Abstract
The power supply quality and power supply safety of a low-voltage residential power distribution system is seriously affected by the occurrence of series arc faults. It is difficult to detect and extinguish them due to the characteristics of small current, high stochasticity, and strong concealment. In order to improve the overall safety of residential distribution systems, a novel method based on discrete wavelet transform (DWT) and deep neural network (DNN) is proposed to detect series arc faults in this paper. An experimental bed is built to obtain current signals under two states, normal and arcing. The collected signals are discomposed in different scales applying the DWT. The wavelet coefficient sequences are used for forming training set and test set. The deep neural network trained by training set under 4 different loads adaptively learn the feature of arc faults. The accuracy of arc faults recognition is sent through feeding test set into the model, about 97.75%. The experimental result shows that this method has good accuracy and generality under different types of loading.
Highlights
Arc faults, luminous discharge phenomena caused by the breakdown of insulating medium between electrodes, are dangerous in low-voltage systems [1]
If timely detection and accurate prediction are not be made, arc faults may spread to adjacent circuits, endanger the power distribution system and cause explosions and fires
Arc faults can be divided into series arc fault, parallel arc fault, and grounding arc fault [3]
Summary
Luminous discharge phenomena caused by the breakdown of insulating medium between electrodes, are dangerous in low-voltage systems [1]. They could occur in conditions of short circuit, overcurrent, poor contact and electric leakage of distribution line and power consumption equipment [2]. Affected by the influence of line impedance, the loop current is usually 5 A to 30 A or lower. Devices such as conventional circuit breakers, fuses, or residual current detectors may not trip or trip by mistake [4]
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