A Novel Decision Tree Regression-Based Fault Distance Estimation Scheme for Transmission Lines (original) (raw)

In this paper, a decision tree regression (DTR) based fault distance estimation scheme for double circuit transmission lines is presented. Fault location is estimated using the information obtained from fault events data. The decision tree (DT) regression was chosen because it requires less training time, offers greater accuracy with large data set and robustness than all other techniques like artificial neural network, support vector machines, adaptive neuro-fuzzy inference system etc. Hitherto, DT has been used for fault detection/classification, but it has not been used for fault location. Three phase current and voltage signals measured at one end of the line are used as inputs to fault location network. The proposed method does not require communication link as it uses only one end measurements. Signals are processed with two signal processing techniques, discrete Fourier transforms (DFT) and discrete wavelet transform (DWT). A comparative study of both the techniques has been carried out to observe the effect of signal processing on the fault location estimation method. The proposed method is tested on three test systems: namely, the 2-bus, the WSCC-9-bus, and the IEEE 14-bus test systems. The test results confirms that the proposed DTR based algorithm is not affected by the variation in fault type, fault location, fault inception angle, fault resistance, pre-fault load angle, SCC, load variation and line parameters. The proposed scheme is relatively simple and easy in comparison with complex equation based fault location estimation methods.