Fault location in series-compensated transmission lines using adaptive network-based fuzzy inference system

Abstract

In this paper a new and accurate method for fault location in series-compensated transmission lines (SCTLs) is presented using adaptive network-based fuzzy inference system (ANFIS). In the proposed approach, the desired features are obtained using the Least-Squares Estimation (LSE) from two cycles of voltage and current data in only one line terminal. After that, extracted features, including the decaying DC offset component of the current, the fundamental components of current and voltage, and the phase difference between current and voltage for three-phase current and voltage signals, are normalized and applied as inputs to the fuzzy neural network for fault location. The subtractive clustering technique has been utilized to design the basic ANFIS. This study has scrutinized different cases, including two different ANFIS arrangements, implementing a single network to perform fault location on the entire line and a separate network for each line segment. Moreover, the efficiency of feature selection has been analyzed using the RReliefF algorithm. Various training patterns and tests have been provided in a test transmission system for different system conditions, such as different fault inception angles, fault locations, fault resistances, and structural conditions. The results validate that the proposed approach has accurately located the fault under a wide range of system variations.