Bayesian estimation of overhead lines failure rate in electrical distribution systems

• We model MV feeder’s failure rate considering deficiency and non-homogeneity of data.
• We proposed a Bayesian driven shrinkage estimator for failure rate estimation.
• Three Bayesian models are examined with 34 feeder’s real data.
• Hierarchical Bayesian Model is superior to the other two models.

Extracting a precise data-driven failure rate of electrical distribution components is a very prominent issue in asset management decision-making process, but lack of appropriate data would cause problems. In this paper, in order to overcome the deficiency and non-homogeneity of outage data, a shrinkage estimator is proposed for failure rate estimation of overhead lines, which compromises between individual and pooled failure rate estimations. This method is modeled through Hierarchical Bayesian Model (HBM). The functionality of HBM is compared with two other Bayesian models using Deviance Information Criterion (DIC) and real failure data of 34 electrical distribution feeders in Alborz Power Distribution Company.