Accurate fault locator for EHV transmission lines based on radial basis function neural networks

This paper describes the design and implementation of an artificial neural networks-based fault locator for extra high voltage (EHV) transmission lines. This locator utilizes faulted voltage and current waveforms at one end of the line only. The radial basis function (RBF) networks are trained with data under a variety of fault conditions and used for fault type classification and fault location on the transmission line. The results obtained from testing of RBF networks with simulated fault data and recorded data from a 400 kV system clearly show that this technique is highly robust and very accurate. The technique takes into account all the practical limitations associated with a real system. Thereby making it possible to effectively implement an artificial intelligence (AI) based fault locator on a real system.