Xiaobing Xiao

The incipient cable fault in active low-resistance-grounded distribution networks is a typical asymmetrical fault and is difficult to locate because the fault current is weak, short-lasting, and easily affected by distributed generation (DG). To address this typical asymmetrical problem, this paper proposes a fault section location method based on multidimensional information correlation analysis. First, an equivalent incipient fault model is established by combining the Kizilcay arc model with an insulation-defect resistance, so that the intermittent arc behavior and the conductive path of degraded insulation can be represented simultaneously. Then, the generalized S-transform is used to extract three features from the transient zero-sequence current, namely the transient current energy index, group phase-angle polarity, and waveform similarity. On this basis, a multidimensional feature vector and a comprehensive similarity coefficient are constructed to identify the fault section, and an auxiliary downstream energy comparison rule is introduced to distinguish the actual fault section from DG-connected pseudo-fault sections. The method is verified in MATLAB/Simulink R2025a under different fault locations, DG access conditions, penetration levels, noise levels, and key parameter variations. The simulation results under the tested conditions indicate that the proposed method can effectively identify asymmetrical incipient cable fault sections in active low-resistance-grounded distribution networks.

To address the low sampling frequency, poor clock accuracy, high power costs, and limited endurance of current lightning monitoring systems, this paper proposes a self-powered lightning current monitoring terminal. The terminal employs an FPGA+STM32 architecture, combining the high-speed data processing capabilities of FPGA with STM32’s multitasking capacity, enabling it to achieve transient lightning signal waveform recognition, high-speed sampling, data storage, and wireless communication. This provides essential data support for lightning protection in power lines. Furthermore, a hybrid power scheme that integrates online energy harvesting with storage batteries ensures the stable and reliable operation of the monitoring terminal in remote areas.

Among various failure causes, lightning overvoltage represents the most significant threat to overhead distribution lines, which serve as critical components in power systems. This study uses the hybrid partial element equivalent circuit (PEEC) multi-conductor transmission line (MTL) method to perform overvoltage simulations and investigate lightning risk distribution along distribution lines developed from a real 10 kV distribution networks in Guizhou, China. The results of the rocket-triggered lightning observation verify the accuracy of the hybrid method for direct lightning simulation. Combining the Monte Carlo method with the electro-geometric model (EGM), the impact of differential protection configurations on annual lightning flashover rates is analyzed. The results demonstrate that lightning strikes on phase wires generate high-magnitude overvoltages but with limited spatial influence, resulting in fewer pole flashovers. Conversely, strikes on poles produce lower overvoltage peaks but affect wider areas, leading to significantly more flashovers. Using annual flashover rates as the risk evaluation metric, the line topologies into high-risk, medium-risk, and other low-risk areas are classified. Targeting an annual flashover rate below 0.4 as the design objective, the configuration schemes of the arresters are progressively optimized. This risk-based approach provides an effective reference framework for differential protection design of distribution line safeguards.

Demand response (DR) has high regulation potential, which can reduce the power supply–demand imbalance caused by extreme disasters. However, its actual effectiveness still needs to be improved because of low user willingness and incomplete compensation mechanisms. To address this issue, a symmetrical incentive mechanism for DR is proposed. Building upon this mechanism, a bi-level load restoration optimization model under extreme events is proposed. The upper-level model minimizes grid-side costs during load restoration, determining load restoration ratios and incentive coefficients transmitted to the lower level. The lower-level model maximizes user profits while considering comfort-level losses from DR participation, solving for actual response quantities that are fed back to the upper level. To efficiently solve the proposed load restoration model, an iterative mixed-integer load restoration solver is proposed. Case studies demonstrate that the proposed symmetrical mechanism achieved an 89.6% participation rate, showing a 2.46% improvement over fixed incentive schemes. Grid payment costs were reduced by CNY 365,400, achieving incentive compatibility that facilitates rapid load restoration post extreme disasters.