Update of an early warning fault detection method using artificial intelligence techniques

Abstract

This presentation describes a research investigation to access the feasibility of using an artificial intelligence (AI) method to predict and detect faults at an early stage in power systems. An AI based detector has been developed to monitor and predict faults at an early stage on particular sections of power systems. The detector for this early warning fault detection device only requires external measurements taken from the input and output nodes of the power system. The AI detection system is capable of rapidly predicting a malfunction within the system. Artificial neural networks (ANNs) are being used as the core of the fault detector. In an earlier paper by the authors (1996), a computer simulated medium length transmission line has been tested by the detector and the results clearly demonstrate the capability of the detector. Today's presentation considers a case study illustrating the suitability of this AI technique when applied to a distribution transformer. Furthermore, an evolutionary optimisation strategy to train ANNs is also briefly discussed in this presentation, together with a 'crystal ball' view of future developments in the operation and monitoring of transmission systems in the next millennium.