Electric arc is a series problem in modern distribution lines where DC microgrids and renewables are connected. If its characteristics and transient responses are modelled and predicted, behaviour of arc can be determined effectively. In this paper, a hybrid DC arc fault detection method is presented. This work is based on real experimental set-up in lab environment. DC power supply of 50V is connected to a load of 5 Omega via a line of 21m. The arc fault is formed using a stepper motor front, middle, and end of the line. Data acquisition card is used to acquire arc voltage and arc current samples with a sampling frequency of 2MHz. The aim of this paper is to estimate the arc location using a hybrid method which consists of differential equation algorithm (DEA), Fourier Technique (FT), and descriptive statistics (DS). DEA is used to extract line resistance and impedance whereas FT and some descriptive statistics are used for feature extraction. As seen from performance curves, it can be said that the proposed hybrid algorithm is able to distinguish DC arc faults according to their location on the line.