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This paper describes the applications of discrete wavelet transforms (DWT) coupled with conventional artificial neural networks (ANN) to the development of a fault location technique under an improved TCSC transmission system model. The fault location scheme is modular based whereby fault type is verified before identifying the fault location using ANN. This method relies on utilising DWT to decompose the line currents obtained from a single terminal into a series of time-scale representations. A feature model using self-organising maps (SOM) is applied herein to verify the fault location capability of the extracted features. Simulation results indicate that this approach can be used as an effective tool for accurate fault location in TCSC systems.