###### Abstract  
This paper presents a statistical algorithm for classification of fault causes on power transmission lines. The proposed algorithm is based upon the root mean square (RMS) current duration, voltage dip, and discrete wavelet transform (DWT) measured at the sending end of a line and the decision tree method, a commonly accessible measurable method. Fault duration of RMS current signal, voltage dip, and DWT gives concealed data of a fault signature as a contribution to decision tree calculation which is utilized to classify various fault causes. The proposed method was carried out in the MATLAB/SIMULINK programming platform based upon the information made with the fault analysis of the 275 kV sample transmission line considering wide variations in the operating conditions. The classifier performance of different parameters was also compared in a confusion matrix form to obtain the best classification results of the decision tree. fault causes; transmission lines; decision tree; root mean square; discrete wavelet transform +
Footnote †: journal: Applied Sciences  
current duration when it increases by a certain percentage, voltage dip percentage, and energy decomposition value. The classification tool works based on the acquired process parameters such as current signal, voltage signal, and frequency. The acquired signals of fault causes classification process contain the dynamic information about duration and voltage dip. Regardless of which measure boundaries are chosen, signal processing techniques such as time domain, frequency domain, and time-frequency domain analyses a lot of signal features to anticipate various types of fault. Previously, many researchers proposed a signal processing method to detect and classify faults. [PERSON] et al. [2] diagnosed the gear box fault using discrete wavelet transform (DWT) and classified the fault using artificial neural network (ANN). The ANN is capable of classifying the gear box fault on various conditions based on numerical values extracted from the wavelet energy decomposition. In [7], [PERSON] et al. proposed a continuous density hidden Markov model (CDHMM) to determine the dynamics of the state transition due to fault occurrences and classify the condition using neural networks. In addition, [PERSON] et al. proposed rotating machinery fault diagnosis using convolutional neural network (CNN) [8]. Fault detection using fuzzy method has also been proposed for photovoltaic (PV) protection [9]. Voltage ratio (VR) and power ratio (PR) are applied as input data for ANN to categorize fault regions in examining PV. Then, the second technique is implemented to detect the exact fault in the PV system. Centroid type for defuzzification process is chosen and 10 different membership functions are considered for fuzzy logic process. In [10], the author proposed fuzzy cause-and-effect network (FCE) in DS for fault diagnosis. The measurement of feeder currents and bus voltage derived from SCADA is converted into fuzzy terms before it is specified into the membership function of fuzzy sets. Furthermore, a decision tree is widely used for fault detection and classification [11]. [PERSON] et al. proposed decision tree classify rules to build a repository of faults in a gear box based on statistical value extracted from wavelet transform [12]. Furthermore, [PERSON] et al. proposed the same method to classify the types of faults in 400 kV power transmission line networks and comparing the accuracy results with obtaining back-propagation neural network [13]. [PERSON] et al. implemented a decision tree to detect and diagnose the fault in grid connected photovoltaic systems under several weather conditions [14]. In much of the previous research, it is observed that the fault detection in overhead transmission lines is mainly focusing on the types of fault whether it is line-to-line fault or grounding fault.