Fault arc detection is an important technology to ensure the safe operation of electrical equipment and prevent electrical fires. The high-frequency noise of the arc current is one of the typical arc characteristics of almost all loads. In order to accurately detect arc faults in a low-voltage alternating-current (AC) system, a novel differential high-frequency current transformer (D-HFCT) sensor for collecting high-frequency arc currents was proposed. The sensitivity and frequency band of the designed sensor were verified to ensure that the acquisition requirements of the high-frequency current were satisfied. A series arc fault simulation experiment system was built, and resistive, inductive, and non-linear load and high-power shielding load experiments were carried out. Experiments showed that the sensor output signal was close to zero in the non-arc state, and the sensor output response was a high-frequency glitch in the arc state. The results were consistent for different loads, and the discrimination between normal and fault states was obvious, which proved that the sensor is suitable for series arc fault detection.
In the field of arc fault detection, it is universally acknowledged that it is very hard to judge whether there is an arc fault through signals of the main line when a masking load (such as air compressors, lamps with dimmers, and so on) is in parallel with a resistive load, which always tends to be a fire hazard. Meanwhile, it is annoying that the normal currents of some appliances are very similar to the arcing ones. In this paper, we have found the principles of a novel detection method called high-frequency coupling, putting the neutral line (N) and the live line (L) through the current transformer (CT), which results in asymmetrical distribution of magnetic flux in the core and the only high-frequency components left in the secondary output of the CT. So it is possible for series arc fault detectors to be free from the masking loads and distinguish between the arcing and the nonarcing clearly. Thanks to this convenient method, an arc fault detector based on the microcontroller unit (MCU) has been proposed to detect arc faults effectively by means of simple multi-indicators. The experimental results show the accuracy of arc fault recognition, in all the masking tests, can reach a high level and the detector can detect an arc fault within a short time. INDEX TERMS Series arc fault, high-frequency coupling, asymmetrical distribution, magnetic flux, multi-indicators, detector, MCU, masking tests.
AC series arc faults in the power system can lead to electrical fires. However, the generalization performance of the determined detection method would be affected under unknown loads, as current features vary with loads. To address this issue, this paper presents a series arc fault detection method based on a high-frequency (HF) RLC arc model and one-dimensional convolutional neural network (1DCNN). By the current transformer used for receiving differential HF features (D-HFCT), current with complex features is firstly simplified and divided into different oscillation-signal types. Since the types of real D-HFCT data are limited, the RLC arc model is used to generate D-HFCT data with various types of oscillation features by adjusting load types, initial phase angles and Bernoulli-sequence frequencies. Then, the simulated data are adopted to train the 1DCNN model. Finally, the trained 1DCNN model can detect series arc faults under different types of real loads. Compared with the 1DCNN method driven by the limited types of real-current data, the presented method shows good generalization ability and achieves 99.33% average detection accuracy under nine types of unknown loads, which benefits from the training of simulated D-HFCT data with abundant HF oscillation features.
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