Abstract
Arc faults are one of the important causes of electric fires. In order to solve the problem of randomness, diversity, the concealment of series arc faults and to improve the detection accuracy, a novel arc fault detection method integrated random forest (RF), improved multi-scale permutation entropy (IMPE) and wavelet packet transform (WPT) are designed. Firstly, singular value decomposition (SVD) was applied to filter the current signal and then the high-dimensional fault features were constructed by extracting IMPE, the wavelet packet energy and the wavelet packet energy-entropy. Afterward, the high-dimensional fault features were employed to train the RF to realize the arc fault detection of different load types and the experimental results verify the effectiveness of the arc fault detection method designed in this paper. Finally, the comparative experiments demonstrates that the RF shows better performance in arc fault detection compared to the back-propagation neural network (BPNN) and least squares support vector machines (LSSVM), and that the experiments of transient events indicate that RF is able to effectively avoid incorrectly detecting different load types during the start operations and stop operations.
Highlights
The level of residential electrification has been greatly raised along with the social economic level growth as well as scientific and technological progress
Compared to least squares support vector machines (LSSVM) and back-propagation neural network (BPNN), random forest (RF) is more effective in avoiding false alarms; 3) During serial arc fault conditions, the number mistakenly detected by BPNN was 60 and the detection accuracy was 80%
The serial arc fault detection method that this paper proposed has an excellent performance in avoiding the incorrect detection of different load types during start and stop operations
Summary
The level of residential electrification has been greatly raised along with the social economic level growth as well as scientific and technological progress. Based on the fault features extracted from the current signals, some researchers have applied machine learning methods such as support vector machine (SVM) [28], LSSVM [29], BPNN [30] and the Kalman filter (KF) [31] to detect arc faults. It is applicable to measure the state change of the current signal state by the energy-entropy of the wavelet packet when series arc faults occur. In order to improve the detection efficiency of the series arc fault detection method for different working states of different load types, this paper proposes a novel arc fault detection method by taking advantage of IMPE, WPT, singular value decomposition (SVD) and RF.
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