In the low-voltage (LV) distribution network, a three-phase unbalance problem often exists. It does not only increase line loss but also threaten the safety of the distribution network. Therefore, the author designs a residential load transfer device for a LV distribution network that can deal with a three-phase unbalance problem by changing the connecting phase of the load. It consists of three parts: user controller for phase swapping, central controller for signal processing and monitoring platform for strategy calculation. This design was based on message queuing telemetry transport (MQTT) communication protocol, and Long Range and 4th Generation mobile telecommunications (LoRa + 4G) communication mode is used to realize the wireless connection between equipment and monitoring platform, and a control scheme is proposed. The improved multi-population genetic algorithm (IMPGA) with multi-objective is used to find the optimal swapping strategy, which is implemented on the monitoring platform. Then the phase swapping is realized by remote control, and the function of reducing three-phase unbalance is realized. The practical experimental result shows that the method can help to reduce the three-phase unbalance rate by changing the connection phase of the load, and the simulation results verify the effectiveness of the algorithm in the phase-swapping strategy.Energies 2019, 12, 2842 2 of 18 often unbalanced. Therefore, in order to protect equipment, ensure the quality of the power supply and reduce losses, an economic and effective solution is needed to solve the three-phase unbalance problem.In China, the LV feeders are usually three-phase four-wire systems, and have single-phase and three-phase power supply modes. In the LV distribution system, the main methods to reduce three-phase unbalance rate are power compensation and load transfer. However, although reactive power compensation can reduce the three-phase unbalance by increasing the power factor, when the three-phase unbalance is very serious, the reactive power compensation will not completely solve the three-phase unbalance problem [7], and reactive power compensation device need high cost [8]. The most direct and effective techniques to deal with the unbalanced distribution of load is to dynamically change the connection phase of single-phase users according to the unbalanced rate, so that the unbalanced rate of three-phase is within the prescribed range. Traditionally, the three-phase unbalance problem has been solved by manually to adjust the users' phase connection, but this method will cause the temporary power lose. And at the same time, due to the uncontrollable customer's power consumption, three-phase unbalance may occur in a short time. Therefore, manual adjustments require high labor costs and make the poor user experience.Power companies strive to innovate and add new elements of automation and intelligence to traditional distribution networks.[9] pointed out that the electric power distribution network is very complex, and not suitable for the...

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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.

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.

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.

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