In the past few decades, the arcing‐high‐impedance fault (arcing‐HIF) detection problems have become an important issue in the effectively grounded distribution network. Many solutions have been proposed to address this problem. The most attractive way is artificial intelligence (AI) method. The paper gives a comprehensive review of arcing‐HIF detection in distribution network‐based AI. First, characteristics and models of arcing‐HIF are analysed, the arcing‐HIF database construction method is also explained; this part is a foundation work for arcing‐HIF detection. Next, arcing‐HIF detection methods based AI are summarised in details including data acquisition, feature extraction and classifier selection. Then, a set of criteria are proposed to evaluate the reliability of arcing‐HIF detection algorithm. Finally, the future trends and challenges to arcing‐HIF detection are also fully accounted. This review can be a valuable guide for researchers who are interested in arcing‐HIF detection‐based AI.

Detecting high-impedance faults (HIF) in distribution networks poses a significant challenge for conventional relay devices due to low fault currents and various characteristics like weaker fault features, distortion offset, and background noise interference. This paper introduces a novel and streamlined method for HIF detection, which ingeniously integrates frequency-band energy curve (FBEC) analysis and Gaussian smoothing to extract trend changes, thus enhancing the precision and effectiveness of HIF detection. The proposed method utilizes continuous wavelet transform (CWT) to extract the time-frequency spectrum from the zero-sequence current. By analyzing the feature-band energy, the FBEC is computed. To mitigate noise interference and enhance the periodic change pattern, a Gaussian filter is applied for smoothing. Distinguishing between HIF and normal operations, including low impedance fault (LIF), capacitor switching (CS), inrush current (IC), and ferromagnetic resonance (FR), is achieved by analyzing the peak points of FBEC. The proposed method’s performance was extensively validated through simulations and field data. The performance of the proposed method was extensively validated through a series of simulations and detailed analysis of real-world field data. The results demonstrated an excellent detection performance on field data, with an impressive accuracy rate of 86.5% and F1-score of 0.87. Moreover, we examined the method’s resilience against noise using data with a Signal-to-Noise Ratio (SNR) of 20dB, resulting in a detection accuracy of 77.3% and F1-score of 0.79. These findings underscore the method’s clear physical meaning, strong interpretability, and versatility, establishing its effectiveness and practicality for real-world applications.

With large passenger and battery capacity, electric buses can play dual roles – commuting tools in transportation networks and mobile energy storage units in power networks. To promote the beneficial synergy between power-transportation coupled networks, it is necessary to properly optimize the operation of electric buses in both networks. This paper proposes a spatial-temporal scheduling framework for the electric bus fleet in a power-transportation coupled network. By scheduling the plug-in locations and charging/discharging profiles of the electric buses, the model not only minimizes the total operational costs of the fleet (i.e., the electricity consumption and battery degradation costs), but also maximizes the revenue by providing flexibility to the power network. The coupled power and transportation constraints are explicitly described in the model. To address the uncertainty from fleet’s energy consumption, the chance-constrained programming is adopted that can be reformulated into second-order cones and efficiently solved by off-the-shelf solvers. Numerical experiments are conducted to validate the superiority of the proposed method based on the IEEE 33-bus distribution network and the Sioux Falls transportation network.