## 1 Introduction
The fault parameter analysis method typically provides high localization accuracy. When considering multiple parameters comprehensively, it can more accurately determine the fault location. However, it relies on data quality and is greatly affected by system complexity [16; 17]. For instance, [PERSON] [18] proposed a novel digital relay protection system for fault localization on hybrid transmission lines, featuring rapid and accurate fault location without the need for prior identification of the faulty section. Intelligent algorithms possess efficient capabilities in data processing, pattern recognition, and solving optimization problems. They are not only capable of handling vast amounts of power system data but also of uncovering potential correlations and patterns within it [19; 20]. For instance, [PERSON] et al. [21] proposed a data-driven method that utilizes fault trajectory samples to determine the fault location and employs a Random Forest Regressor (RFR) model for real-time detection of fault location and duration. The traveling wave method processes data relatively quickly and can complete fault location tasks in a short time. However, the accuracy of fault location is affected by the arrival time and velocity of transient wavefronts, thus influencing the precision of localization [22; 23]. For instance, [PERSON] et al. [24] analyzed fault traveling waves on LCC-MMC HVDC transmission lines and proposed a fault localization principle based on the modal traveling wave arrival time difference and the ratio of fault traveling wave propagation paths. [PERSON] et al. [25] introduced a novel traveling-wave-based fault-location algorithm for multiterminal transmission lines. Overall, the integration of algorithms with traveling wave methods improves the accuracy and real-time performance of fault localization, making fault localization in hybrid systems more efficient and reliable. Determining the arrival time and velocity of fault traveling wave heads in fault location can be achieved using the traveling wave method. [PERSON] et al. [26] use variational mode decomposition (VMD) and the Teager energy operator (TEO) to detect traveling wave faults through fault signals. [PERSON] et al. [PERSON] _et al._ (2018) proposed a traveling-wave-based fault localization method for two-terminal transmission lines, utilizing asynchronous current measurement data recorded by Intelligent Electronic Devices (IEDs) at both ends. [PERSON] et al. [PERSON] _et al._ (2018) proposed a traveling-wave-based solution for time synchronization of two-terminal data on transmission lines, estimating the arrival times of modal traveling waves at both ends through analysis of line energization or fault records with high sampling rates. [PERSON] et al. [PERSON] _et al._ (2018) proposed a novel traveling wave fault location method based on time linear dependence, utilizing the proximal point optimization algorithm (PPOA) to identify the shortest transmission path of the fault traveling wave. However, this method is still susceptible to various abnormal conditions, such as faults in the traveling wave location device, startup failures, and time recording errors, which may lead to inaccurate fault localization. [PERSON] et al. [PERSON] _et al._ (2018) employed a single-ended traveling wave fault location technique based on waveform fitting, which effectively eliminates errors and provides accurate fault locations and impedance information. However, it may be sensitive to changes in fault types and modes, necessitating further testing and validation. Currently, research on tree flash faults primarily focuses on prevention, handling, and diagnosis, with limited studies specifically addressing tree flash fault localization. Understanding the formation process of tree flash faults is crucial for researching their localization.