Automated distribution network fault cause identification with advanced similarity metrics

Distribution network monitoring has the potential to improve service levels by reporting the origin of fault events and informing the nature of remedial action. To achieve this practically, intelligent systems to automatically recognize the cause of network faults could provide a data driven solution, however, these usually require a large amount of examples to learn from, making their implementation burdensome. Furthermore, the choice of input to such a system in order to make accurate classifications is not always clear. In response to this challenge, this paper contributes a means of using minimal amounts of historical fault data to infer fault cause from substation current data through a novel structural similarity metric applied to the associated power quality waveform. This approach is demonstrated along with disturbance context similarity assessment on an industrially relevant benchmark data set where it is shown to provide an improvement in classification accuracy over comparable techniques.