A hybrid method for fault location estimation in a fixed series compensated lines

This paper proposes a fault distance estimation scheme for fixed series capacitor compensated parallel transmission lines using discrete wavelet transform and decision tree regression. The purpose of the data mining based scheme is to avoid the complicated equation based methods that have been suggested by researchers to overcome the drawbacks of conventional fault location scheme. Although decision tree has inherent advantage over other methods like artificial neural network and support vector machines to work with large data sets, it has not been used in fault location estimation in series compensated (SC) transmission line so far. Decision tree is chosen to locate the faults because of its ability to work with large data set and high accuracy in associating the fault pattern to the fault distance using regression analysis. The discrete wavelet transform processed signals makes the decision process of decision tree regression easy by providing appropriate features. The proposed method is evaluated with variation of fault location, fault type, pre-fault load angle, location of series capacitor, degree of series compensation, fault inception angle, line parameters, inter-circuit faults and fault resistance. The test result of decision tree regression based location estimation scheme ensures that, it can estimate the fault distance accurately.