Detection of DC Series Arc Fault Based on VMD and ELM

Ma, Tao; Tian, Ersheng; Liu, Zhenxing; Liu, Shuxin; Guo, Tao; Wang, Taowei; Fu, Long Tai

doi:10.1088/1742-6596/1486/6/062037

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…The comparison I: Comparative analysis on the necessity of VMD parameter optimization. When no VMD parameter optimization is carried out, the default value of VMD decomposition is set as [K, Į]= [4,2000] according to the study in article [11]. When the AO-VMD parameters proposed in this paper are optimized, the results are shown in Table 4.…”

Section: Contrastive Analysismentioning

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: Contrastive Analysismentioning

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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show abstract

“…Based on various electrical fault data from different sources, methods such as expert systems, Petri nets, multi-source fusion and analytical models [1][2][3][4][5][6][7] are derived. But there is the problem of poor recognition accuracy, so the optimization of the model has become the main research direction.…”

Section: Introductionmentioning

confidence: 99%

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

Zou¹,

Lv²,

Li³

et al. 2022

J. Phys.: Conf. Ser.

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With the increase of electrical equipment, the hidden danger of its failure also rises. Aiming at the problem that the classification accuracy of the basic traditional classification algorithm is not high when judging whether the electrical failure occurs, this paper proposes a Gray Wolf Optimization Support Vector Machine model to improve the recognition rate of electrical diagnosis, referred to as GWO-SVM. We first collected the most common linear (incandescent lamps) and non-linear (microwave ovens) household electrical appliances in normal working and arc fault waveform signals in real life, and then carried out frequency domain feature extraction. Finally, compared with no-optimized SVM and BP neural network, we employed the gray wolf optimization algorithm to optimize support vector machine, and the accuracy of GWO-SVM reaches 90%.

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Intelligent DC Arc-Fault Detection of Solar PV Power Generation System via Optimized VMD-Based Signal Processing and PSO–SVM Classifier

Cai

Wai

2022

IEEE J. Photovoltaics

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Detection of DC Series Arc Fault Based on VMD and ELM

Cited by 4 publications

References 1 publication

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

Intelligent Electrical Fault Detection and Recognition Based on Gray Wolf Optimization and Support Vector Machine

Intelligent DC Arc-Fault Detection of Solar PV Power Generation System via Optimized VMD-Based Signal Processing and PSO–SVM Classifier

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