection in photovoltaic systems based on voltage signal determinism and used a recursive graph method to derive the signal determinism for detecting series arc faults [30].…”

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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“…The arc fault detection products primarily rely on algorithms that distinguish between fault current and normal current. These algorithms encompass threshold techniques [8] as well as machine learning approaches [9][10].…”

Section: Introductionmentioning

confidence: 99%

Detection performance of arcing fault detectors in the Chinese market according to the Chinese Standard GB/T 31143

Huang,

Jia,

et al. 2024

J. Phys.: Conf. Ser.

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There are two sets of standards available in the Chinese market that specify the design requirements and operating tests for arc fault detection devices (GB/T 31143) and arcing fault detectors (GB 14287.4) respectively. While arcing fault detectors (AFDs) are claimed to have passed GB 14287.4 standard tests, it remains unclear whether these AFDs can pass GB/T 31143 standards and effectively detect arc fault signals. This study experiments to explore the operational performance of AFDs in the Chinese market based on GB/T 31143 standards, revealing that not all AFDs can pass this test and provide adequate protection for people’s lives and property. The main reason for the failure is that the arcing characteristics, such as flat shoulders and reduced amplitude, of the signal are easily masked by other branch circuit loads.

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Series Arc Fault Detection Based on Random Forest and Deep Neural Network

Cited by 59 publications

References 27 publications

AC Series Arc Fault Diagnosis Method Based on AO-VMD Multidimensional Feature Extraction

AC Series Arc Fault Diagnosis Method Based on AO-VMD Multidimensional Feature Extraction

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

Detection performance of arcing fault detectors in the Chinese market according to the Chinese Standard GB/T 31143

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