## 1 Introduction  
Many big cities have undergone economic and infrastructure growth, with a rising population density due to migration from rural to urban areas. This trend has resulted in increasing demand for electricity that requires more transmission lines to connect different parts of a substation to the end-user. In order to improve the landscape in large cities and reduce the stack of overhead lines, underground cables have been employed in distributing power in high density or tourist areas. Thus, the topology of the distribution network is shifting from mainly overhead lines to a combination of overhead lines and underground cables. Currently, Thailand is also installing hybrid transmission lines (a combination of overhead lines and underground cables) in various cities throughout the country. There are significantly different parameters between overhead lines and underground cables, which can cause an error in the conventional protection system and risk reliability issues in the electrical network. Thus, an algorithm that can detect and classify fault types under the hybrid transmission line needs to be developed. A significant proportion of faults in electrical power systems occur in transmission lines. Fault signal analysis in transmission systems is one of the important fields to ensure that the operator has a method to deal with disturbance correctly and limit the damage on the overall system. There has been a lot of research in the past that performed analysis using various methodologies [1; 2]. Wavelet transform is a signal processing method that has gained attention from researchers for the application of fault analysis and has been applied for fault analysis in electrical systems due to properties that make it suitable for analyzing the transient sate of the signal [3; 4; 5; 6; 7; 8; 9; 10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. A wavelet transform was applied to detect and classify faults in transmission lines [3; 4; 5; 6; 7; 8; 9]. The maximum wavelet singular value (MWSV) was applied for the detection and classification of faults. Current signals were input for the wavelet transform. This type of fault classification depends on the Euclidean norm of the MWSV, and faults are identified by coefficients and setup values [3]. Herein, detection and classification of faults in transmission lines were performed by introducing a novel method based on power spectral density (PSD) in time and frequency. The PSD index (in time) was applied for fault detection, and the PSD index (in frequency) was used for classification [4]. Thus, faults could be detected in a short time, and classification was completed using the Hellinger distance [4]. The mother wavelet is important because if the appropriate mother wavelet is chosen, effective results can be obtained. Previous studies have highlighted the importance of the mother wavelet [8; 16]. In [8], the mother wavelets affected the classification of faults in hybrid transmission lines. Daubechies (db) mother wavelet exhibits better accuracy than any other mother wavelet. Likewise, in [16], Daubechies (db) mother wavelet was found to completely satisfy fault classification. By combining the wavelet transform method with artificial intelligence (AI) methods, the accuracy of fault classification in electrical systems has been improved [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. The discrete wavelet transform (DWT) has been applied previously to decompose fault signals [11; 13; 14; 17]. The DWT results were input into artificial neural networks (ANNs) to detect and classify faults in transmission lines. A technique for classification of internal and external faults of protection zones in transmission lines has been proposed, in which fault signals were decomposed by the wavelet transform, and high frequency components and spectral energy were input into a support vector machine (SVM) to detect faults and classify them, respectively. Various AI methods are applied for a multitude of applications.