Application of fault tree analysis for customer reliability assessment of a distribution power system

A new method is developed for predicting customer reliability of a distribution power system using the fault tree approach with customer weighted values of component failure frequencies and downtimes. Conventional customer reliability prediction of the electric grid employs the system average (SA) component failure frequency and downtime that are weighted by only the quantity of the components in the system. These SA parameters are then used to calculate the reliability and availability of components in the system, and eventually to find the effect on customer reliability. Although this approach is intuitive, information is lost regarding customer disturbance experiences when customer information is not utilized in the SA parameter calculations, contributing to inaccuracies when predicting customer reliability indices in our study. Hence our new approach directly incorporates customer disturbance information in component failure frequency and downtime calculations by weighting these parameters with information of customer interruptions. This customer weighted (CW) approach significantly improves the prediction of customer reliability indices when applied to our reliability model with fault tree and two-state Markov chain formulations. Our method has been successfully applied to an actual distribution power system that serves over 2.1 million customers. Our results show an improved benchmarking performance on the system average interruption frequency index (SAIFI) by 26% between the SA-based and CW-based reliability calculations.

► We model the reliability of a power system with fault tree and two-state Markov chain.
► We propose using customer weighted component failure frequencies and downtimes.
► Results show customer weighted values perform superior to component average values.
► This method successfully incorporates customer disturbance information into the model.