## 1 Introduction  
Efficient and accurate fault identification is critical for emergency repair of high voltage transmission lines, which are an integral part of the power system and prone to failure. Compared with traditional transmission lines, 66 kV transmission lines are characterized by high voltage levels, long distances, complex topological structures, large capacitance to the ground during a fault, and a tendency to generate arc over-voltages, which lead to complex fault features and difficulties in accurate extraction and fault identification. During fault events, zero-sequence current and zero-sequence voltage change first and are more accurate, complete, and fault-tolerant than steady-state current and voltage. In the case of high-resistance ground faults, the transient characteristics of fault high-frequency signals are inversely proportional to the transition resistance [1], and the transient zero-sequence current has a nonlinear relationship in the transition process [2; 3; 4]. Over the years, various fault detection methods based on zero-sequence current and voltage have been developed to detect ground faults, such as one that extracts waveforms based on fault morphological characteristics and another that uses the transient main resonant component [5]. However, these methods have limitations in actual working conditions and can struggle to distinguish the resonant component from the power frequency component. Further, a high-resistance ground fault detection method based on the transient main resonant component has been proposed by [PERSON] et al. [6], however, this method is not effective at extracting the main resonant component and it is difficult to distinguish the main resonant component from the power frequency component. There are several primary causes of arc ground faults, including lightning impulses, poor connections, and aging insulation [7; 8]. However, arc grounding faults cannot be accurately identified. Moreover, a method using the change in waveforms such as voltage-current characteristics and zero-sequence currents as features has been proposed for fault recognition [9]. In addition, a fault identification method based on artificial neural networks has been proposed [10] due to the difficulty in extracting the characteristics of arc earth faults and the differences in fault characteristics at different voltage levels and in different environments. The method requires further consideration to distinguish faults from normal system disturbances. A time-domain analysis method based on Hilbert-Huang transform and a wavelet analysis method based on the time distribution characteristics of fault harmonic characteristics have also been proposed [11; 12]. However, these methods only analyze and judge the waveform without extracting and optimizing fault features. In view of the difficulty of fault feature extraction and fault type identification for 66 kV transmission lines, a fault recognition method based on a fault characteristic matrix and an improved particle swarm optimization (IPSO)-wavelet neural network (WNN) has been proposed in this paper. Firstly, in the aspect of fault feature analysis and extraction, theoretical and simulation analysis is carried out for various types of faults, and fault features are selected as the basis for identification. Secondly, in order to ensure the integrity of fault information, the decomposition scale of dbN wavelet is determined by using the mode maximum matrix, and the fault characteristic matrix is constructed by combining the energy values of each frequency band, so as to achieve accurate fault feature extraction. In view of the limitations of standard WNN, this paper improves a hidden layer of the WNN i to reduce the impact of large fluctuation data on identification accuracy, and an IPSO algorithm is used to design a prediction error function, improving the recognition accuracy and speed of the recognition model, so as to realize the rapid and accurate recognition of fault types.