Data-driven reliability modeling, based on data mining in distribution network fault statistics

Power distribution fault statistics provide splendid resource for extracting experimental knowledge. The extracted knowledge includes the inherit characteristics of the network assets. Analysis and estimation of failures require a comprehensive understanding of faults in terms of the relevant effective parameters. This paper outlines a data-driven model to represent momentary failure rate in terms of the most influential factors based on the study of the recorded historical fault data as well as the expert's experiments in the Greater Tehran Electricity Distribution Company. A methodology is presented for momentary fault causes identification and model construction using artificial neural networks. Satisfactory results indicate that the developed model can easily be implemented to estimate other fault types in power distribution systems.