Impulse fault diagnosis in power transformers using self-organising map and learning vector quantisation

An artificial intelligence approach is proposed to an impulse fault diagnosis problem in oil-filled power transformers. The experiment focuses on the distinction between the effects caused by faults of a different nature and the different physical location of occurrences in a transformer winding. The proposed method involves an artificial neural network-based pattern recognition technique, to recognise the frequency responses of the winding admittance of a typical high-voltage transformer under healthy and different faulty conditions of winding insulation. It attempts to establish a correlation between the nature and site of the internal insulation fault and its associated frequency response. A self-organising neural network model has been employed as the basic pattern recogniser, to discover the significant patterns and to extract the hidden information from a set of frequency response patterns obtained from an EMTP model of the transformer with artificially simulated faults. A learning vector quantisation-based classification technique has been applied to efficiently classify visually indistinguishable response patterns. The method applied to a winding model of a high-voltage transformer, with tap changer winding, exhibited high diagnostic accuracy by successful detection and discrimination of faults of a different nature and site of occurrence