## 1 Introduction  
As the scale of power transmission and distribution systems, as well as power facilities, continues to expand, the number of hybrid overhead-underground lines in the power supply network is also increasing. High-voltage transmission lines are susceptible to external factors such as wind, rain, lightning, and internal faults, leading to power system failures. The occurrence of grounding faults in power systems due to direct or indirect contact between trees and transmission lines is referred to as \"tree-flash faults\" [1, 2, 3]. For instance, in 2016, a major power outage occurred in South Australia, Australia, affecting approximately 750,000 users. One of the causes of the outage was intense tornadoes and lightning strikes, which led to trees collapsing and ultimately resulted in short circuits in the transmission lines, causing the power outage [4]. When a tree flash fault occurs in hybrid overhead-underground lines, it is crucial to promptly determine the precise location of the fault. This is of significant importance for swiftly rectifying the fault, ensuring the smooth operation of the power system, and enhancing the reliability of the supply system. Research on tree flash faults requires the full utilization of sensing technology, data collection, and analytical methods. By integrating advanced sensors and intelligent algorithms, real-time monitoring and analysis of tree flash faults can be achieved, driving the development of the power system toward intelligence. With the rapid development of new energy sources and the continuous evolution of electricity demand, power systems require more flexible and reliable solutions [5]. Researchers have conducted relevant studies on tree flash faults and have obtained corresponding results. [PERSON] [6] not only delves into the in-depth exploration of tree structure and growth characteristics but also includes a comprehensive assessment of potential risk factors. [PERSON] et al. [7] proposed a tree tilt monitoring solution based on an AI-IoT integrated framework, aiming to prevent tree collapse accidents in advance and enhance urban forestry safety. [PERSON] et al. [8] proposed a method to identify vegetation-related failure collapse sequences and their timescales by simulating the dynamic behavior of line sag and vegetation growth. This provides important insights for predicting and preventing tree-related failures. [PERSON] et al. [9] compared the effectiveness of different models in predicting the collapse of Platanus orientalis trees in urban forests and proposed a prediction model based on a Support Vector Machine (SVM), providing urban forest managers with an effective tool for ecological failure risk assessment. [PERSON] et al. [10] analyzed the mechanism of tree fault occurrence and simulated the discharge process between trees and transmission lines in a laboratory environment, revealing the changing characteristics of discharges when trees approach transmission lines. [PERSON] et al. [11] analyzed distribution network outage data to identify the primary factors leading to vegetation-related outages and extracted relevant features. [PERSON] et al. [12] proposed a data-driven approach to predict vegetation-related outages in power distribution systems, utilizing machine learning methods for prediction, and developed a real-time outage prediction platform. [PERSON] et al. [13] conducted a study based on experimental data to investigate high-impedance faults (HIFs) caused by trees and proposed a new mathematical model to illustrate the influence of environmental conditions and biological classification. [PERSON] et al. [14] proposed a new single-ended localization method based on non-contact magnetic field strength measurement for tree-related high-impedance faults. [PERSON] et al. [15] proposed a fault diagnosis scheme for tree flash fault syndrome associated with cross-country faults (CCFs). Common methods for hybrid line fault localization include fault parameter analysis, intelligent algorithms, and traveling wave methods.