A single line-to-ground (SLG) fault may lead to a more severe line-to-line fault and power supply interruption if the ground-fault current exceeds a certain value. Arc suppression device (ASD) is a good solution to eliminate the ground-fault current. A novel hybrid ASD is proposed in this article, which consists of a passive device and an active one. The passive device utilizes multiterminal breakers and an isolation transformer to couple a secondary voltage of a zig-zag grounding transformer to the neutral point to compensate the majority of the ground-fault current. The active device uses a single-phase voltage source inverter to eliminate the residual fault current due to the leakage inductance of the zig-zag grounding transformer in the passive device. A dual-loop voltage and current control method for the active device is designed for the accurate residual current compensation. Results of simulation and prototype experiment validate the effectiveness of the proposed hybrid ASD. The proposed hybrid ASD does not need to detect distributed line-to-ground parameters, and it has lower cost, less control complexity, higher reliability, and better performance, compared to other ASDs.

Single-line-to-ground (SLG) fault arcs cause the intermittent over-voltages, leading to short circuits within large-scale area. In this paper, a flexible arc suppression device is proposed, which adopts a zigzag grounding transformer, multi-terminal breakers and an isolation transformer to regulate zero-sequence voltage. Principle of zero-sequence voltage regulation (ZVR) and arc suppression method is presented. By carefully choosing the secondary side voltages of grounding transformer as the input of isolation transformer, the zero-sequence voltage is nearly constrained to the opposite of faulty phase supply voltage, and the ground-fault current is limited to a very small value. This device has high adaptability for low-resistance ground fault as it doesn't adopt any electronic apparatus. A 10kV prototype was established and multiple fault conditions were experimented to verify the effectiveness of the proposed ZVR device and its arc suppression principle.

This paper aims to tackle the difficulties in identifying series arc faults with feature aliasing in low-voltage electricity scenarios. Supported by feature sensitivity analysis, a method for identifying low-voltage series arc faults using a time variance-accuracy coefficient optimized random forest (TVARF) model is proposed. Firstly, in the quantitative analysis of real sample feature sensitivity, it is discovered that the main reason for the above issue is that the sensitivity of the sample features decreases in certain scenarios, thereby interfering with the judgment of traditional arc detection methods. Secondly, the random forest model, with its anti-interference capabilities derived from its ensemble operation mechanism, is utilized as the core model of proposed method. Thirdly, this paper constructs a TVA coefficient to achieve multi-objective optimization of random forest hyper-parameters and institutes a program module that use slow-voltage load state samples for training the random forest model.Additionally,aprogramfordenoisingandextractingtimefrequency features is embedded into the input of the trained random forest model. Finally, the TVA-RF method hit 99.978% accuracy in laboratory tests, outperforming traditional methods in accuracy, training efficiency, and calculation speed. In real low-voltage systems, it achieved 98.096% accuracy. Overcoming feature overlap interference, the TVA-RF method accurately identified low-voltage series arc faults.

The struggling and twitching of an electrically shocked body in low-voltage distribution grids can complicate and diversify the electric shock current, posing challenges to traditional electric shock detection methods. To tackle this issue, we propose a fault identification method for animal electric shocks considering unstable contact situations. First, a low-voltage distribution network grounding fault experimental platform is established in a real system, creating a sample library that encompasses various operational scenarios. Second, based on the dynamic characteristics of contact resistance and the principles of arc breakdown generation, an electrical equivalent modeling analysis is conducted on the unstable contact region. The underlying mechanisms that increase the complexity and diversity of electric shock current are revealed. Then, innovative features such as “the sum of periodic point differences,” “energy fluctuation arrays,” “pulse regularity,” and “the count of reversals in the direction of changes” are proposed. By integrating several traditional features and automatically extracted encoded features, a feature vector for animal electric shock faults is established from the perspectives of dynamic amplitude, high-frequency components, energy, and encoded features. In addition, a light gradient boosting machine optimized by multiattraction mechanism zebra optimization has been developed. This model achieves accuracy in pattern recognition tasks through its unique ensemble computation and one-sided sampling mechanism, while also reducing time and space costs. Finally, the proposed method is compared with three existing electric shock identification methods using our experimental platform's testing set. The results indicate that the proposed method demonstrates superiority in terms of accuracy for identifying animal electric shocks.

In order to solve the problem of inaccurate fault location in the transmission network under some abnormal conditions, such as traveling wave location device faults, startup failure and time recording error, a novel traveling wave fault location method based on directed tree model and linear fitting is proposed. A directed tree model of the fault traveling wave transmission along the shortest path is established based on graph theory analysis of traveling wave transmission network. Two straight lines are fitted on the coordinate plane where the accurate fault location is obtained direct by coordinate information of the intersection of these two fitted straight lines (FSLs), according to the transmission characteristics of the fault traveling wave in the directed tree model. The wave velocity is used as the slope of the fitted straight line, and the influence of its uncertainty on the fault location is eliminated. The time record error points of the location device are automatically eliminated in the linear fitting. PSCAD simulation results prove that fault information of the entire transmission network is comprehensively utilized by the proposed method and the ring network is automatically unlooped. The reliability and accuracy of fault location are remarkably improved, particularly for the fault scenarios with recorded information abnormity.