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Summary Estimating a precise data‐driven failure rate of electrical components in the distribution networks is a prominent task in asset management of the network. To estimate the failure rates in the overhead distribution lines, there are two main challenges: data deficiency and population variability. In order to overcome difficulties, this paper proposes an applicable method based on hierarchical Bayesian Poisson regression (HBPR). The proposed method is applied to the real distribution system with 34 feeders. The deviance information criterion and model checking procedure are used to compare the goodness of fit between HBPR and exchangeable hierarchical Bayesian model (EHBM). Finally, to show the functionality of the HBPR model, the failure rates obtained from HPBR and EHBM are used to calculate reliability indices, and the results are compared with the actual value of indices. Copyright © 2014 John Wiley & Sons, Ltd.

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A reward and penalty scheme (RPS) is used for setting up the service quality which exposes the distribution companies to financial benefits caused by demand for the reliability of customers. In this paper, an algorithm for the optimal switch number and placement in distribution networks in the presence of RPS is presented. The primary objective is reliability improvement and minimization of the cost of sectionalizing switches (SS) and tie switches (TS) for a given regulatory period considering acceptable financial risk. In this algorithm, the uncertainty in the reliability appears as financial risk.A genetic algorithm is adopted to solve the optimization problem. The number and location of SS and TS are found while financial incentives of RPS, capital investment and annual operation and maintenance costs are considered. The performance of the proposed approach is assessed and illustrated on a real distribution network. The results show the efficiency of the proposed algorithm. Nomenclature SS t CC Capital investment and installation cost of SS installed in year . TS t CC Capital investment and installation cost of TS installed in year . l Energy price ($/kWh) for load sector . ,, s z k D Average duration of unplanned outage interruption z-th zone of s-th feeder result of failure occurrence in k-th zone of s-th feeder. DRAnnual discount rate. f ( ) i SAIDI Probability distribution of SAIDI for plan . () t f CENS Probability distribution of energy not supplied cost in year . ,, f( ) t s z N Frequency distribution of failures of z-th zone of s-th feeder in year . SS t MC Operation and maintenance cost of SS in year . TS t MC Operation and maintenance cost of TS in year . m Life period of SS and TS. T NC Total number of customers. , sz NC Number of customers located in z-th zone on s-th feeder. ,, NS s z P Expected interrupted load (kW) in z-th zone of s-th feeder. RPI i Probability distribution of reward penalty incentive for plan . RPS( ) SAIDI Reward penalty scheme based on SAIDI index. i SC Switch placement cost for plan T Period of regulation.  VaRValue at risk at a given confidence level (1 − )%.