At present, the detection methods on series arc faults are mainly based on the current of the main circuit, which probably results in misjudgment because of the singularity of the normal working current in a nonlinear load. What's worse, is the arc fault characteristic in the small power branch is easily ignored in the current of the main circuit, which leads to missed judgment. To solve these problems, a detection method based on coupling signal acquisition and higher-order cumulant identification is presented, through the electromagnetic coupling mechanism of high frequency current. By analyzing the coupling signal of the high-frequency current and using the higher-order cumulant algorithm during arcing process, the kurtosis of the coupling signal is calculated. In this paper, the unified threshold value on the kurtosis in various conditions is obtained. The results show that the novel method can be effectively used to detect and identify the series arc faults.INDEX TERMS Coupling, higher-order cumulant, kurtosis, series arc faults.

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.

This paper presents a new method for effective detection of AC series arc fault (SAF) and extraction of SAF characteristics in residential buildings, which addresses the challenges with conventional current detection methods in discriminating arcing and non-arcing current due to their similarity. Different from the traditional method, in the proposed method, the differential magnetic flux is coupled to obtain high-frequency signals by putting the live line and the neutral line through the current transformer, which can effectively solve the problem of SAF features disappearing in the trunk-line current. However, similar to the traditional method, the effectiveness of the proposed coupling method could also be compromised when being used in cases with dimmer load and load starting process. This is found to be caused by the presence of high-amplitude pulse phenomenon in the non-arcing signals in these scenarios, which are incorrectly detected as arcing signals in other loads. To address this issue, a short-observation-window singular value decomposition and reconstruction algorithm (SOW-SVDR) is used to enhance the capability to identify SAFs by the coupling method. The proposed method has been implemented and validated according to UL1699 standard with different types of loads connected to the system and also tested under their starting processes. The experimental results show that the proposed approach is more effective in detecting arc faults compared with existing methods.

During AC series arc faults (SAFs), arcing current features can change significantly or vanish rapidly under different load-combination modes and fault inception points. The phenomena make it very challenging for feature-extracting algorithms to detect SAFs. To address the issues, this paper presents a detection model based on regular coupling features (RCFs). After the model is only trained by the samples in single-load circuits, it can detect SAFs under unknown multi-load circuits. To extract the RCFs, asymmetric magnetic flux is coupled by passing the live line and the neutral line through the current transformer. The coupling signals are not influenced by the multi-load circuits. According to the unique signals, two time-domain features and one frequency-domain feature are extracted to represent the RCFs, including impulsefactor analysis (IFA), covariance-matrix analysis (CMA) and multiple frequency-band analysis (MFA). Then, the impulse factor and its threshold are used to preprocess the signals and decrease analysis complexity for the classifier. Finally, the experimental results show that the proposed method has significantly improved generalization ability and detection accuracy in SAF detection.